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license: mit
tags:
- astronomy
- time-series
- light-curves
- onnx
library_name: onnx
---
# Astromer 1
## Paper
Donoso-Oliva, C., Becker, I., Protopapas, P., Cabrera-Vives, G., Forster, F., & Estévez, P. A. (2023). *ASTROMER: A transformer-based embedding for the representation of light curves*. Astronomy & Astrophysics, 670, A54.
```bibtex
@article{astromer1,
author = {Donoso-Oliva, C. and Becker, I. and Protopapas, P. and
Cabrera-Vives, G. and Forster, F. and Est{\'e}vez, P. A.},
title = {{ASTROMER}: A transformer-based embedding for the representation
of light curves},
journal = {Astronomy \& Astrophysics},
volume = {670},
pages = {A54},
year = {2023},
doi = {10.1051/0004-6361/202243928},
}
```
## Original code
<https://github.com/astromer-science/main-code> (Astromer v1 tag)
## License
MIT — see [LICENSE](LICENSE).
## Model overview
Astromer 1 is a transformer encoder pretrained on MACHO R-band light curves via
masked magnitude prediction. It maps irregularly-sampled photometric time series
to per-timestep contextual embeddings using an MJD-aware sinusoidal positional
encoding. The architecture uses 2 transformer layers, 4 attention heads, and a
head dimension of 64, producing 256-dimensional embeddings.
## Inputs
All tensors are `float32`. Both magnitudes and times are **zero-mean normalized** before
passing to the model (subtract the per-window mean of each).
| Tensor | Shape | Description |
|--------|-------|-------------|
| `input` | `[batch, 200, 1]` | `mag − mean(mag)` over the window |
| `times` | `[batch, 200, 1]` | `time − mean(time)` over the window |
| `mask_in` | `[batch, 200, 1]` | 1 = valid observation, 0 = padded position |
## Outputs (ONNX)
| File | Output shape | Aggregation |
|------|-------------|-------------|
| `astromer1_mean.onnx` | `[batch, 256]` | Masked mean pooling over valid timesteps |
| `astromer1_max.onnx` | `[batch, 256]` | Masked max pooling over valid timesteps |
| `astromer1_full.onnx` | `[batch, 200, 256]` | Full per-timestep sequence |
ONNX opset: 13.
## Preprocessing steps
Photometric errors are **not used** at inference — only time and magnitude are needed.
The upstream code internally expects a 3-column `[time, mag, err]` array, but the error
column is dead code in the encoder (extracted but never used). Pass dummy zeros if
running the pipeline directly.
1. **Collect** observation times (in days — need not be absolute MJD) and magnitudes.
2. **Truncate** each light curve to at most 200 observations (take the first 200 if longer).
3. **Zero-mean normalize** both columns over the window:
`time -= time.mean()`, `mag -= mag.mean()`
4. **Pad** shorter light curves to exactly 200 positions: append zeros to both `input` and `times`.
5. **Build the mask**: set `mask_in = 1` for real observations, `mask_in = 0` for padded positions.
6. **Reshape** each tensor to `[batch, 200, 1]` (add trailing dimension).
The sequence length is fixed at 200 by the pretrained weights.
## Weights
Source: [Zenodo record 18207945](https://zenodo.org/records/18207945)
Training dataset: MACHO R-band light curves
Checkpoint: `pt_macho_v1_2021.zip`
The test-data parquet file was generated with these MACHO weights using truncation to the first 200 observations.
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