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THE CHANDRA SOURCE CATALOG

IAN N. EVANS,¹ FRANCIS A. PRIMINI,¹ KENNY J. GLOTFELTY,¹ CRAIG S. ANDERSON,¹ NINA R. BONAVENTURA,¹
JUDY C. CHEN,¹ JOHN E. DAVIS,² STEPHEN M. DOE,¹ JANET D. EVANS,¹ GIUSEPPINA FABBIANO,¹ ELIZABETH C. GALLE,¹
DANNY G. GIBBS II,¹ JOHN D. GRIER,¹ ROGER M. HAIN,¹ DIANE M. HALL,³ PETER N. HARBO,¹ XIANGQUN (HELEN) HE,¹
JOHN C. HOUCK,² MARGARITA KAROVSKA,¹ VINAY L. KASHYAP,¹ JENNIFER LAUER,¹ MICHAEL L. MCCOLLOUGH,¹
JONATHAN C. MCDOWELL,¹ JOSEPH B. MILLER,¹ ARIK W. MITSCHANG,¹ DOUGLAS L. MORGAN,¹ AMY E. MOSSMAN,¹
JOY S. NICHOLS,¹ MICHAEL A. NOWAK,² DAVID A. PLUMMER,¹ BRIAN L. REFSDAL,¹ ARNOLD H. ROTTS,¹
ANETA SIEMIGINOWSKA,¹ BETH A. SUNDHEIM,¹ MICHAEL S. TIBBETTS,¹ DAVID W. VAN STONE,¹ SHERRY L. WINKELMAN,¹
AND PANAGOU LA ZOGRAFOU¹

Accepted May 22, 2010

ABSTRACT

The Chandra Source Catalog (CSC) is a general purpose virtual X-ray astrophysics facility that provides access to a carefully selected set of generally useful quantities for individual X-ray sources, and is designed to satisfy the needs of a broad-based group of scientists, including those who may be less familiar with astronomical data analysis in the X-ray regime. The first release of the CSC includes information about 94,676 distinct X-ray sources detected in a subset of public ACIS imaging observations from roughly the first eight years of the Chandra mission. This release of the catalog includes point and compact sources with observed spatial extents $\lesssim 30''$ . The catalog (1) provides access to the best estimates of the X-ray source properties for detected sources, with good scientific fidelity, and directly supports scientific analysis using the individual source data; (2) facilitates analysis of a wide range of statistical properties for classes of X-ray sources; and (3) provides efficient access to calibrated observational data and ancillary data products for individual X-ray sources, so that users can perform detailed further analysis using existing tools. The catalog includes real X-ray sources detected with flux estimates that are at least 3 times their estimated $1\sigma$ uncertainties in at least one energy band, while maintaining the number of spurious sources at a level of $\lesssim 1$ false source per field for a 100 ks observation. For each detected source, the CSC provides commonly tabulated quantities, including source position, extent, multi-band fluxes, hardness ratios, and variability statistics, derived from the observations in which the source is detected. In addition to these traditional catalog elements, for each X-ray source the CSC includes an extensive set of file-based data products that can be manipulated interactively, including source images, event lists, light curves, and spectra from each observation in which a source is detected.

Subject headings: catalogs — X-rays: general

1. INTRODUCTION

Ever since Uhuru (Giacconi et al. 1971), X-ray astronomy missions have had a tradition of publishing catalogs of detected X-ray sources, and these catalogs have provided the fundamental datasets used by numerous studies aimed at characterizing the properties of the X-ray sky. While source catalogs are the primary data products from X-ray sky surveys (e.g., Giacconi et al. 1972; Forman et al. 1978; Elvis et al. 1992; Voges 1993; Voges et al. 1999), the Einstein IPC catalog (Harris et al. 1990) demonstrated the utility of catalogs of serendipitous sources identified in the fields of pointed-observation X-ray missions. More recent serendipitous source catalogs (e.g., Gioia et al. 1990; White et al. 1994; Ueda et al. 2005; Watson et al. 2008) have further expanded the list of sources with X-ray data available for further analysis by the astronomical community.

Source catalogs typically include a uniform reduction of the mission data. This provides a significant advantage for the general scientific community because it removes the need for end-users, who may be unfamiliar with the complexities of the particular mission and its instruments, to perform detailed reductions for each observation and detected source.

When compared to all previous and current X-ray missions, the Chandra X-ray Observatory (e.g., Weisskopf et al. 2000, 2002) breaks the resolution barrier with a sub-arcsecond on-axis point spread function (PSF). Launched in 1999, Chandra continues to provide a unique high spatial resolution view of the X-ray sky in the energy range from 0.1 to 10 keV, over a $\sim 60$ – $250$ square arcminute field of view. The combination of excellent spatial resolution, a reasonable field of view, and low instrumental background translate into a high detectable-source density, with low confusion and good astrometry. Chandra includes two instruments that record images of the X-ray sky. The Advanced CCD Imaging Spectrometer (ACIS; Bautz et al. 1998; Garmire et al. 2003) instrument incorporates ten $1024 \times 1024$ pixel CCD detectors (any six of which can be active at one time) with an effective pixel size of $\sim 0.5''$ on the sky, an energy resolution of order 110 eV at the Al-K edge (1.49 keV),

ievans@cfa.harvard.edu

¹ Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138

² MIT Kavli Institute for Astrophysics and Space Research, 77 Massachusetts Avenue, Cambridge, MA 02139

³ Northrop Grumman, 60 Garden Street, Cambridge, MA 02138FIG. 1.— Distribution of CSC release 1.0 master sources on the sky, in Galactic coordinates.

and a typical time resolution of $\sim 3.2$ s. The High Resolution Camera (HRC; Murray et al. 2000) instrument consists of a pair of large format micro-channel plate detectors with a pixel size $\sim 0.13''$ on the sky and a time resolution of $\sim 15.6$ $\mu$ s, but with minimal energy resolution. The wealth of information that can be extracted from identified serendipitous sources included in Chandra observations is a powerful and valuable resource for astronomy.

The aim of the Chandra Source Catalog (CSC) is to disseminate this wealth of information by characterizing the X-ray sky as seen by Chandra. While numerous other catalogs of X-ray sources detected by Chandra may be found in the literature (e.g., Zezas et al. 2006; Brassington et al. 2008; Romano et al. 2008; Luo et al. 2008; Muno et al. 2009; Elvis et al. 2009), the region of the sky or set of observations that comprise these catalogs is restricted, and they are typically aimed at maximizing specific scientific goals. In contrast, the CSC is intended to be an all-inclusive, uniformly processed dataset that can be utilized to address a wide range of scientific questions. The CSC is intended ultimately to comprise a definitive catalog of X-ray sources detected by Chandra, and is being made available to the astronomical community in a series of increments with increasing capability over the next several years.

The first release of the CSC was published in 2009 March. This release includes information about 135,914 source detections, corresponding to 94,676 distinct X-ray sources on the sky, extracted from a subset of public imaging observations obtained using the ACIS instrument during the first eight years of the Chandra mission. The distribution of release 1 sources on the sky is presented in Figure 1.

We expect that the CSC will be a highly valuable tool for many diverse scientific investigations. However, the catalog is constructed from pointed observations obtained using the Chandra X-ray Observatory, and is neither all-sky nor uniform in depth. The first release of the catalog includes only point and compact sources, with observed extents $\lesssim 30''$ . Because of the difficulties inherent in detecting highly extended sources and point and compact sources that lie close to them, and quantifying in a consistent and robust way the properties of such sources, we have chosen to exclude entire fields (or in some cases, individual ACIS CCDs) containing such sources from the first release of the CSC, as described in § 3.1. Therefore, the catalog does not include sources near some of the most famous Chandra targets, and there may be selection effects that restrict the source content

of the catalog and which therefore may limit scientific studies that require unbiased source samples.

The minimum flux significance threshold for a source to be included in the first release of the CSC is set conservatively, and corresponds typically to $\sim 10$ detected source photons (on-axis) in the broad energy band integrated over the total exposure time. This conservative threshold was chosen to maintain the spurious source rate at an acceptable level over the wide variety of Chandra observations that are included in this release of the catalog. We expect to relax this criterion in future releases based on experience gained constructing the current release.

A number of other Chandra catalogs do include sources with fewer net counts than the CSC. Such fainter thresholds are attainable typically either because of specific attributes of the observations included in those catalogs, or because of the assumptions made when constructing the catalog.

As an example of the former category, the XBootes survey catalog (Kenter et al. 2005) includes sources that are roughly a factor of two fainter than the CSC flux significance threshold. That survey is constructed from short (5 ks) observations obtained in an area with low line-of-sight absorption. This results in a negligible background level that substantially simplifies source detection and enables identification of sources with very few counts. Some Chandra catalogs derived from observations with the range of exposures comparable to those that comprise the CSC (e.g., Elvis et al. 2009; Laird et al. 2009; Muno et al. 2009) also include fainter sources. However, in these cases the additional source fractions are in general not large, typically adding $\lesssim 10%$ more sources below the CSC threshold, as described in detail in § 3.7.1.

For other Chandra catalogs, visual review and validation at the source level is a planned part of the processing thread (e.g., Kim et al. 2007; Muno et al. 2009). In some cases (e.g., Broos et al. 2007), visual review may be used to adjust processing parameters for individual sources. Such manual steps are time-consuming, but enable lower significance levels to be achieved while maintaining an acceptable spurious source rate. In contrast, the CSC catalog construction process requires that the processing pipelines run on a wide range of observations with a minimum of manual intervention. The scope of the CSC is simply too large to require manual handling at the source level. We do not manually inspect individual source detections, nor do we adjust source detection or processing parameters based on manual evaluation. Instead, the CSC uses a largely automated quality assurance approach, as described in § 3.14.

The sky coverage of the first catalog release (Fig. 2) totals $\sim 320$ square degrees, with coverage of $\sim 310$ square degrees brighter than a $0.5$ – $7.0$ keV flux limit of $1.0 \times 10^{-13}$ $\text{erg cm}^{-2} \text{s}^{-1}$ , decreasing to $\sim 135$ square degrees brighter than $1.0 \times 10^{-14}$ $\text{erg cm}^{-2} \text{s}^{-1}$ , and $\sim 6$ square degrees brighter than $1.0 \times 10^{-15}$ $\text{erg cm}^{-2} \text{s}^{-1}$ . These numbers will continue to grow as the Chandra mission continues, with a 15 year prediction of the eventual sky coverage of the CSC of order 500 square degrees, or a little over 1% of the sky.

In this paper we describe in detail the content and construction of release 1 of the CSC. However, where appropriate we also discuss in addition the steps required to process HRC instrument data used to construct re-FIG. 2.— Sky coverage of release 1.0 of the CSC, in the ACIS broad energy band. The ordinate value is the total sky area included in the CSC that is sensitive to point sources with fluxes at least as large as the corresponding value on the abscissa.

lease 1.1 of the catalog, since the differences in the algorithms are small. Release 1.1 of the catalog is scheduled for spring 2010. This paper is organized into 5 sections, including the introduction. In § 2, we present a description of the catalog. This includes the catalog design goals, an outline of the general characteristics of Chandra data that are relevant to the catalog design, the organization of the data within the catalog, approaches to data access, and an outline of the data content of the catalog. Section 3, which comprises the bulk of the paper, describes in detail the methods used to extract the various source properties that are included in the catalog, with particular detail provided when the algorithms are new or have been adapted for use with Chandra data. A brief description of the principal statistical properties of the catalog sources is presented in § 4; this topic is treated comprehensively by F. A. Primini et al. (2010, in preparation). Conclusions are presented in § 5. Finally, Appendix A contains details of the algorithm used to match source detection from multiple overlapping observations, as well as the mathematical derivation of the multivariate optimal weighting formalism used for combining source position and positional uncertainty estimates from multiple observations.

2. CATALOG DESCRIPTION

2.1. Design Goals

The CSC is intended to be a general purpose virtual science facility, and provides simple access to a carefully selected set of generally useful quantities for individual sources or sets of sources matching user-specified search criteria. The catalog is designed to satisfy the needs of a broad-based group of scientists, including those who may be less familiar with astronomical data analysis in the X-ray regime, while at the same time providing more advanced data products suitable for use by astronomers familiar with Chandra data.

The primary design goals for the CSC are to (1) allow simple and quick access to the best estimates of the X-ray source properties for detected sources, with good scientific fidelity, and directly support scientific analysis using the individual source data; (2) facilitate analysis of a wide range of statistical properties for classes of X-ray sources; (3) provide efficient access to calibrated

observational data and ancillary data products for individual X-ray sources, so that users can perform detailed further analysis using existing tools such as those included in the Chandra Interactive Analysis of Observations (CIAO; Fruscione et al. 2006) portable data analysis package; and (4) include all real X-ray sources detected down to a predefined threshold level in all of the public Chandra datasets used to populate the catalog, while maintaining the number of spurious sources at an acceptable level.

To achieve these goals, for each detected X-ray source the catalog records the source position and a detailed set of source properties, including commonly used quantities such as multi-band aperture fluxes, cross-band hardness ratios, spectra, temporal variability information, and source extent estimates. In addition to these traditional elements, the catalog includes file-based data products that can be manipulated interactively by the user. The primary data products are photon event lists (e.g., Conroy 1992), which record measures of the location, time of arrival, and energy of each detected photon event in a tabular format. Additional data products derived from the photon event list include images, light curves, and spectra for each source individually from each observation in which a source is detected. The catalog release process is carefully controlled, and a detailed characterization of the statistical properties of the catalog to a well defined, high level of reliability accompanies each release. Key properties evaluated as part of the statistical characterization include limiting sensitivity, completeness, false source rates, astrometric and photometric accuracy, and variability information.

2.2. Data Characteristics

Both ACIS and HRC cameras operate in a photon counting mode, and register individual X-ray photon events. For each photon event, the two dimensional position of the event on the detector is recorded, together with the time of arrival and a measure of the energy of the event. In most operating modes, lists of detected events are recorded over the duration of an observation, typically between 1 ks and 160 ks, and are then telemetered to the ground for subsequent processing.

To minimize the effect of bad detector pixels, and to avoid possible burn-in degradation of the camera by bright X-ray sources, the pointing direction of the telescope is normally constantly dithered in a Lissajous pattern, with a typical scale length of about 20" on the sky and a period of order 1 ks, while taking data. The motion of the telescope is recorded via an "aspect camera" (Aldcroft et al. 2000) that tracks the motion of a set of (usually 5) guide stars as a function of time during the observation. The coordinate transformation needed to remove the motion from the event (photon) positions is computed from the aspect camera data and applied during data processing.

Breaking down the 4-dimensional X-ray data hypercube into spatial, spectral, and temporal axes provides a natural focus on the properties that may be of interest to the general user, but also identifies some of the complexities inherent in Chandra data that must be addressed by catalog construction and data analysis algorithms.

Spatially, the Chandra PSF varies significantly with off-axis and azimuthal angle (with the former variationFIG. 3.— Sample local Chandra model PSFs projected onto the ACIS detector pixel plane extracted from the CSC. The upper, middle, and lower sets of 4 images correspond to PSF models computed at the monochromatic effective energies of the ACIS hard, medium, and soft energy bands, respectively. From left to right, the images correspond to PSFs determined at off-axis angles $\theta = 0'$ , $5'$ , $10'$ , and $15'$ , respectively. The orientation and details of the PSF substructure varies with azimuthal angle, $\phi$ . The image intensity scaling is proportional to the square root of the pixel flux.

dominating), as well as with incident photon energy (Fig. 3). Close to the optical axis of the telescope, the PSF is approximately symmetric with a 50% enclosed energy fraction radius of order $0.3''$ over a wide range of energies, but at $15'$ off-axis the PSF is strongly energy-dependent, asymmetric, and significantly extended, with a 50% enclosed energy fraction radius of order $13''$ at 1.5 keV.

For the widely used ACIS detector, the instrumental spectral energy resolution is of order 100–200 eV, and depends on incident photon energy and location on the detector. Because the energy resolution is significantly lower than the typical energy width of the features and absorption edges that define the effective area of the telescope optics (and therefore the quantum efficiency of the telescope plus detector system), a full matrix formulation that considers the redistribution of source X-ray flux into the set of instrumental pulse height analyzer bins must be used when performing spectral analyses. This is in contrast to the more familiar scenario from many other wavebands, where the instrumental resolution is often much higher than the spectral variation of quantum efficiency, enabling the commonly used implicit assumption that the flux redistribution matrix is diagonal (and is therefore not considered explicitly).

We note in passing that Chandra is equipped with a pair of transmission gratings that can be inserted into the optical path, and is therefore capable of performing high spectral resolution (slitless spectroscopy) observations. However, such observations are not included in the current release of the CSC.

Time domain analyses must consider the impact of spacecraft dither within an observation. Strong false variability signatures at the dither frequency can arise because of variations of the quantum efficiency over the detector, or because the source or background region dithers off the detector edge or across a gap between adjacent ACIS CCDs. Corrections for these effects, as well as for cosmic X-ray background flares that can be highly variable over periods of a few kiloseconds, must be applied when computing light-curves. The extremely

FIG. 4.— Three separate observations that include the area surrounding the bright X-ray source CXO J162624.0-242448 are shown. In each panel source detections from observations 00619, 00635, and 00637 are identified in cyan, green, and red, respectively. Left: Observation 00619 (4.1 ks exposure). In this short exposure, only the bright source visible at an off-axis angle of $\sim 7.7'$ . The PSF is somewhat extended. Center: Observation 00635 (100.7 ks exposure). In this deep exposure, the bright X-ray source is located $\sim 15.6'$ off-axis in this deep exposure. The extended PSF is clearly visible. Right: Observation 00637 (96.4 ks exposure). The bright source is located $\sim 3.0'$ off-axis, and the combination of the compact PSF and long exposure resolves the region into 3 distinct source detections.

low photon event rates common for many faint X-ray sources typically require time domain statistics to be evaluated using event arrival-time formulations instead of rate-based approaches.

An additional level of complexity occurs because many astronomical sources of interest that will be included in the catalog are extremely faint. Rigorous application of Poisson counting statistics is required when deriving source properties and associated errors, separating X-ray analyses from many other wavebands where Gaussian statistics are typically assumed.

2.3. Data Organization

The tabulated properties included in the CSC are organized conceptually into two separate tables, the Source Observations Table and the Master Sources Table. Distinguishing between source detections (as identified within a single observation) and X-ray sources physically present on the sky is necessary because many sources are detected in multiple observations and at different off-axis angles (and therefore have different PSF extents).

Each record included in the Source Observations Table tabulates properties derived from a source detection in a single observation. These entries also include pointers to the associated file-based data products that are included in the catalog, which are all observation-specific in the first catalog release. Each record in the Source Observations Table is further split internally into a set of source-specific data and a set of observation-specific, but source-independent, data. The latter are recorded once to avoid duplication. A description of the data columns recorded in the Source Observations Table for each source detection is provided in Table 1.

Because of the dependence of the PSF extent with off-axis angle, multiple distinct sources detected on-axis in one observation may be detected as a single source if located far off-axis in a different observation (Fig. 4). During catalog processing, source detections from all observations that overlap the same region of the sky are spatially matched to identify distinct X-ray sources. Estimates of the tabulated properties for each distinct X-ray source are derived by combining the data extracted from all source detections and observations that can be uniquely associated, according to the algorithms described in § 3. The best estimates of the source propertiesTable 1. Source Observations Table Properties

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
Observation Identification
obsid	No	No		Observation identifier (ObsId)
obi	No	No		Observation interval number (ObI)
Observation Target and Pointing
targname	No	No		Observation target name specified by observer
ra_targ	No	No		Target position specified by observer, ICRS right ascension
dec_targ	No	No		Target position specified by observer, ICRS declination
ra_pnt	No	No		Mean spacecraft pointing, ICRS right ascension
dec_pnt	No	No		Mean spacecraft pointing, ICRS declination
roll_pnt	No	No	deg	Mean spacecraft pointing, roll angle
ra_nom	No	No		Tangent plane reference position, ICRS right ascension
dec_nom	No	No		Tangent plane reference position, ICRS declination
roll_nom	No	No	deg	Tangent plane reference position, roll angle
Observation Timing
gti_start	No	No	s	Start time of valid data, MET (seconds since 1998 Jan 01 00:00:00 TT)
gti_stop	No	No	s	Stop time of valid data, MET
gti_elapsed	No	No	s	Total elapsed time of the observation ( $gti\_stop - gti\_start$ )
gti_obs	No	No		Start time of valid data, ISO 8601 format (yyyy-mm-ddThh:mm:ss)
gti_end	No	No		Start time of valid data, ISO 8601 format
gti_mjd_obs	No	No		Start time of valid data, MJD
mjd_ref	No	No		MJD corresponding to 0 s MET
Instrument Configuration
instrument	No	No		Instrument used for the observation, ACIS or HRC
grating	No	No		Transmission grating used for the observation, NONE, HETG, or LETG
datamode	No	No		Instrument data mode used for the observation
readmode	No	No		ACIS readout mode used for the observation
exptime	No	No	s	ACIS CCD frame time
cycle	No	No		ACIS readout cycle for the observation, P (primary) or S (secondary) for alternating exposure (interleaved) mode observations, or P for other ACIS modes
timing_mode	No	No		HRC precision timing mode
Processing Information
ascdver	No	No		Software version used to create the Level 3 full-field event data file
caldbver	No	No		Calibration database version used to calibrate the Level 3 full-field event data file
crdate	No	No		Creation date/time of the Level 3 full-field event data file, UTC

Table 1—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
Observing Cycle
ao	No	No		Chandra observing cycle in which the observation was scheduled
Observation-Specific Source Identification
region_id	No	No		Unique identifier for each detected source region within a single observation
source_id	No	No		Unique identifier for each distinct source component within a single source region
Source Position^c
ra	Yes	No		Source position, ICRS right ascension
dec	Yes	No		Source position, ICRS declination
gal_l	Yes	No	deg	Source position, Galactic longitude
gal_b	Yes	No	deg	Source position, Galactic latitude
err_ellipse_r0	Yes	No	arcsec	Major radius of the 95% confidence level error ellipse
err_ellipse_r1	Yes	No	arcsec	Minor radius of the 95% confidence level error ellipse
err_ellipse_ang	Yes	No	deg	Position angle of the major axis of the 95% confidence level error ellipse
theta	No	No	arcmin	Source aperture position, off-axis angle ( $\theta$ )
phi	No	No	deg	Source aperture position, azimuthal angle ( $\phi$ )
chipx	No	No	pixels	Detector Cartesian $x$ position corresponding to $(\theta, \phi)$
chipy	No	No	pixels	Detector Cartesian $y$ position corresponding to $(\theta, \phi)$
Source Significance
flux_significance	Yes	No		Significance of the source determined from the ratio of the source flux to the estimated error in the local background
detect_significance	Yes	No		Significance of the source detection determined by the wavdetect algorithm
Source Codes and Flags^d
conf_code	No	No		Source regions overlap (source is confused; bit-coded value)
dither_warning_flag	No	No		Highest statistically significant peak in the power spectrum of the source region count rate occurs at the dither frequency of the observation or at a beat frequency of the dither frequency
edge_code	No	No		Source position or region dithered off a detector chip edge during the observation (bit-coded value)
extent_code	No	No		Deconvolved source extent is inconsistent with a point source at the 90% confidence level (bit-coded value)
multi_chip_code	No	No		Source position or region dithered across multiple detector chips during the observation (bit-coded value)
pileup_warning	No	No		ACIS broad energy band count rate per pixel per CCD frame time (see Davis 2007a)
sat_src_flag	No	No		Source is saturated; source properties are unreliable

Table 1—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
streak_src_flag	No	No		Source is detected on an ACIS readout streak; source properties may be affected
var_code	No	No		Source displays flux variability during the observation (bit-coded value)
man_inc_flag	No	No		Source was manually included in the catalog via human review
man_reg_flag	No	No		Source region parameters (location, dimensions) were manually adjusted via human review
Source Extent^e
mjr_axis_raw	Yes	No	arcsec	$1\sigma$ radius along the major axis of the ellipse defining the observed source extent
mnr_axis_raw	Yes	No	arcsec	$1\sigma$ radius along the minor axis of the ellipse defining the observed source extent
pos_angle_raw	Yes	No	deg	Position angle of the major axis of the ellipse defining the observed source extent
mjr_axis_raw_err	Yes	No	arcsec	Estimated error on the observed source extent major axis
mnr_axis_raw_err	Yes	No	arcsec	Estimated error on the observed source extent minor axis
pos_angle_raw_err	Yes	No	deg	Estimated error on the observed source extent position angle
psf_mjr_axis_raw	Yes	No	arcsec	$1\sigma$ radius along the major axis of the ellipse defining the local model PSF extent
psf_mnr_axis_raw	Yes	No	arcsec	$1\sigma$ radius along the minor axis of the ellipse defining the local model PSF extent
psf_pos_angle_raw	Yes	No	deg	Position angle of the major axis of the ellipse defining the local model PSF extent
psf_mjr_axis_raw_err	Yes	No	arcsec	Estimated error on the local model PSF extent major axis
psf_mnr_axis_raw_err	Yes	No	arcsec	Estimated error on the local model PSF extent minor axis
psf_pos_angle_raw_err	Yes	No	deg	Estimated error on the local model PSF extent position angle
major_axis	Yes	No	arcsec	$1\sigma$ radius along the major axis of the ellipse defining the deconvolved source extent
minor_axis	Yes	No	arcsec	$1\sigma$ radius along the minor axis of the ellipse defining the deconvolved source extent
pos_angle	Yes	No	deg	Position angle of the major axis of the ellipse defining the deconvolved source extent
major_axis_err	Yes	No	arcsec	Estimated error on the deconvolved source extent major axis
minor_axis_err	Yes	No	arcsec	Estimated error on the deconvolved source extent minor axis
pos_angle_err	Yes	No	deg	Estimated error on the deconvolved source extent position angle
Aperture Photometry
ra_aper	No	No		Center of the source and background apertures, ICRS right ascension
dec_aper	No	No		Center of the source and background apertures, ICRS declination
mjr_axis_aper	No	No	arcsec	Semi-major axis of the elliptical source region aperture
mnr_axis_aper	No	No	arcsec	Semi-minor axis of the elliptical source region aperture
pos_angle_aper	No	No	deg	Position angle of the semi-major axis of the elliptical source region aperture
area_aper	No	No	arcsec²	Area of the modified elliptical source region aperture (includes corrections for exclusion regions due to overlapping sources)
mjr_axis1_aperbkg	No	No	arcsec	Semi-major axis of the inner ellipse of the annular background region aperture
mnr_axis1_aperbkg	No	No	arcsec	Semi-minor axis of the inner ellipse of the annular background region aperture
mjr_axis2_aperbkg	No	No	arcsec	Semi-major axis of the outer ellipse of the annular background region aperture
mnr_axis2_aperbkg	No	No	arcsec	Semi-minor axis of the outer ellipse of the annular background region aperture
pos_angle_aperbkg	No	No	deg	Position angle of the semi-major axes of the annular background region aperture

Table 1—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
area_aperbkg	No	No	arcsec²	Area of the modified annular background region aperture (includes corrections for exclusion regions due to overlapping sources)
mjr_axis_aper90	Yes	No	arcsec	Semi-major axis of the elliptical Point Spread Function 90% Enclosed Counts Fraction aperture
mnr_axis_aper90	Yes	No	arcsec	Semi-minor axis of the elliptical PSF 90% ECF aperture
pos_angle_aper90	Yes	No	deg	Position angle of the semi-major axis of the elliptical PSF 90% ECF aperture
area_aper90	Yes	No	arcsec²	Area of the modified elliptical PSF 90% ECF aperture (includes corrections for exclusion regions due to overlapping sources)
mjr_axis1_aper90bkg	Yes	No	arcsec	Semi-major axis of the inner ellipse of the annular PSF 90% ECF background aperture
mnr_axis1_aper90bkg	Yes	No	arcsec	Semi-minor axis of the inner ellipse of the annular PSF 90% ECF background aperture
mjr_axis2_aper90bkg	Yes	No	arcsec	Semi-major axis of the outer ellipse of the annular PSF 90% ECF background aperture
mnr_axis2_aper90bkg	Yes	No	arcsec	Semi-minor axis of the outer ellipse of the annular PSF 90% ECF background aperture
pos_angle_aper90bkg	Yes	No	deg	Position angle of the semi-major axes of the annular PSF 90% ECF background aperture
area_aper90bkg	Yes	No	arcsec²	Area of the modified annular PSF 90% ECF background region aperture (includes corrections for exclusion regions due to overlapping sources)
psf_frac_aper	Yes	No		Fraction of the PSF included in the modified source region aperture
psf_frac_aperbkg	Yes	No		Fraction of the PSF included in the modified background region aperture
psf_frac_aper90	Yes	No		Fraction of the PSF included in the modified PSF 90% ECF aperture
psf_frac_aper90bkg	Yes	No		Fraction of the PSF included in the modified PSF 90% ECF background aperture
cnts_aper	Yes	No	counts	Total counts observed in the modified source region aperture
cnts_aperbkg	Yes	No	counts	Total counts observed in the modified background region aperture
src_cnts_aper	Yes	No	counts	Aperture-corrected net counts inferred from the source region aperture
src_rate_aper	Yes	Yes	counts s^-1	Aperture-corrected net count rate inferred from the source region aperture
photflux_aper	Yes	Yes	photons cm^-2 s^-1	Aperture-corrected net photon flux inferred from the source region aperture, calculated by counting X-ray events
flux_aper	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the source region aperture, calculated by counting X-ray events
flux_powlaw_aper	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the source region aperture, calculated from an absorbed $E^{-1.7}$ power-law spectral model
flux_bb_aper	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the source region aperture, calculated from an absorbed $kT = 1.0$ keV black-body spectral model
cnts_aper90	Yes	No	counts	Total counts observed in the modified PSF 90% ECF aperture
cnts_aper90bkg	Yes	No	counts	Total counts observed in the modified PSF 90% ECF background region aperture
src_cnts_aper90	Yes	No	counts	Aperture-corrected net counts inferred from the PSF 90% ECF aperture
src_rate_aper90	Yes	Yes	counts s^-1	Aperture-corrected net count rate inferred from the PSF 90% ECF aperture
photflux_aper90	Yes	Yes	photons cm^-2 s^-1	Aperture-corrected net photon flux inferred from the PSF 90% ECF aperture, calculated by counting X-ray events

Table 1—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
flux_aper90	Yes	Yes	erg ccm^-2 s^-1	Aperture-corrected net energy flux inferred from the PSF 90% ECF aperture, calculated by counting X-ray events
flux_powlaw_aper90	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the PSF 90% ECF aperture, calculated from an absorbed $E^{-1.7}$ power-law spectral model
flux_bb_aper90	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the PSF 90% ECF aperture, calculated from an absorbed $kT = 1.0$ keV black-body spectral model
Hardness Ratios
hard_ $\langle x \rangle \langle y \rangle$	No	Yes		Spectral hardness ratio measured between ACIS energy bands $\langle x \rangle$ and $\langle y \rangle$ ; $\text{hard}_\bullet \langle x \rangle \langle y \rangle = (\text{flux\_aper\_} \langle x \rangle - \text{flux\_aper\_} \langle y \rangle) / \text{flux\_aper\_b}$
Spectral Model Fits^f
flux_powlaw	No	Yes	erg cm^-2 s^-1	Net integrated 0.5–10 keV energy flux of the best power-law model spectral fit to the source region aperture PI spectrum
alpha	No	Yes		Photon index ( $\alpha$ , defined as $F_E \propto E^{-\alpha}$ ) of the best power-law model spectral fit to the source region aperture PI spectrum
nh_powlaw	No	Yes	$10^{20}$ cm^-2	Total neutral Hydrogen column density, $N_H$ , of the best power-law model spectral fit to the source region aperture PI spectrum
powlaw_stat	No	No		$\chi^2$ (data variance) statistic per degree of freedom of the best power-law model spectral fit to the source region aperture PI spectrum
flux_bb	No	Yes	erg cm^-2 s^-1	Net integrated 0.5–10 keV energy flux of the best black-body model spectral fit to the source region aperture PI spectrum
kt	No	Yes	keV	Temperature ( $kT$ ) of the best black-body model spectral fit to the source region aperture PI spectrum
nh_bb	No	Yes	$10^{20}$ cm^-2	Total neutral Hydrogen column density, $N_H$ , of the best black-body model spectral fit to the source region aperture PI spectrum
bb_stat	No	No		$\chi^2$ (data variance) statistic per degree of freedom of the best black-body model spectral fit to the source region aperture PI spectrum
Temporal Variability
var_index	Yes	No		Gregory-Loredo variability index in the range [0, 10]
var_prob	Yes	No		Gregory-Loredo variability probability
ks_prob	Yes	No		Kolmogorov-Smirnov variability probability
kp_prob	Yes	No		Kuiper’s test variability probability
var_mean	Yes	No	counts s^-1	Flux variability mean value, calculated from an optimally-binned light curve
var_sigma	Yes	No	counts s^-1	Flux variability standard deviation, calculated from an optimally-binned light curve
var_min	Yes	No	counts s^-1	Flux variability minimum value, calculated from an optimally-binned light curve
var_max	Yes	No	counts s^-1	Flux variability maximum value, calculated from an optimally-binned light curve

Table 1—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
Source-Specific Observation Summary
livetime	No	No	s	Effective exposure time after applying the good time intervals and the deadtime correction factor
detector	No	No		Detector elements over which the background region bounding box dithers during the observation

^aIndicates that tabulated properties include separate entries for each energy band. The individual band entries are identified by the suffix “_ $\langle x \rangle$ ”, where $\langle x \rangle$ is one of the energy band designations listed in Table 4.

^bIndicates that tabulated properties include separate entries for $\sim 68%$ lower and upper confidence limits. The data value is tabulated using the indicated property name, while the lower and upper confidence limits are identified by the suffixes “_lolim” and “_hilim,” respectively. If a property includes both confidence limits and separate entries for each band, then the confidence limit suffix precedes the band designation suffix.

^cIn the first release of the catalog, the source position error ellipse is approximated by a circle.

^dTranslations for source codes that contain bit-coded values are presented in Table 8.

^eIn the first release of the catalog, the deconvolved source extent ellipse is approximated by a circle. The deconvolved source extent is computed if at least 6 counts are included in the source region aperture; the estimated error is computed if at least 15 counts are included in the deconvolved source extent ellipse.

^fSpectral model fits are only performed if the source has at least 150 net counts in the ACIS broad energy band.Table 2. Master Sources Table Properties

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
Source Name
name	No	No		Source name in the format “CXO Jhhmmss.s $\pm$ ddmss”
Source Position^c
ra	No	No		Source position, ICRS right ascension
dec	No	No		Source position, ICRS declination
gal_l	No	No	deg	Source position, Galactic longitude
gal_b	No	No	deg	Source position, Galactic latitude
err_ellipse_r0	No	No	arcsec	Major radius of the 95% confidence level error ellipse
err_ellipse_r1	No	No	arcsec	Minor radius of the 95% confidence level error ellipse
err_ellipse_ang	No	No	deg	Position angle of the major axis of the 95% confidence level error ellipse
Source Flux Significance (SNR)
significance	No	No		Highest source flux significance across all observations
Source Flags
conf_flag	No	No		Source regions overlap (source is confused)
extent_flag	No	No		Deconvolved source extent is inconsistent with a point source at the 90% confidence level
pileup_flag	No	No		ACIS pile-up fraction exceeds $\sim 10\%$ in all observations; source properties may be affected
sat_src_flag	No	No		Source is saturated in all observations; source properties are unreliable
streak_src_flag	No	No		Source is detected on an ACIS readout streak in all observations; source properties may be affected
var_flag	No	No		Source displays flux variability within an observation or between observations
var_inter_hard_flag	No	No		Source hardness ratios are statistically inconsistent across multiple observations
man_inc_flag	No	No		Source was manually included in the catalog via human review
man_match_flag	No	No		Cross-observation source matching was performed manually via human review
man_reg_flag	No	No		Source region parameters (location, dimensions) were manually adjusted via human review
Source Extent^d
major_axis	Yes	No	arcsec	$1\sigma$ radius along the major axis of the ellipse defining the deconvolved source extent
minor_axis	Yes	No	arcsec	$1\sigma$ radius along the minor axis of the ellipse defining the deconvolved source extent
pos_angle	Yes	No	deg	Position angle of the major axis of the ellipse defining the deconvolved source extent
major_axis_err	Yes	No	arcsec	Estimated error on the deconvolved source extent major axis
minor_axis_err	Yes	No	arcsec	Estimated error on the deconvolved source extent minor axis
pos_angle_err	Yes	No	deg	Estimated error on the deconvolved source extent position angle
Aperture Photometry
photflux_aper	Yes	Yes	photons cm^-2 s^-1	Aperture-corrected net photon flux inferred from the source region aperture, calculated by counting X-ray events

Table 2—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
flux_aper	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the source region aperture, calculated by counting X-ray events
flux_powlaw_aper	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the source region aperture, calculated from an absorbed $E^{-1.7}$ power-law spectral model
flux_bb_aper	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the source region aperture, calculated from an absorbed $kt = 1.0$ keV black-body spectral model
photflux_aper90	Yes	Yes	photons cm^-2 s^-1	Aperture-corrected net photon flux inferred from the Point Spread Function 90% Enclosed Counts Fraction aperture, calculated by counting X-ray events
flux_aper90	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the PSF 90% ECF aperture, calculated by counting X-ray events
flux_powlaw_aper90	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the PSF 90% ECF aperture, calculated from an absorbed $E^{-1.7}$ power-law spectral model
flux_bb_aper90	Yes	Yes	erg cm^-2 s^-1	Aperture-corrected net energy flux inferred from the PSF 90% ECF aperture, calculated from an absorbed $kt = 1.0$ keV black-body spectral model
Spectral Hardness Ratios
hard_ $\langle x \rangle \langle y \rangle$	No	Yes		Spectral hardness ratio measured between ACIS energy bands $\langle x \rangle$ and $\langle y \rangle$ ; $\text{hard}_{\langle x \rangle \langle y \rangle} = (\text{flux\_aper}_{\langle x \rangle} - \text{flux\_aper}_{\langle y \rangle})/\text{flux\_aper\_b}$
Model Spectral Fits^e
flux_powlaw	No	Yes	erg cm^-2 s^-1	Net integrated 0.5–10 keV energy flux of the best power-law model spectral fit to the source region aperture PI spectrum
alpha	No	Yes		Photon index ( $\alpha$ , defined as $F_E \propto E^{-\alpha}$ ) of the best power-law model spectral fit to the source region aperture PI spectrum
nh_powlaw	No	Yes	10²⁰ cm^-2	Total neutral Hydrogen column density, $N_H$ , of the best power-law model spectral fit to the source region aperture PI spectrum
flux_bb	No	Yes	erg cm^-2 s^-1	Net integrated 0.5–10 keV energy flux of the best black-body model spectral fit to the source region aperture PI spectrum
kt	No	Yes	keV	Temperature ( $kT$ ) of the best black-body model spectral fit to the source region aperture PI spectrum
nh_bb	No	Yes	10²⁰ cm^-2	Total neutral Hydrogen column density, $N_H$ , of the best black-body model spectral fit to the source region aperture PI spectrum
nh_gal	No	No	10²⁰ cm^-2	Galactic neutral Hydrogen column density, $N_H(\text{Gal})$ in the direction of the source determined from Dickey & Lockman (1990)
Temporal Variability
var_intra_index	Yes	No		Intra-observation Gregory-Loreda variability index in the range [0, 10] (highest value across all observations)

Table 2—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
var_intra_prob	Yes	No		Intra-observation Gregory-Loreda variability probability (highest value across all observations)
ks_intra_prob	Yes	No		Intra-observation Kolmogorov-Smirnov variability probability (highest value across all observations)
kp_intra_prob	Yes	No		Intra-observation Kuiper’s test variability probability (highest value across all observations)
var_intra_sigma	Yes	No	counts s^-1	Intra-observation flux variability standard deviation, calculated from an optimally-binned light curve (highest value across all observations)
var_inter_index	Yes	No		Inter-observation variability index in the range [0, 10]; indicates whether the source region photon flux is constant between observations
var_inter_prob	Yes	No		Inter-observation variability probability, calculated from the $\chi^2$ distribution of the photon fluxes of the individual observations
var_inter_sigma	Yes	No	photons cm^-2 s^-1	Inter-observation flux variability standard deviation; the spread of the individual observation photon fluxes about the error weighted mean
Observation Summary^f
acis_num	No	No		Total number of ACIS imaging observations contributing to the Master Sources Table record of the source
acis_hetg_num	No	No		Total number of ACIS/HETG observations contributing to the Master Sources Table record of the source
acis_letg_num	No	No		Total number of ACIS/LETG observations contributing to the Master Sources Table record of the source
acis_time	No	No	s	Total ACIS imaging exposure time (seconds of good time) for all ACIS imaging observations contributing to the Master Sources Table record of the source
acis_hetg_time	No	No	s	Total ACIS/HETG observation exposure time (seconds of good time) for all ACIS/HETG observations contributing to the Master Sources Table record of the source
acis_letg_time	No	No	s	Total ACIS/LETG observation exposure time (seconds of good time) for all ACIS/LETG observations contributing to the Master Sources Table record of the source
hrc_num	No	No		Total number of HRC imaging observations contributing to the Master Sources Table record of the source
hrc_letg_num	No	No		Total number of HRC/LETG observations contributing to the Master Sources Table record of the source
hrc_hetg_num	No	No		Total number of HRC/HETG observations contributing to the Master Sources Table record of the source
hrc_time	No	No	s	Total HRC imaging exposure time (seconds of good time) for all HRC imaging observations contributing to the Master Sources Table record of the source
hrc_letg_time	No	No	s	Total HRC/LETG observation exposure time (seconds of good time) for all HRC/LETG observations contributing to the Master Sources Table record of the source

Table 2—Continued

Property	Multi-^a Band	Conf.^b Lim.	Units	Description
hrc_hetg_time	No	No	s	Total HRC/HETG observation exposure time (seconds of good time) for all HRC/HETG observations contributing to the Master Sources Table record of the source

^cIn the first release of the catalog, the source position error ellipse is approximated by a circle.

^dIn the first release of the catalog, the source extent ellipse is approximated by a circle.

^eSpectral model fits are only performed if the source has at least 150 net counts in the ACIS broad energy band. These properties are copied from the ACIS observation with the highest flux_significance in any energy band.

^fThe first release of the catalog does not include observations obtained using the High Resolution Camera or observations obtained using the High or Low Energy Transmission Gratings.Table 3. File-Based Data Products

Data^a Product	File Name^b Specifier	Description
Full-Field Data Products
Event List	evt3	Photon event list, with associated Good Time Intervals (GTIs), recorded in consecutive FITS Hierarchical Data Units (HDUs)
Image	$\langle x \rangle\_img3$	Per-energy-band background-subtracted, exposure corrected images^c (photons $\text{cm}^{-2} \text{s}^{-1}$ )
Image (JPEG)	$\langle b \rangle\_img3$	Background-subtracted, exposure corrected images; 3-color JPEG encoding for ACIS observations (soft/medium/hard) energy bands color coded as (red/green/blue); monochromatic JPEG encoding for HRC observations
Background Image	$\langle x \rangle\_bkgimg3$	Per-energy-band background images^c (counts); includes high spatial frequency “readout streak” component for ACIS observations
Exposure Map	$\langle x \rangle\_exp3$	Per-energy-band exposure map images^c ( $\text{cm}^2 \text{s photon}^{-1}$ ) computed at the band monochromatic effective energy
Sensitivity Map	$\langle x \rangle\_sens3$	Per-energy-band limiting sensitivity images^c (photons $\text{cm}^{-2} \text{s}^{-1}$ ); minimum photon flux per energy band required for a point source to satisfy the flux significance threshold necessary for inclusion in the catalog, as a function of position in the field of view
Aspect Histogram	ahst3	Table of $X$ , $Y$ offsets (pixels) and roll-angle offsets (deg) vs. time due to spacecraft dither motion
Bad Pixel Map	bpix3	Detector bad pixel region-definitions, including observation-specific bad pixels
Field of View	fov3	Observation-specific sky field of view region-definitions
Source Region Data Products^d
Source Region	reg3	Modified source region aperture and background region aperture region-definitions
Event List	regevt3	Photon event list, with associated GTIs recorded in consecutive FITS HDUs
Image	$\langle x \rangle\_regimg3$	Per-energy-band background-subtracted, exposure corrected images^e (photons $\text{cm}^{-2} \text{s}^{-1}$ )
Image (JPEG)	$\langle x \rangle\_regimg3$	Per-energy-band background-subtracted, exposure corrected images^e ; monochromatic JPEG encoding (ACIS only) Exposure corrected image^e ; 3-color JPEG encoding for ACIS observations (soft/medium/hard) energy bands color coded as (red/green/blue)
Image 3-color (JPEG)	reg3img3
Exposure Map	$\langle x \rangle\_regexp3$	Per-energy-band exposure map images^e ( $\text{cm}^2 \text{s photon}^{-1}$ ) computed at the band monochromatic effective energy
Point Spread Function	$\langle x \rangle\_psf3$	Per-energy-band local model point spread function images computed at the band monochromatic effective energy
Point Spread Function (JPEG)	$\langle x \rangle\_psf3$	Per-energy-band local model point spread function images computed at the band monochromatic effective energy
ARF	arf3	Ancillary response file; table of telescope plus detector effective area ( $\text{cm}^2$ ) vs. energy bin
RMF	rmf3	(ACIS-only) Detector redistribution matrix file
PI Spectrum	pha3	(ACIS-only) Per-energy-band pulse-invariant source region aperture and background region aperture spectra, with associated GTIs, in consecutive FITS HDUs
Light Curve	$\langle x \rangle\_lc3$	Per-energy-band optimally-binned light curve, computed using the Gregory-Loredo formalism

Table 3—Continued

Data^a Product	File Name^b Specifier	Description

^aAll data products are recorded in FITS format, except where noted. Files are named $\langle instr \rangle f \langle obsid \rangle _ \langle obi \rangle N \langle ver \rangle _ [r \langle region_id \rangle] \langle specifier \rangle . \langle ext \rangle$ , where $\langle instr \rangle$ is either ACIS or HRC, $\langle obsid \rangle$ is the five digit observation identifier, $\langle obi \rangle$ is the three digit observation interval number, $\langle ver \rangle$ is the file processing version number, $\langle region_id \rangle$ is the source region identifier, $\langle specifier \rangle$ is the file name specifier listed in the table, and $\langle ext \rangle$ is fits for FITS format files and jpg for JPEG format files; the region identifier element (enclosed in square brackets) is only present for source region data products.

^b $\langle x \rangle$ designates the energy band, one of b, s, m, or h for ACIS, and w for HRC; $\langle b \rangle$ is the image blocking factor.

^cMultiple blocked images are recorded in consecutive FITS HDUs; several blocking factors are used to bin multiple sky pixels into single image pixels, as described in § 3.4.

^dSource region data product images include the rectangular region, oriented along the cardinal directions, that bounds the background region aperture.

^eSource region data product images are blocked at the same blocking factor as the smallest corresponding full-field image that includes the background region aperture bounding box.FIG. 5.— Linkages between the Master Sources Table and the Source Observations Table entries for the source detections from Fig. 4 are depicted. The 3 source detections in observation 00637 are uniquely identified with distinct X-ray sources on the sky, and will be associated with the corresponding master sources through “unique” linkages. Similarly, the single source detection (region 5) in observation 00619 is an unambiguous match to region 6 in observation 00637, and so will also be associated with the same master source via a unique linkage. The confused detection, region 99 in observation 00635 overlaps the 3 source detections in observation 00637, and so is associated with the corresponding master sources with “ambiguous” linkages.

for each distinct X-ray source are recorded in the Master Sources Table. A description of the data columns recorded in the Master Sources Table for each source is provided in Table 2.

Each distinct X-ray source is thus conceptually represented in the catalog by a single entry in the Master Sources Table, and one or more associated entries in the Source Observations Table (one for each observation in which the source was detected).

All of the tabulated properties included in both the Master Sources Table and the Source Observations Table can be queried by the user. Bi-directional links between the entries in the two tables are managed transparently by the database, so that the user can access all observation data for a single source seamlessly.

If a source detection included in the Source Observations Table can be related unambiguously to a single X-ray source in the Master Sources Table, then the corresponding table entries will be associated by “unique” linkages. Source detections included in the Source Observations Table that cannot be related uniquely to a single X-ray source in the Master Source Table will have their entries associated by “ambiguous” linkages (Fig. 5).

The data from ambiguous source detections are not used when computing the best estimates of the source properties included in the Master Sources Table. In the case of ACIS observations, source detections for which the estimated photon pile-up fraction (Davis 2007a) exceeds $\sim 10%$ will not be used if source detections in other ACIS observations do not exceed this threshold.

Using the linkages between the entries in the two tables, the user will nevertheless be able to identify all of the X-ray sources in the catalog that could be associated with a specific detection in a single observation, and vice-versa. These linkages may be important, for example, when identifying candidate targets for follow-up studies based on a data signature that is only visible

in the observation data for a confused source.

2.4. Data Access

The primary user tool for querying the CSC is the CSCview web-browser interface (Zografou et al. 2008), which can be accessed from the public catalog web-site⁴. The user can directly query any of the tabulated properties included in either the Master Sources Table or the Source Observations Table, display the contents of an arbitrary set of properties for matching sources, and retrieve any of the associated file-based data products for further analysis. CSCview provides a form-based data-mining interface, but also allows users to enter queries written using the Astronomical Data Query Language (ADQL; Ortiz et al. 2008) standard directly. Query results can be viewed directly on the screen, or saved to a data file in multiple formats, including tab-delimited ASCII (which can be read directly by several commonly used astronomical applications) and International Virtual Observatory Alliance⁵ (IVOA) standard formats such as VOTable (Ochsenbein 2009).

Automated access to query the catalog from data analysis applications and scripts running on the user’s home platform was identified as being needed for several science use cases. VO standard interfaces, including Simple Cone Search (Williams et al. 2008) and Simple Image Access (Tody & Plante 2009), provide limited query and data access capabilities, while more sophisticated interactions are possible through a direct URL connection. Support for VO workflows using applicable standards will be added in the future as these standards stabilize. An interface that integrates catalog access with a visual sky browser provides a simple mechanism for visualizing the regions of the sky included in the catalog, and may also be particularly beneficial for education and public outreach purposes.

Since Chandra is an ongoing mission, the CSC includes a mechanism to permit newly released observations to be added to the catalog and be made visible to end users, while at the same time providing stable, well-defined, and statistically well-characterized released catalog versions to the community. This is achieved by maintaining a revision history for each database table record, together with flags that establish whether catalog quality assurance and catalog inclusion criteria are met, and using distinct views of the catalog databases that utilize these metadata.

“Catalog release views” provide access to each released version of the catalog, with the latest released version being the default. Catalog releases will be infrequent (no more than of order 1 per year) because of the controls built in to the release process, and because of the requirement that each release be accompanied by a detailed statistical characterization of the included source properties. Once data are included in a catalog release view, then they are frozen in that view, even if the source properties are revised or the source is deleted in a later catalog release. A source may be deleted if the detection is subsequently determined to be an artifact of the data or processing, but the most likely reason that a source is

⁴ http://cxc.cfa.harvard.edu/csc/

⁵ http://www.ivoa.net/deleted from a later catalog release is that additional observations included in the later release resolve the former detection into multiple distinct sources.

“Database views” provide access to the catalog database, including any new content that may not be present in an existing catalog release. Because on-going processing is continually modifying the catalog database, tabulated data and file-based data products in a database view may be superseded at any time, and the statistical properties of the data are not guaranteed.

We anticipate that users who require a stable, well-characterized dataset will choose primarily to access the catalog through the latest catalog release view. On the other hand, users who are interested in searching the latest data to identify sources with specific signatures for further study will likely use the latest database view.

2.5. Data Content

The first release of the CSC includes detected sources whose flux estimates are at least 3 times their estimated $1\sigma$ uncertainties, which typically corresponds to about 10 net (source) counts on-axis and roughly 20–30 net counts off-axis, in at least one energy band. In this release, multiple observations of the same field are not combined prior to source detection, so the flux significance criterion applies to each observation separately.

For each source detected in an observation, the catalog includes approximately 120 tabulated properties. Most values have associated lower and upper confidence limits, and many are recorded in multiple energy bands. The total number of columns included in the Source Observations Table (including all values and associated confidence limits for all energy bands) is 599.

Roughly 60 master properties are tabulated for each distinct X-ray source on the sky, generated by combining measurements from multiple observations that include the source. Combining all values and associated confidence limits for all energy bands yields a total of 287 columns included in the Master Sources Table.

The tabulated source properties fall mostly into the following broad categories: source name, source positions and position errors, estimates of the raw (measured) extents of the source and the local point spread function, and the deconvolved source extents, aperture photometry fluxes and confidence intervals measured or inferred in several ways, spectral hardness ratios, power-law and thermal black-body spectral fits for bright ( $> 150$ net counts) sources, and several source variability measures (Gregory-Loreda, Kolmogorov-Smirnov, and Kuiper tests).

Also included in the CSC are a number of file-based data products in formats suitable for further analysis in CIAO. These products, described in Table 3, include both full-field data products for each observation, and products specific to each detected observation-specific source region.

The full-field data products include a “white-light” full-field photon event list, and multi-band exposure maps, background images, exposure-corrected and background-subtracted images, and limiting sensitivity maps.

Source-specific data products include a white-light photon event list, the source and background region definitions, a weighted ancillary response file (the time-

FIG. 6.— Chandra effective area and average ACIS quiescent background as a function of energy. The blue and cyan curves present the combined HRMA plus ACIS effective area at the ACIS-S aimpoint, with zero and the late 2009 level of focal plane contamination, respectively. The red and cyan curves show the effective area at the ACIS-I aimpoint, again with zero and late 2009 contamination, respectively. The dotted black line shows the quiescent background flux density on the ACIS S3 CCD, while the solid black line represents the ACIS I3 CCD background. The energies corresponding to the edges of the CSC energy bands are shown by vertical dashed lines.

averaged product of the combined telescope/instrument effective area and the detector quantum efficiency), multi-band exposure maps, images, model ray-trace PSF images, and optimally binned light-curves. Observations obtained using the ACIS instrument additionally include low-resolution ( $E/\Delta E \sim 10\text{--}40$ , depending on incident photon energy and location on the array) source and background spectra and a weighted detector redistribution matrix file (the probability matrix that maps photon energy to detector pulse height).

2.5.1. Energy Bands

The energy bands used to derive many CSC properties are defined in Table 4. The energy bands are chosen to optimize the detectability of X-ray sources while simultaneously maximizing the discrimination between different spectral shapes on X-ray color-color diagrams.

The effective area of the telescope (including both the Chandra High Resolution Mirror Assembly [HRMA] and the detectors) is shown in Figure 6 as a function of energy, together with the average ACIS quiescent backgrounds derived from blank sky observations (Markevitch 2001a). The effective area is measured at the locations of the nominal “ACIS-S” aimpoint on the ACIS S3 CCD, and the nominal “ACIS-I” aimpoint on the ACIS I3 CCD.

Where possible, the energy bands are chosen to avoid large changes of effective area within the central region of the band, since such variations degrade the accuracy of the monochromatic effective energy approximation described below. For example, the M-edge of the Iridium coating on the HRMA has significant structure in the $\sim 2.0\text{--}2.5$ keV energy range that provides a natural breakpoint between the ACIS medium and hard energy bands. Note however, that large effective area variations are unavoidable within the ACIS broad and soft energy bands and the HRC wide energy band.

Weighting the effective area by the source spectral shape and integrating over the bandpass provides an indication of the relative detectability of a source in theTABLE 4
CSC ENERGY BANDS

Band Name	Band Designation	Energy^a Range	Monochromatic^a Energy	Integrated ACIS-I	Effective ACIS-S	Area^b HRC-I
ACIS Energy Bands
Ultra-soft	u	0.2–0.5	0.4	7.36–2.24	68.7–23.0	...
Soft	s	0.5–1.2	0.92	216–155	411–274	...
Medium	m	1.2–2.0	1.56	438–401	539–493	...
Hard	h	2.0–7.0	3.8	1590–1580	1680–1670	...
Broad	b	0.5–7.0	2.3	2240–2140	2630–2440	...
HRC Energy Band
Wide	w	0.1–10	1.5	...	...	605

^a keV.^b keV cm², computed at the ACIS-I, ACIS-S, and HRC-I aimpoints. For ACIS energy bands, the pair of values are the integrated effective area with zero focal plane contamination (first number) and with the late 2009 level of focal plane contamination (second number).

energy band. Selecting energy band boundaries so that source detectability is roughly the same in different energy bands more uniformly distributes Poisson errors across the bands, and so enhances detectability in the various bands.

Several different source types were simulated when selecting energy bands. These included absorbed non-thermal (power-law) models with photon index values ranging from 1 to 4, absorbed black-body models with temperature varying from 20 eV to 2.0 keV, and absorbed, hot, optically-thin thermal plasma models (Raymond & Smith 1977) with $kT = 0.25$ –4.0 keV. In all cases, the Hydrogen absorbing column was varied over the range $1.0 \times 10^{20}$ – $1.0 \times 10^{22}$ cm⁻². Detected X-ray spectra were simulated using PIMMS (Mukai 2009), and then folded through the bandpasses to construct synthetic X-ray color-color diagrams (see Fig. 7 for example color-color diagrams based on the final band parameters). Energy bands chosen to fill the color-color diagrams maximally provide the best discrimination between different spectral shapes. For detailed X-ray spectral-line modeling, the Raymond & Smith (1977) models have been superseded by more recent X-ray plasma models (e.g., Mewe et al. 1995; Smith et al. 2001). However, since the radiated power of the newer models as a function of temperature is not significantly different from the 1993 versions of the Raymond & Smith (1977) models used here, the latter are entirely adequate for the purpose of evaluating coverage of the X-ray color-color diagrams and the task is greatly simplified because of their availability in PIMMS⁶.

Grimm et al. (2009) compared broad band X-ray photometry with accurate ACIS spectral fits and found that model-independent fluxes could be derived from the photometry measurements to an accuracy of about 50% or better for a broad range of plausible spectra. They used similar but not identical energy bands to those adopted for the CSC, but did not use the method of deriving fluxes from individual photon energies employed herein.

Combining all of these considerations (McCollough

yields the following selection of energy bands for the CSC.

The ACIS soft (s) energy band spans the energy range 0.5–1.2 keV. The lower bound is a compromise that is set by several considerations. ACIS calibration uncertainties increase rapidly below 0.5 keV, so this establishes a fairly hard lower limit to avoid degrading source measurements in the energy band. As shown in Figure 8, below about 0.6 keV the background count rate begins to increase rapidly, while the integrated effective area rises very slowly resulting in few additional source counts. While pushing the band edge to higher energy will result in a lower background, the integrated effective area drops rapidly if the lower bound is raised above $\sim 0.8$ keV, reducing the number of source counts collected in the band. We choose to set the lower bound equal to 0.5 keV since doing so enhances the detectability of super-soft sources, while not noticeably impacting measurements of other sources. The upper cutoff for the soft energy band is set equal to 1.2 keV, which balances the preference for uniform integrated effective areas amongst the energy bands with the desire to maximize the area of X-ray color-color plot parameter space spanned by the simulations.

The lower bound of the ACIS medium (m) energy band matches the upper bound of the soft energy band. We locate the upper band cutoff at 2.0 keV since this value tends to maximize the coverage of the X-ray color-color diagram. This value also moves the Iridium M-edge out of the sensitive medium band, and instead placing it immediately above the lower boundary of the ACIS hard (h) energy band.

The high energy boundary of the latter band is set to 7.0 keV. This cutoff provides a good compromise between maximizing integrated effective area and minimizing total background counts (Fig. 8). Above 7.0 keV, the background rate increases rapidly at the ACIS-S, while below this energy the integrated effective area decreases rapidly at the ACIS-I aimpoint. Placing the hard energy band cutoff at 7.0 keV also has the advantage that the Fe K $\alpha$ line is included in the band, allowing intense Fe line sources to be detected without compromising the measurement quality for typical catalog sources.

⁶ The newer Mekal and APEC models are included in PIMMS v4.0.FIG. 7.— Synthetic color-color diagrams computed for the ACIS hard ( $h$ ), medium ( $m$ ), soft ( $s$ ), and broad ( $b = h + m + s$ ) energy bands. Left: Absorbed power-law models. The solid lines are lines of constant photon index $\Gamma = 1.0, 2.0, 3.0$ , and $4.0$ (from right to left). The dashed lines are lines of constant neutral Hydrogen column densities $N_{\text{H}} = 1.0 \times 10^{20}, 1.0 \times 10^{21}, 2.0 \times 10^{21}, 5.0 \times 10^{21}$ , and $1.0 \times 10^{22} \text{ cm}^{-2}$ (from bottom to top). Right: Hot, optically thin thermal plasma models (Raymond & Smith 1977). The solid lines are lines of constant temperature $kT = 0.25, 0.5, 1.0, 2.0$ , and $4.0 \text{ keV}$ (from left to right). The dashed lines are lines of constant neutral Hydrogen column densities $N_{\text{H}} = 1.0 \times 10^{20}, 1.0 \times 10^{21}, 2.0 \times 10^{21}, 1.4 \times 10^{22}$ , and $1.75 \times 10^{22} \text{ cm}^{-2}$ (from bottom to top). Energy bands were chosen to optimize the ability to estimate spectral parameters from color-color diagrams.

The ACIS broad ( $b$ ) band covers the same energy range as the combined soft, medium, and hard bands, and therefore spans the energy range $0.5\text{--}7.0 \text{ keV}$ .

Simulations indicate that an additional energy band extending below $0.5 \text{ keV}$ is beneficial for discriminating super-soft X-ray sources in color-color plots. The ACIS front-illuminated CCDs have minimal quantum efficiency below $0.3 \text{ keV}$ , while the response of the back-illuminated CCDs extends down to $\sim 0.1 \text{ keV}$ . Hydrocarbon contamination is present on both the HRMA optics (Jerius 2005) and the ACIS optical blocking filter (Marshall et al. 2004). The latter reduces the effective area at low energies, and enhances the depth of the Carbon K-edge. An ACIS ultra-soft ( $u$ ) band covering $0.2\text{--}0.5 \text{ keV}$ is added to provide better discrimination of super-soft sources. Source detection is not performed in this energy band, because of the typical lower overall signal-to-noise ratio (SNR) and the resulting enhanced false-source rate.

Finally, since the HRC (particularly HRC-I) has minimal spectral resolution, a single wide ( $w$ ) band that includes essentially the entire pulse height spectrum (specifically, PI values $0 : 254$ ), roughly equivalent to $0.1\text{--}10 \text{ keV}$ , is used for HRC observations.

While bands in these general energy ranges give the best balance of count rate and spectral discrimination, our simulations indicate that the exact choice of band boundary energies is not critical at the 10% level.

2.5.2. Band Effective Energies

In principle, the variations of HRMA effective area, detector quantum efficiency, and (for ACIS) focal plane contamination, with energy imply that energy-dependent data products such as exposure maps or PSFs should be constructed by integrating the source spectrum over the energy band. This approach would be both extremely time-consuming, and require knowledge of the source spectrum that is typically not available a priori. In practice, a monochromatic effective energy is chosen for each energy band to be used to construct energy dependent data products (McCollough 2007).

The monochromatic effective energy for each band is

determined using the relation

$E_{\text{eff}} = \frac{\int dE EA(E)Q(E)C(E)S(E)}{\int dE A(E)Q(E)C(E)S(E)}, \quad (1)$

where $E$ is the energy, $A$ is the effective area of the HRMA, $Q$ is the detector quantum efficiency, $C$ is the reduction in transmission due to focal plane contamination, $S$ is a power-law spectral weighting function of the form $(E/E_0)^{-\alpha}$ , and the integral is performed over the energy band.

The monochromatic effective energies for each energy band were calculated for sources located at the ACIS-I and ACIS-S aimpoints, and also for the nominal aimpoint on the HRC-I detector. Since the CSC is constructed from observations acquired throughout the Chandra mission, ACIS focal plane contamination models with both zero contamination (appropriate for observations obtained early in the mission) and the contamination level current as of late 2009 were employed. Power-law spectral weighting functions with $\alpha$ varying from $0.0$ to $2.0$ were used. Setting $\alpha = 1$ gives a spectral weighting function that approximates an absorbed $\Gamma = 1.7$ power-law spectrum, and the limits for $\alpha$ were chosen to span the typical range of values determined from fits to a canonical subset of Chandra datasets. The remaining parameters in equation (1) are extracted from the Chandra calibration database (CalDB; George & Corcoran 2005; Graessle et al. 2006). The monochromatic effective energies for ACIS were chosen to be the approximate arithmetic means of the $\alpha = 1$ values derived for the ACIS-I and ACIS-S aimpoints, with zero and late 2009 focal plane contamination. For ACIS energy bands other than the broad band, the monochromatic effective energies computed for a single value of $\alpha$ all agree within $\lesssim 0.1 \text{ keV}$ . The dependence on $\alpha$ is similarly small, except for the hard energy band, where varying $\alpha$ from $0.0$ to $2.0$ changes the monochromatic effective energy from $\sim 4.2 \text{ keV}$ to $\sim 3.4 \text{ keV}$ . For the ACIS broad energy band, the agreement between the different models for a single value of $\alpha$ is $\sim \pm 0.3 \text{ keV}$ . However, for this band the dependence on $\alpha$ is more significant, varying from $\sim 3.3 \text{ keV}$ for $\alpha = 0.0$ to $\sim 1.6 \text{ keV}$ for $\alpha = 2.0$ . The monochromaticFIG. 8.— Left: Plot shows how the ACIS soft (s) energy band integrated effective area and background count rate per CCD vary with the choice of lower bound for the energy band. Markers for different lower bounds are shown. The individual curves show the relationship at the ACIS-S and ACIS-I aimpoints, and with zero and the late 2009 level of focal plane contamination, as follows. Solid line: ACIS-S aimpoint, no contamination; dotted line: ACIS-S aimpoint, late 2009 contamination; dashed line: ACIS-I aimpoint, no contamination; dash-dotted line: ACIS-I aimpoint, late 2009 contamination. Right: Plot shows how the ACIS hard (h) energy band integrated effective area and background count rate per CCD vary with the choice of upper bound for the energy band. Markers and line styles are the same as in the left panel.

effective energies used to construct the CSC are reported in Table 4.

Although the use of a single monochromatic effective energy for each energy band simplifies data analysis by removing the dependence on the source spectrum, some error will be introduced for sources that have either extremely soft or extremely hard spectra compared to the canonical $\alpha = 1.0$ power-law spectral weighting function. Knowledge of the expected magnitude of the error that may be introduced is helpful when evaluating catalog properties.

For both the ACIS medium and hard energy bands, neither extremely soft nor extremely hard source spectra induce variations in exposure map levels that are greater than $\sim 10%$ , so photometric errors due to source spectral shape should not exceed this value. In the ACIS soft energy band, very soft spectra may produce deviations of order 5–20%, with the largest excursions expected for the front-illuminated CCDs. These differences increase to $\sim 15$ –35% for the ACIS ultra-soft energy band, with the largest values once again associated with the front-illuminated CCDs. For all of the ACIS narrow energy bands, the errors induced by extremely hard spectra are much smaller than those caused by extremely soft spectra. The presence of the Iridium edge and the large energy ranges included in the ACIS broad and HRC wide energy bands may produce significantly larger variations for extreme spectral shapes. Very soft spectra can alter exposure map values by $\sim 65$ –90% in the ACIS broad energy band, although there is little impact in the HRC wide energy band. Conversely, extremely hard spectra may induce changes up to $\sim 70%$ in the HRC wide energy band, and $\sim 25$ –30% in the ACIS broad energy band. As described in § 4.4, model-based statistical characterization of CSC source fluxes (F. A. Primini et al. 2010, in preparation) produces results that are generally consistent with these expectation, with the exception that flux errors in the ACIS broad energy band appear to be $\sim 10%$ for most sources.

When computing fluxes for point sources, an aperture correction is applied to compensate for the fraction of the PSF that is not included in the aperture. Since the

extent of the Chandra PSF varies with energy, using a monochromatic effective energy can introduce a flux error because the energy dependence of the PSF fraction is not considered. This error can be bounded by a post facto comparison of PSF fractions for catalog source detections in the 5 ACIS energy bands. The majority of variations between energy bands fall in the range 4–8%, with 90% of source detections showing $< 10%$ differences. These values represent an upper bound on the error introduced within an energy band by the use of a monochromatic effective energy.

2.5.3. Coordinate Systems and Image Binning

As described previously, X-ray photon event data are recorded in the form of a photon event list. The pixel position on the detector where a photon was detected is recorded in the “chip” pixel coordinate system. Event positions are remapped to celestial coordinates through a series of transforms, as described by McDowell (2001). The first step in this process remaps chip coordinates to a uniform real-valued virtual “detector” pixel space by applying corrections for the measured detector geometry, and instrumental and telescope optical system distortions recorded in the CalDB. Subsequent application of the time-dependent aspect solution removes the spacecraft dither motion, and maps the event positions to a uniform virtual “sky” pixel plane. The latter has the same pixel scale as the original instrumental pixels, but is oriented with North up (+Y direction) and is centered at the celestial coordinates of the tangent plane position for the observation. As an aid to users, the location of each event in each coordinate system is recorded in the calibrated photon event list. A simple unrotated world coordinate system transform maps sky positions to ICRS right ascension and declination by applying the plate scale calibration to the difference between the position of the source and a fiducial point, which is typically the optical axis of the telescope. The celestial coordinates of the fiducial point are determined from the aspect solution.

Sky images are constructed from the calibrated photon event lists by binning photon positions in sky coordinates```

graph TD subgraph Per-Observation S1[Select Observation
Sec. 3.1] --> S2[Recalibrate Observation
Sec. 3.2] S2 --> S3[Determine Background
Sec. 3.3] S3 --> S4[Detect Sources and Identify Apertures
Sec. 3.4] end S4 --> S5[Determine Source Position and Errors
Sec. 3.5] S5 --> S6[Compute Local PSF and Source Extent
Sec. 3.6] S6 --> S7[Extract Aperture Photometry Values
Sec. 3.7] S7 --> S8[Compute Limiting Sensitivity Map
Sec. 3.8] S8 --> D{ACIS >= 150
Net Counts?} D -- yes --> S9[Perform Spectral Model Fits
Sec. 3.9] D -- no --> S10[Compute Spectral Model Energy Fluxes
Sec. 3.10] S9 --> S10 S10 --> S11[Compute Spectral Hardness Ratios
Sec. 3.11] S11 --> S12[Compute Variability & Light Curve
Sec. 3.12] S12 --> S13[Set Codes & Flags
Sec. 3.13]


FIG. 9.— High-level flow diagram depicting the steps used to process each observation’s full field-of-view, detect sources, and extract the physical properties for each detected source. The references identify the relevant sections of the text that describe in detail the methods used.

into a regular, rectangular image pixel grid. A consequence of constructing images by binning in sky coordinates is that *Chandra* images are always oriented with North up. The choice of image blocking factor determines the number of sky pixels that are binned into a single image pixel. Full field image products associated with ACIS observations are constructed by binning the area covered by the inner  $2048 \times 2048$  sky pixels at single pixel resolution, then binning the inner  $4096 \times 4096$  sky pixels at block 2, and finally binning the entire  $8192 \times 8192$  sky pixel field at block 4. The corresponding blocking factors for HRC-I observations are 2, 5, and 12. Using a constant blocked image size of  $2048 \times 2048$  pixels reduces overall data volume, while preserving resolution in the outer areas of the field of view where the PSF size is significantly larger than a single pixel.

### 3. CATALOG GENERATION

In this section, we describe in detail the methods used to derive the X-ray source properties that are included in the CSC, with particular detail provided in cases where the algorithms are new or have been newly adapted for use with *Chandra* data.

The principal steps necessary to generate the catalog consist of processing the data for each observation’s full field-of-view, detecting X-ray sources included within that field of view, and then extracting the spatial, photometric, spectral, and temporal properties of each detected source. Figure 9 is a depiction of the high-level flow used to perform these steps. In the figure, each block references the section of the text that describes in detail the methods used. The physical properties associated with each source detection are recorded as a separate row in the catalog Source Observations Table.

Once the source detections from each observation have been evaluated, they are correlated with source detections from all other spatially overlapping observations to identify distinct X-ray sources on the sky. The steps required to perform the source cross-matching, and then

combine the data from multiple observations of a single source to evaluate the source’s properties, follow a similar flow to the one presented in Figure 10. Many of the elements that comprise the second flow are built on the foundations developed for the related steps from the first flow. For convenience and continuity of notation, the former are described in the same text sections as the latter. The properties for each distinct X-ray source are included as a separate row in the catalog Master Sources Table.

Data processing for release 1 of the CSC was performed using versions 3.0–3.0.7 of the *Chandra* X-ray Center data system (CXCD; Evans et al. 2006a,b) catalog processing system (“CAT”), with calibration data extracted from CalDB version 3.5.0. The observation recalibration steps included in CAT3.0 correspond *approximately* with those included CIAO 4.0. In several cases, programs developed for CAT3.0 to evaluate source properties have been repackaged with new interfaces for interactive use in subsequent CIAO releases (see Table 5).

#### 3.1. Observation Selection

While the CSC ultimately aims to be a comprehensive catalog of X-ray sources detected by *Chandra*, all of the functionality required to achieve that goal are not included in the release 1 processing system. A set of pre-filters is used to limit the data content to the set of observations that the catalog processing system is capable of handling.

For release 1, only public ACIS “timed-exposure” read-out mode imaging observations obtained using either the “faint,” “very faint,” or “faint with bias” datamodes are included. ACIS observations that are obtained using CCD subarrays with  $\leq 128$  rows are also excluded, because there are too few rows to ensure that source-free regions can be identified reliably when constructing the high spatial frequency background map. HRC-I imaging mode observations are not included in release 1 of the catalog, but are included in incremental release 1.1.Per-Source

graph TD A[Match Source Detections
Sec. 3.4] --> B[Combine Source Positions and Errors
Sec. 3.5] B --> C[Combine Intrinsic Source Extents
Sec. 3.6] C --> D[Combine Aperture Photometry Values
Sec. 3.7] D --> E[Propagate Spectral Model Fits
Sec. 3.9] E --> F[Propagate Spectral Model Energy Fluxes
Sec. 3.10] F --> G[Compute Spectral Hardness Ratios
Sec. 3.11] G --> H[Compute Variability
Sec. 3.12] H --> I[Set Flags
Sec. 3.13]


FIG. 10.— As Fig. 9, except that the steps used to cross-match source detections, and combine data from multiple observations to evaluate source properties, are shown.

HRC-S observations are excluded because of the presence of background features associated with the edges of “T”-shaped energy-suppression filter regions that form part of the UV/ion-shield. Observations of solar system objects are not included in the CSC.

All observations included in the CSC must have been processed using the standard data processing pipelines included in version 7.6.7, or later, of the CXCDS. This version of the data system was used to perform the most recent bulk reprocessing of *Chandra* data, and includes revisions to the pipelines that compute the aspect solution that is used to correct for the spacecraft dither motion and register the source events on the sky. Observations must have successfully passed the “validation and verification” (quality assurance) checks that are performed upon completion of standard data processing.

The largest scale lengths used to detect sources to be included in the CSC have angular extents  $\sim 30''$ . Sources with apparent sizes greater than this are either not detected, or may be detected incorrectly as multiple close sources. Prior to catalog construction, all observations are inspected visually for the presence of extended sources that may be detected incorrectly, and such observations are excluded from catalog processing. For ACIS observations, if the presence of any spatially extended emission is restricted to a single CCD only, then the data from that CCD are dropped, and sources detected on any remaining CCDs are typically included in the catalog. The latter rule allows many sources surrounding bright, extended cores of galaxies to be included in the catalog, rather than having the entire observation rejected outright.

While the visual inspection and rejection process is inherently subjective in nature, an attempt was made to calibrate the method by constructing a “training set” of several hundred observations that were processed through a test version of the catalog pipelines. The training set observations included a wide variety of point, compact, and extended sources, with differing exposures and SNR, which were classified as accept/reject based on the actual results of running the pipeline source detection and source property extraction steps. These observations and classifications were then used to train the personnel who performed the visual inspection process.

### 3.2. Observation Recalibration

Although all observations included in the CSC have been processed through the CXCDS standard data processing pipelines, we nevertheless re-run the instrument-specific calibration steps as the first step in catalog construction. One reason for reapplying the instrumental calibrations is that they are subject to continuous improvements, and may have been revised since the last time the observations were processed or reprocessed. A second reason is to ensure that a single set of calibrations are applied to all datasets, so that the resulting catalog will be calibrated as homogeneously as possible.

For ACIS, the principal instrument-specific calibrations that are re-applied are the (time-dependent) gain calibration and the correction for CCD charge transfer inefficiency (CTI). The former calibration maps the measured pulse height for each detected X-ray event into a measurement of the energy of the corresponding incident X-ray photon. CTI correction attempts to account for charge lost to traps in the CCD substrate when the charge is being read out. This effect is considerably larger than anticipated prior to launch because of damage to the ACIS CCDs caused by the spacecraft’s radiation environment. Additionally, observation-specific bad pixels and hot pixels are flagged for removal, as are “streak” events on CCD S4 (ACIS-8). The latter apparently result from a flaw in the serial readout electronics (Houck 2000). Pixel afterglow events, which arise because of energy deposited into the CCD substrate by cosmic ray charged particles, are removed using the `acis_run_hotpix` tool that is also included in CIAO. Although this program can miss some real faint afterglows, such events are very unlikely to exceed the flux significance threshold required for inclusion in the catalog. The default 0.5 pixel event position randomization in chip coordinates is used when the calibrations are reapplied.

The main instrument specific calibrations for HRC data relate to the “degapping” correction that is applied to the raw X-ray event positions to compensate for distortions introduced by the HRC detector readout hardware. Several additional calibrations compensate for effects introduced by amplifier range switching and ringing in the HRC electronics, and a number of validity tests are performed to flag X-ray event positions that cannot beTABLE 5  
CSC-RELATED CIAO TOOLS

<table border="1">
<thead>
<tr>
<th>Tool Name</th>
<th>CIAO Version</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>aprates</td>
<td>4.1</td>
<td>Calculate source aperture photometry properties</td>
</tr>
<tr>
<td>dmellipse</td>
<td>4.1</td>
<td>Calculate ellipse including specified encircled fraction</td>
</tr>
<tr>
<td>eff2evt</td>
<td>4.1</td>
<td>Calculate energy flux from event energies</td>
</tr>
<tr>
<td>lim_sens</td>
<td>4.1</td>
<td>Create a limiting sensitivity map</td>
</tr>
<tr>
<td>mkpsfmap</td>
<td>4.1</td>
<td>Look up PSF size for each pixel in an image</td>
</tr>
<tr>
<td>acis_streak_map</td>
<td>4.1.2</td>
<td>Create a high spatial frequency background map</td>
</tr>
<tr>
<td>dither_region</td>
<td>4.1.2</td>
<td>Calculate region on detector covered by a sky region</td>
</tr>
<tr>
<td>evalpos</td>
<td>4.1.2</td>
<td>Get image values at specified world coordinates</td>
</tr>
<tr>
<td>glvary</td>
<td>4.1.2</td>
<td>Search for variability using Gregory-Loreda algorithm</td>
</tr>
<tr>
<td>pileup_map</td>
<td>4.1.2</td>
<td>Create image that gives indication of pileup</td>
</tr>
<tr>
<td>modelflux</td>
<td>4.1.2</td>
<td>Calculate spectral model energy flux</td>
</tr>
<tr>
<td>srcextent</td>
<td>4.1.2</td>
<td>Compute source extent</td>
</tr>
<tr>
<td>create_bkg_map</td>
<td>4.2</td>
<td>Create a background map from event data</td>
</tr>
<tr>
<td>dmimgpm</td>
<td>4.2</td>
<td>Create a low spatial frequency background map</td>
</tr>
</tbody>
</table>

properly corrected due to amplifier saturation and other effects.

Since data are recorded continuously during an observation, a “Mission Time Line” is constructed during standard data processing that records the values of key spacecraft and instrument parameters as a function of time. These parameters are compared with a set of criteria that define acceptable values, and “Good Time Intervals” (GTIs) that include scientifically valid data are computed for the observation. The GTI filter from standard data processing is reapplied without change as part of the recalibration process.

Background event screening performed as part of catalog data recalibration is somewhat more aggressive than that performed as part of standard data processing, typically reducing the non-X-ray background. For a 10ks observation, the median catalog background rate is roughly 80% of the nominal field background rates (*Chandra* X-ray Center 2009), although there is considerable scatter. F. A. Primini et al. (2010, in preparation) include a detailed statistical analysis of the improvements to the non-X-ray background afforded by this screening.

The reduction of the background event rate is achieved by removing time intervals containing strong background flares. These time intervals are identified separately for each chip. First, the background regions of the image are identified by constructing a histogram of the event data, determining the mean and standard deviations of the histogram values, and rejecting all pixels that have values more than 3 standard deviations above the mean. An optimally-binned light curve of the background pixels is then created using the Gregory-Loreda algorithm (see § 3.12.1). Time bins for which the count rate exceeds  $10\times$  the minimum light curve value are identified. The corresponding intervals are considered to be background flares, and the GTIs are revised to exclude those periods.

We emphasize that the objective of this procedure is to remove only the most intense background flares, which occur relatively infrequently. Time intervals that include moderately enhanced background rates are not rejected by this process, since their contributions increase the overall SNR. The aggregate loss of good exposure time exceeds 25% for less than 1.5% of the observations included in the catalog; the loss is greater than 10% for

3% of the observations, and greater than 5% for 5% of the observations.

For each observation included in the CSC, the recalibrated photon event list is archived, together with several additional full-field data products. These include multi-resolution exposure maps computed at the monochromatic effective energies of each energy band and the associated ancillary data products (aspect histogram, bad pixel map, and field of view region definition), used to construct them (see Table 3).

### 3.3. Background Map Creation

For the first release of the CSC, background maps are used for automated source detection. They are created directly from each individual observation with the necessary accuracy. The general observation background is assumed to vary smoothly with position, and is modeled using a single low spatial frequency component. Although this assumption is in general satisfied across the fields of view included in this catalog release, there may be localized regions where the background intensity has a strong spatial dependence, and therefore where the detectability of sources may be reduced. Several different approaches were considered for constructing the low spatial frequency background component, including spatial transforms, low pass filters, and data smoothing. However, the most effective and physically meaningful technique is a modified form of a Poisson mean. This method, described below, estimates the local background from the peak of the Poisson count distribution included in a defined sampling area. The dimensions of the sampling area act effectively as a spatial low pass filter that determines the minimum angular size that contributes to the background.

High spatial frequency linear features, commonly referred to as “readout streaks,” result when bright X-ray sources are observed with ACIS. These streaks arise from source photons that are detected during the CCD readout frame transfer interval ( $\sim 40\ \mu\text{s}$  per row) following each exposure ( $\sim 3.2\ \text{s}$  per exposure for a typical observation). All pixels along a given readout column are effectively exposed to all points on the sky that lie along that column during the frame transfer interval, so that columns including bright X-ray sources have enhanced count rates along their length. Unless accounted for by the source detection step, the increased counts in theFIG. 11.— ACIS broad-band high and low spatial frequency background maps for observation 00735, as used for catalog source detection (Fig. 12, *Right*). *Left*: ACIS high spatial frequency background map component. Each image pixel represents  $2 \times 2$  blocked sky pixels. Intensities have an offset of  $+0.1$  count (image pixel) $^{-1}$  added, and the result is scaled logarithmically over the range 0.0375–3.75. The readout streak associated with the bright source is clearly visible. *Right*: ACIS low spatial frequency background map component. The Poisson mean includes a residual image of the bright source, at a peak level of  $\sim 0.15$  count (image pixel) $^{-1}$ .

bright readout streak are detected as multiple sources. Although readout streaks are comprised of mis-located source photons, we choose to model them as a background component.

Background maps computed for ACIS observations include contributions from both components, while HRC background maps include only the low spatial frequency component.

The reader should note that background maps are *not* used when deriving source properties such as aperture photometry. Instead, a local background value determined in an annular aperture surrounding the source is used, as described in § 3.4.1. Significant spatial variations of the observed X-ray flux on the scale of the background aperture will increase the background local variance, thus reducing the significance of the source detection, perhaps below the threshold required for inclusion of the source in the catalog. This effect is seen in some galaxy cores, where the unresolved emission contributes X-ray flux to the annular background apertures surrounding each source.

### 3.3.1. ACIS High Spatial Frequency Background

The algorithm described here is a refinement of method used by McCollough & Rots (2005) to address the impact of readout streaks on source detection. The streak map is computed at single pixel resolution independently for each ACIS CCD and energy band. The first step is to identify the bright-source-free regions on the detector. For ease of computation the orientation of the  $X$ -axis is defined to be along the chip rows (perpendicular to the readout direction) and the  $Y$ -axis is defined to be along the direction of the readout columns. To identify the source-free regions, the photon event totals,  $X_{\text{sum}}$  summed along the  $X$ -axis are constructed, and the median ( $\tilde{X}$ ), mode ( $\hat{X}$ ), and standard deviation ( $\sigma_X$ )

of the distribution of the  $X_{\text{sum}}$  values are computed. These values provide a basic characterization of the background. From an examination of many data histograms, the maximum value of  $X_{\text{sum}}$  which can still be considered background dominated is given by

 $X_{\text{sum}}(\text{max}) = \min[\tilde{X} + n \sigma_X, 2 \hat{X}],$ 

where  $n$  is set to 1. Rows for which  $X_{\text{sum}} \gg X_{\text{sum}}(\text{max})$  include a substantial bright source contribution. All rows with  $X_{\text{sum}} \leq X_{\text{sum}}(\text{max})$  (excluding off-chip and dither regions) are considered to comprise the source-free regions and are used to calculate the streak map.

The average number of events per pixel is calculated separately for each readout column ( $Y$ -axis direction) from all of the rows in the source-free regions. These values are replicated across each CCD row to create an image that includes the sum of the readout streak contribution and the mean one-dimensional low spatial frequency background component. The latter must be accounted for when combining the high spatial frequency readout streak map with the two-dimensional low spatial frequency background map.

For the algorithm to obtain a good measure of the background, of order 100 bright-source-free rows are required. This condition is satisfied for most observations. Observations with too few source-free rows poorly sample the background. This can lead to erroneously low intensities for bright readout streaks in the resulting background map, which may enhance the false source rate along these streaks. Faint sources that fall in the source-free rows will be considered to be part of the background, which can lead to similar results. Nevertheless, the algorithm is remarkably effective, even in crowded regions such as the Orion complex and the Galactic center fields. An example broad-band ACIS streak map, created forFIG. 12.— ACIS broad-band image of the central region of the field of observation 00735 (M81), which includes an extremely bright source that produces a very bright readout streak. Because photon pile-up has eroded the central peak of the bright source, the source is detected incorrectly as multiple close sources that must be rejected manually. *Left*: Numerous false sources are detected along the length of the readout streak if the latter is not modeled as part of the background. Source detections are shown in cyan. No quality assurance processing has been applied to these detections. *Right*: When the background map described in the text is used, the false sources are suppressed. Source detections in green are included in the catalog; sources in red do not meet the minimum flux significance criteria for inclusion in the catalog; sources in magenta have been rejected manually during quality assurance processing.

observation 00735 (M81), is shown in Figure 11, *Left*.

### 3.3.2. Low Spatial Frequency Background

For each observation, a low spatial frequency background map is constructed separately for each energy band and image blocking factor (see § 3.4). McCollough & Rots (2008) provide an initial discussion of this algorithm and general background map creation.

As described above, for ACIS observations the high spatial frequency background map includes a component that represents the one-dimensional average of the low frequency background over the rows used to create the streak map. This component, as well as the high spatial frequency background, are removed by subtracting the streak map from the original image from which it was created. For each image blocking factor, the difference image is constructed by subtracting the appropriately re-gridded streak map from the corresponding blocked original image.

For each pixel in the resulting difference image, a centered sampling region with dimensions  $n \times n$  pixels is defined. Spatial scales smaller than  $\sim n$  pixels are attenuated. The sampling regions are truncated at the edges of the images, and so some higher frequency information may propagate into the background map. However this effect has not been found to have any significant impact on the utility of the resulting map.

A histogram of the count distribution is constructed from the pixels included in the sampling region associated with each image pixel. The first histogram bin will typically span the count range from  $-0.5$  to  $+0.5$  for ACIS observations, since the readout streak map has been subtracted and there will be some negative pixels. The low spatial frequency background at this image pixel location is computed using a modified form of a Poisson

mean

 $b_{\text{lf}} = \text{mean}[h(a) \cup h(b) \cup h(c)],$ 

where  $h(x)$  is the number of counts in histogram bin  $x$ ,  $a$  is the bin with the maximum number of histogram counts, and  $b$  and  $c$  are the lower and higher bins immediately adjacent to  $a$ . The low spatial frequency background map is formed by computing  $b_{\text{lf}}$  for each pixel location in the image. For ACIS observations,  $n = 129$  pixels, corresponding to a spatial scale of order  $1'$  for images blocked at single pixel resolution. Figure 11, *Right* displays the ACIS broad-band low spatial frequency map for observation 00735 (M81) that corresponds to the streak map shown in the left hand panel of the figure.

### 3.3.3. Total Background Map

The first step in creating the total background map is to correct the readout streak map (for ACIS observations only) for the effects of reduced exposure near the edges of the observation that arise due to the spacecraft dither, by dividing by the appropriate band-specific normalized exposure map. Similarly, the low spatial frequency background map is corrected by dividing by the smoothed, band-specific normalized exposure map. The smoothing that is applied to the normalized exposure map in the latter case matches the smoothing applied when constructing the low spatial frequency background map. Finally, the two background components for each energy band are summed to produce the total exposure-normalized background map that is required for source detection.

Figure 12 displays the central region of the broad-band ACIS image of M81 (observation 00735), with source detections overlaid. The source detections shown in the left-hand panel are those that result if the background is modeled internally by `wavdetect` (see § 3.4, below); the panel on the right shows the source detections resultingfrom using the total background from Fig. 11. Using the background map has eliminated the false sources detected on the readout streak.

The total background maps for each energy band are also archived and accessible through the catalog. These maps differ from those used for source detection in that they have been multiplied by the normalized band-specific exposure map, and are therefore recorded in units of counts. For the convenience of the user, we also store multi-resolution photon-flux images for the full field of each observation, created by filtering the photon event list by energy band, binning to the appropriate image resolution, subtracting the total background map appropriate to the energy band, and dividing by the corresponding exposure map.

### 3.4. Source Detection

Candidate sources for inclusion in the CSC are identified using the CIAO **wavdetect** wavelet-based source detection algorithm (Freeman et al. 2002). **wavdetect** has been used successfully with *Chandra* data by a number of authors (e.g., Brandt et al. 2001; Giaconni et al. 2002; Lehmer et al. 2005; Kim et al. 2007; Muno et al. 2009), and its capabilities and limitations are well known (e.g., Valtchanov et al. 2001).

Early in the catalog processing pipeline development cycle, several different methods for detecting sources were evaluated. In addition to **wavdetect**, these included the CIAO implementations of the sliding cell (Harnden et al. 1984; Calderwood et al. 2001) and Voronoi tessellation and percolation (Ebeling & Wiedenmann 1993) algorithms, and a version of the **SExtractor** package (Bertin & Arnouts 1996) modified locally to use Poisson errors in the low count regime.

The Voronoi tessellation and percolation algorithm was quickly discarded because of the significant computational requirements and complexities for automated use. A series of simulations was used to compare the performance of the remaining methods with respect to source detection efficiency for isolated point sources, the efficiency with which close, equally-bright pairs of point sources with  $2''$  and  $4''$  separations are resolved, and false source detection rate (A. Dobrzycki, private communication; Hain et al. 2004). The first two properties were evaluated for point sources containing 10, 30, 100, and 2000 counts, with off-axis angles  $0$ – $10'$  with  $1'$  spacing, and nominal background rates for exposure times of 3, 10, 30, and 100 ks. The false source rate was evaluated as a function of off-axis angle for the same exposure times.

All three detection algorithms performed reliably for bright, isolated sources located close to the optical axis. Compared to the remaining methods, **wavdetect** had better source detection efficiency for faint sources located several arcminutes off-axis, and was able to resolve close pairs of sources more reliably than the sliding cell technique. The locally modified version of **SExtractor** provided inconsistent results, in some cases detecting large numbers of spurious sources.

These simulations were performed early in the catalog processing pipeline development cycle, as an aid in selecting the source detection algorithm to be used for catalog construction. They did not make use of the background maps described in the previous section. The ac-

tual performance of the source detection process used to construct the CSC is established from more detailed and robust simulations, as described in § 4 and references therein.

Based on the results of the simulations, **wavdetect** was selected as the source detection method of choice for the CSC.

The **wavdetect** algorithm does not require a uniform PSF over the field of view, and is effective in detecting compact sources in moderately crowded fields with variable exposure and Poisson background statistics. To detect candidate sources in a two-dimensional image  $D$ , **wavdetect** repeatedly constructs the two-dimensional correlation integral

 $C(x, y; \alpha) = \iint dx' dy' W(x - x', y - y'; \alpha) D(x', y') \quad (2)$ 

for a set of Marr (“Mexican Hat”) wavelet functions,  $W$ , with scale sizes that are appropriate to the source dimensions to be detected. The elliptical form of the Marr wavelet may be written in the dimensionless form

 $W(x, y; \alpha) = (2 - \rho^2) \exp(-\rho^2/2), \quad (3)$ 

where

 $\rho^2 = \frac{1}{a_1^2} (x \cos \phi + y \sin \phi)^2 + \frac{1}{a_2^2} (-x \sin \phi + y \cos \phi)^2$ 

and the parameters  $\alpha = (a_1, a_2, \phi)$  define the semi-major and semi-minor radii and rotation angle of the Mexican Hat.

A localized clump of counts in the image  $D$  will produce a local maximum of  $C$  if the scale sizes defined by  $\alpha$  are approximately the same as, or larger than, the dimension of the clump. To determine whether a local maximum of  $C$  is due to the presence of a *source*, the detection significance,  $S_{i,j}$ , in each image pixel  $(i, j)$  is determined from

 $S_{i,j} = \int_{C_{i,j}}^{\infty} dC p(C|n_{B,i,j}),$ 

where  $n_{B,i,j}$  is the number of background counts within the limited spatial extent of  $W$ , and  $p(C|n_{B,i,j})$  is the probability of  $C$  given the background  $B$ . If  $S_{i,j} \leq S_0$ , where  $S_0$  is a defined limiting significance level, then pixel  $(i, j)$  is identified as a source pixel.

The limiting significance level used to generate the CSC is set to  $S_0 = 2.5 \times 10^{-7}$ . This formally corresponds to  $\sim 1$  false source due to random fluctuations per  $2048 \times 2048$  pixel image, although due to the heuristics of the algorithm, the actual number of false sources may be lower. The situation is further complicated in our case because the final candidate source list output from the CSC source detection pipeline is a combination of several **wavdetect** runs in different energy bands (see below). We note that reliable quantitative estimates of the false source rates and detection efficiency can only be provided through simulations, as discussed in § 4. As described in § 2.5, we impose an additional restriction on the flux significance of a source. To ensure that the flux significance requirement is the defining criterion for a source to be included in the catalog, we have verified that the flux significances of sources that pass our **wavdetect**FIG. 13.— *Top:* Estimated flux significance versus catalog flux significance for  $\sim 11,000$  sources detected in the ACIS broad energy band in a pre-release test version of the CSC. The “estimated” flux significance is defined as the ratio  $\text{net\_counts}/\text{net\_counts\_err}$ , as reported by **wavdetect**, and correlates well with the actual flux significance used to determine catalog inclusion. Horizontal lines indicate the median of the points in each bin, and the vertical lines identify the extreme points. Boxes include 90% of the points in each bin. *Bottom:* Distribution of estimated flux significances for all detected sources (solid line), including those which fell below the flux significance threshold for the test catalog. The distribution of estimated significances for sources included in the catalog is shown by the dotted line; the dashed line is the distribution of actual flux significances for the same sources. The flux significances for all detected sources extends well below the distributions for sources included in the catalog.

threshold extend well below that required to satisfy the flux significance rule (see Figure 13). We estimate that roughly  $\sim 1/3$  of all the sources detected by **wavdetect** fall below this threshold.

Source detection is performed recursively by applying **wavdetect** to multiply-blocked sky images constructed as described in § 2.5.3. The use of a constant blocked image size maintains algorithm efficiency while not compromising detection efficiency in the outer areas of the field of view where the PSF size is significantly larger than a single pixel.

Applied to the CSC, wavelets with scales  $a_i = 1, 2, 4, 8$ , and 16 (blocked) pixels are computed for each image blocking factor and each energy band except for the ACIS ultra-soft band. This combination of wavelet scales and image blocking factors provides good sensitivity for detection of sources with observed angular extents  $\lesssim 30''$ . Some point sources with extreme off-axis angles,  $\theta > 20'$ , may not be detected because the size of the local PSF exceeds the largest wavelet scale/blocking factor combination. F. A. Primini et al. (2010, in preparation) calibrate this effect statistically.

Source detection is not performed in the ACIS ultra-soft energy band. This band is impacted heavily both by increased background and by decreased effective area because of ACIS focal plane contamination (the ratio of integrated background to effective area is 1–2 orders of magnitude larger for the  $u$  band when compared to the other ACIS energy bands). Under these circumstances we are limited by the accuracy of the background map determination; small errors in the background map result

in an unacceptable fraction of spurious source detections.

The **wavdetect** algorithm incorporates steps to compare nearby correlation maxima identified at multiple wavelet scales to ensure that each source is counted only once. After duplicates are eliminated, a source cell that includes the pixels containing the majority of the source flux is constructed. Although a source cell may have an arbitrary shape, for simplicity an elliptical representation of the source region is used throughout the CSC. The lengths of the semi-axes of this source region ellipse are set equal to the  $3\sigma$  orthogonal deviations of the distribution of the counts in the source cell.

Source region ellipses for candidate sources detected within a single observation from images with different blocking factors or in different energy bands are combined outside of **wavdetect** to produce a single merged source list. This step rejects any detections that have RMS radii smaller than the 50% enclosed counts fraction radius of the local PSF, calculated at the monochromatic effective energy of the band in which the source is detected. Such detections are likely artifacts arising from cosmic ray impacts. Candidate source detections whose centroids are closer than the local PSF radius, or that are closer than  $3/4$  of the mean detected source ellipse radii, are deemed to be duplicates. If any duplicates are identified, then the detection from the image with the smallest blocking factor is kept, and if the image blocking factors are equal, then the detection with the highest significance is used. This approach ensures that data from the highest spatial resolution blocked image will be used to detect point and compact sources. How-FIG. 14.— Histogram of detected source PSF fractions. For sources with off-axis angles  $\theta \leq 10'$ , the PSF fraction included in the source region aperture is shown by the solid line, while the dashed line displays the PSF fraction included in the background region aperture. For sources with  $\theta > 10'$ , the dotted line represents the PSF fraction included in the source region aperture and the dash-dotted line indicates the PSF fraction included in the background region aperture.

ever, knotty emission that is located on top of extended structures will tend to be identified as distinct compact sources, while the extended emission is not recorded.

#### 3.4.1. Source Apertures

Numerous source-specific catalog properties are evaluated within defined apertures. We define the “PSF 90% ECF (enclosed counts fraction) aperture” for each source to be the ellipse that encloses 90% of the total counts in a model PSF centered on the source position. Because the size of the PSF is energy dependent, the dimensions of the PSF 90% ECF aperture vary with energy band.

We define the “source region aperture” for each source to be equal to the corresponding  $3\sigma$  source region ellipse included in the merged source list, scaled by a factor of  $1.5\times$ . Like the PSF 90% ECF aperture, the source region aperture is also centered on the source position, but the dimensions of the aperture are *independent* of energy band. Evaluation of model PSFs with off-axis angles  $\lesssim 10'$  demonstrates that the dimensions of the source region aperture correspond *approximately* to the dimensions of the PSF 90% ECF ellipses for the ACIS broad energy band. This is confirmed *a posteriori* by examining the distribution of PSF aperture fractions in source and background (see below) region apertures of all individual catalog sources with ACIS broad band flux significance  $\geq 3.0$ . Figure 14 demonstrates that the source region apertures typically include  $\sim 90$ – $95\%$  of the PSF, while the background region apertures contain  $\lesssim 5$ – $10\%$ . We emphasize that while these fractions are typical, the actual PSF fractions, determined by integrating the model PSF over the source and background region apertures and excluding regions from contaminating sources, are used for the actual determination of source fluxes (see § 3.7).

Comparison of the source fluxes within the PSF 90% ECF aperture and the source region aperture provide a crude indication whether a source is extended. If the flux in the source region aperture is significantly greater than the flux in the PSF 90% ECF aperture, then the source

region determined by `wavdetect` is considerably larger than the local PSF, and the source is likely extended.

Both the PSF 90% ECF aperture and the source region aperture are surrounded by corresponding background region annular apertures. In both cases, the inner edge of the annulus is set equal to the outer edge of the corresponding source aperture, while the radius of the outer edge of the annulus is set equal to  $5\times$  the inner radius of the source region aperture. Although the background region apertures defined in this manner include  $\sim 5$ – $10\%$  of the X-rays from the source, this contamination is accounted for explicitly when computing aperture photometry fluxes.

Overlapping sources could contaminate any measurements obtained through the source and background apertures. To avoid this, both types of apertures are modified to exclude areas that are included in any overlapping source region apertures, or that fall off the detector. Areas surrounding ACIS readout streaks are also excluded from the modified background apertures. *Aperture-specific catalog quantities are derived from the event data in the appropriate modified aperture.* The fractions of the local model PSF counts that are included in the modified apertures are recorded in the catalog for each source, and are used to apply aperture corrections when computing fluxes, under the assumption that the source is well modeled by the PSF.

The modified source region and background region aperture definitions are recorded as FITS files using the spatial region file convention (Rots & McDowell 2008). CIAO (Fruscione et al. 2006) can be used to apply these regions as spatial filters to extract the photon event data for the source (or background) from the archived photon event list. To simplify access to file-based data products (see Table 3) for individual sources, we also separately store the source region photon event list, per-band exposure maps, and per-band source region images. These products include data from the rectangular region of the sky that is oriented North–South/East–West and that bounds the background region.

#### 3.4.2. Matching Source Detections from Multiple Observations

Each source record in the CSC Master Sources Table is constructed by combining source detections included in the Source Observations Table from one or more observations. A necessary first step in this process requires matching the source detections from all of the observations that include the same region of the sky. Cross-matching algorithms (e.g., Devereux et al. 2005; Gray et al. 2006) are often focussed on efficiently matching large catalogs, and typically use criteria on the position difference distribution, or cross-correlation approach techniques, for identifying matches. In many cases, these approaches assume (often implicitly) that the source PSF is at least approximately spatially uniform across the field of view, and comparable between the datasets being matched.

However, when matching source detections across multiple *Chandra* observations, the strong dependence of the PSF size with off-axis angle must be considered explicitly, since source detections that are well off-axis in one observation are often resolved into multiple sources close to the optical axis in other observations. UnderFIG. 15.— *Left:* Upper and lower images illustrate the common source matching case where the source detections from the individual observations all uniquely match a single source on the sky. The source region aperture determined from the upper image is shown in cyan, while the source region aperture determined from the lower image is shown in red. *Center:* In this case, the off-axis source region aperture computed from the source detection in the upper image, shown in cyan, overlaps multiple source region apertures from the observation in the lower image, shown in red. The cyan source detection is confused, and will be connected to the master sources associated with the red source detections using “ambiguous” linkages. *Right:* The sources detected in these observations for a confused “pair of pairs.” The fractional overlaps between the pair of cyan source region apertures and the pair of red source region apertures is sufficiently large that these detections are assigned to be resolved by human review.

these circumstances the source positions determined by `wavdetect` are *not* comparable, and cannot be used for source matching. Instead, the source matching approach used for the CSC is based on the overlaps between the PSF 90% ECF apertures of the source detections from the individual observations. Although empirical in nature, this algorithm works well for matching compact source detections between *Chandra* observations.

The detailed algorithm is described in Appendix A. The method identifies the overlap fractions between the PSF 90% ECF apertures of overlapping source detections from the observations, and separates them into three different categories.

The first category is the simplest, where the source detections from the various observations have apertures that all mutually overlap (Fig. 15, *Left*). This is the most common situation, and corresponds to the case where the source detections all uniquely match a single source on the sky. Roughly 90% of the  $\sim 18,000$  sources in the Master Sources Table that are linked to more than one source detection in the Source Observations Table fall into this category. Each of the matching entries in the latter table will be associated with the corresponding Master Sources Table entry with a “unique” linkage, as described in § 2.3.

In the second category, the aperture associated with a source detection in one observation overlaps the apertures associated with multiple distinct source detections from other observations. This circumstance typically

arises because source detections from a single observation are always assumed to be distinct; this assumption can fail very far off-axis ( $\theta \gtrsim 20'$ ), where the PSF size exceeds the maximum `wavdetect` wavelet scale/image blocking factor combination. This category is illustrated in Figure 15, *Center*, and arises most often because a source detection in one observation is resolved into multiple sources by one or more of the overlapping observations. The unresolved source detection in the Source Observations Table will be connected to all Master Sources Table entries associated with the matching resolved source detections via “ambiguous” linkages, and the detection will be flagged as confused. The X-ray photon events associated with the unresolved detection cannot be distributed across the matching resolved sources. In release 1 of the CSC, source properties derived from the detection will not be used to compute the source properties included in the Master Sources Table. Upper limits for photometric quantities could in principle be extracted from the unresolved source detection, and these would be quite valuable for variability studies. This capability will be included in a future release of the CSC.

In a few cases, a set of aperture overlaps cannot be resolved automatically using the current algorithm. This third category typically occurs when there are multiple overlapping, confused source detections. In this case, the source detections are flagged for review by a human, who is then responsible for resolving the matches. Only 415 (out of 94,676) master sources include source detections

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