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AstraCLR
Paper
Majumder et al., 2026, in prep.
Original model
GitHub: https://github.com/TorshaMajumder/astra (private)
HuggingFace: https://huggingface.co/ashrot/astra-clr-base
Inference library: https://github.com/snad-space/astra-infer
License
MIT
Model overview
AstraCLR is a contrastive-learning encoder for multi-band photometric light curves. It maps a ZTF (g, r, i) light curve to a 512-dimensional embedding via a transformer architecture trained with a contrastive objective. Unlike ASTROMER, the model accepts multi-band input by concatenating per-band observation sequences with a log-wavelength channel. The ONNX file is distributed pre-built and requires no ML-framework conversion.
Inputs
| Tensor | Shape | dtype | Description |
|---|---|---|---|
input |
[batch, 700, 1] |
float32 | Inverse-variance-weighted mean-subtracted magnitude per band window |
times |
[batch, 700, 1] |
float32 | Observation time minus MJD offset (58 000) |
band_info |
[batch, 700, 1] |
float32 | lg(effective wavelength in Å) for the observation's band |
mask |
[batch, 700] |
float32 | 0 = real observation, 1 = padded |
The 700-element sequence is a concatenation of three per-band windows: g (0–299, 300 obs), r (300–649, 350 obs), i (650–699, 50 obs).
Output
| Tensor | Shape | Description |
|---|---|---|
| mean | [batch, 512] |
Light-curve embedding |
Preprocessing steps
- Sort observations chronologically within each band.
- Select the first
SEQ_PER_BAND[band]observations ("beginning" strategy). - Magnitude normalisation per band: subtract the inverse-variance-weighted mean.
norm_mag = mag − Σ(mag/magerr²) / Σ(1/magerr²) - Time normalisation:
norm_time = mjd − 58 000 - Band channel:
band_info = lg(eff_wavelength_Å)— g: lg(4746.48), r: lg(6366.38), i: lg(7829.03) - Padding: zero-pad shorter windows to the required length; set
mask = 1for padded positions,mask = 0for real observations.
Model file
HuggingFace (ONNX): https://huggingface.co/light-curve/astra-clr
Source: https://huggingface.co/ashrot/astra-clr-base/blob/main/astra-clr.onnx Training data: ZTF Zubercal DR16 (https://huggingface.co/datasets/snad-space/astra-zubercaldr16_gaiadr3vclassre)