AstraCLR
Part of the light-curve family of open-source tools for astronomical time-series analysis.
Available from Python via the
light-curve package:
pip install light-curve. Documentation:
light-curve.snad.space.
Paper
Majumder et al., 2026, in prep.
Original model
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 = 1 for
padded positions, mask = 0 for real observations.
Model file
