juliensimon/pantheon-plus-sne-ia

Pantheon+ Type Ia Supernovae

The gold standard cosmological dataset -- 1,701 spectroscopically confirmed Type Ia supernovae from the Pantheon+ analysis, used to measure the Hubble constant (H0) and constrain the dark energy equation of state. This is the dataset behind the "Hubble tension" debate.

Dataset description

Pantheon+ combines supernova light curves from 20 surveys spanning redshifts 0.0012 to 2.261. Each SN Ia is standardized using the SALT2 light-curve fitter, providing stretch (x1) and color (c) parameters that transform raw apparent magnitudes into calibrated distance moduli. Combined with Cepheid-calibrated distances from SH0ES, this dataset yields the most precise local measurement of the Hubble constant.

Schema

Column	Type	Description
sn_name	string	Supernova identifier (CID)
survey_id	int64	Survey identifier
redshift_hd	float64	Hubble-diagram redshift (peculiar velocity corrected)
redshift_hd_err	float64	Hubble-diagram redshift uncertainty
redshift_cmb	float64	CMB-frame redshift
redshift_cmb_err	float64	CMB-frame redshift uncertainty
redshift_helio	float64	Heliocentric redshift
redshift_helio_err	float64	Heliocentric redshift uncertainty
apparent_mag_b	float64	Apparent B-band magnitude (SALT2 mB)
apparent_mag_b_err	float64	Apparent magnitude uncertainty
stretch_x1	float64	SALT2 stretch parameter x1
stretch_x1_err	float64	Stretch uncertainty
color_c	float64	SALT2 color parameter c
color_c_err	float64	Color uncertainty
host_log_mass	float64	Host galaxy log stellar mass (solar masses)
host_log_mass_err	float64	Host mass uncertainty
fit_probability	float64	SALT2 fit probability
distance_modulus	float64	Distance modulus from SH0ES analysis
distance_modulus_err	float64	Distance modulus diagonal uncertainty

Quick stats

• 1,701 Type Ia supernovae from 20 surveys
• Redshift range: 0.0012 to 2.261 (median 0.164)
• Distance modulus range: 29.00 to 46.18 mag

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/pantheon-plus-sne-ia", split="train")
df = ds.to_pandas()

# Hubble diagram
import matplotlib.pyplot as plt
import numpy as np

valid = df[df["distance_modulus"] > 0]
plt.errorbar(valid["redshift_cmb"], valid["distance_modulus"],
             yerr=valid["distance_modulus_err"],
             fmt=".", ms=2, alpha=0.5, elinewidth=0.5)
plt.xscale("log")
plt.xlabel("Redshift (CMB frame)")
plt.ylabel("Distance modulus (mag)")
plt.title("Pantheon+ Hubble Diagram")
plt.show()

# Color-stretch distribution
plt.scatter(df["stretch_x1"], df["color_c"], s=2, alpha=0.3)
plt.xlabel("Stretch x1")
plt.ylabel("Color c")
plt.title("SALT2 Parameter Distribution")
plt.show()

# Redshift distribution
df["redshift_cmb"].hist(bins=50)
plt.xlabel("Redshift")
plt.ylabel("Count")
plt.title("Pantheon+ Redshift Distribution")
plt.show()

Data source

Scolnic, D., et al. (2022), The Pantheon+ Analysis: The Full Dataset and Light-curve Release. Astrophysical Journal, 938, 113.

Brout, D., et al. (2022), The Pantheon+ Analysis: Cosmological Constraints. Astrophysical Journal, 938, 110.

Data release: PantheonPlusSH0ES/DataRelease

Pipeline

Source code: juliensimon/space-datasets

Citation

@dataset{pantheon_plus_sne,
  author = {Simon, Julien},
  title = {Pantheon+ Type Ia Supernovae},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/pantheon-plus-sne-ia},
  note = {Based on Scolnic et al. (2022) and Brout et al. (2022) Pantheon+ data release}
}

License

CC-BY-4.0