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README.md

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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`. Magnitudes must be **zero-mean normalized** before
+passing to the model (subtract the per-light-curve mean magnitude).
+
+| Tensor | Shape | Description |
+|--------|-------|-------------|
+| `input` | `[batch, 200, 1]` | Zero-mean normalized magnitudes |
+| `times` | `[batch, 200, 1]` | Observation times in MJD |
+| `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
+
+1. **Collect** MJD observation times and magnitudes for each light curve.
+2. **Zero-mean normalize** magnitudes: subtract the mean magnitude of each light curve individually (`mag -= mag.mean()`).
+3. **Truncate** each light curve to at most 200 observations (take the first 200 if longer).
+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