Donoso-Oliva, C., Becker, I., Protopapas, P., Cabrera-Vives, G., Cádiz-Leyton, M., & Moreno-Cartagena, D. (2026). Generalizing across astronomical surveys: Few-shot light curve classification with Astromer 2. Astronomy & Astrophysics (in press).
bibtex
1@article{astromer2,
2 author = {Donoso-Oliva, C. and Becker, I. and Protopapas, P. and
3 Cabrera-Vives, G. and C{\'a}diz-Leyton, M. and Moreno-Cartagena, D.},
4 title = {Generalizing across astronomical surveys: Few-shot light curve
5 classification with {Astromer} 2},
6 journal = {Astronomy \& Astrophysics},
7 year = {2026},
8 note = {In press},
9}
Astromer 2 is a BERT-inspired transformer encoder pretrained on 1.5 million MACHO light curves via masked magnitude prediction. The encoder processes irregularly-sampled photometric time series (time, magnitude) using MJD-aware positional encoding and a trainable mask token. It produces per-timestep contextual embeddings that can be aggregated into a fixed-size representation for downstream tasks such as few-shot classification.
time — observation time in days. Need not be absolute MJD; any consistent time axis in days works because the pipeline subtracts the per-window mean before the encoder sees it. The pretrained weights were produced from MACHO data with MJD ~48800–51700.
mag — magnitude. MACHO instrumental magnitudes are typically negative (e.g. −10 to −3); the pipeline is not restricted to that range.
Photometric errors are not used at inference. The upstream preprocessing code expects a 3-column [time, mag, err] array internally, but errors only appear in the pretraining reconstruction-loss weights (outputs['w_error']), which are never passed to the encoder. Pass dummy zeros if you run the pipeline directly.
Preprocessing steps
All steps are implemented in code/src/data/loaders.py (get_loader) and code/src/data/preprocessing.py.
Step 1 — Windowing
The upstream code supports two windowing strategies via the sampling flag of to_windows:
sampling=True — random window (used during pretraining): a single contiguous window of 200 observations is drawn at a uniformly random starting position. Light curves shorter than 200 observations are used in full.
sampling=False — sequential windows (used for test-data generation): the light curve is divided into sequential, non-overlapping windows of 200 observations. A light curve of length L yields ⌊L/200⌋ + 1 windows; the last window may be shorter than 200 and is padded in step 3. Light curves shorter than 200 observations produce a single window. When a light curve produces multiple windows, each window yields a separate embedding vector; to obtain a single per-light-curve embedding, average the per-window embeddings.
Test-data is generated with sampling=False.
Source: src/data/preprocessing.py:to_windows.
Step 2 — Zero-mean normalization
Subtract the per-window column mean from each column:
x_norm = x - mean(x, axis=0) # x has shape [n_obs, 3]; columns: time, mag, err
After this step times = time − mean(time) and input = mag − mean(mag) are centred around zero.
Source: src/data/preprocessing.py:standardize.
Step 3 — Padding and mask construction
Right-pad the normalised sequence to exactly 200 time steps with zeros. Construct mask_in:
mask_in[i] = 0 for i < n_obs (real observation — visible to encoder)
mask_in[i] = 1 for i >= n_obs (padding — hidden from encoder)
Note on mask convention: the internal pipeline uses mask_in=0 for visible positions and mask_in=1 for padding/hidden positions. This is the opposite of the ONNX interface (see below).
Source: src/data/masking.py:mask_sample, padding block at the end.
Step 4 — Format encoder inputs
Extract the two encoder inputs from the normalised, padded array:
Tensor
Source
Shape
input
normalised magnitude column
[batch, 200, 1]
times
normalised time column
[batch, 200, 1]
mask_in
constructed in step 3
[batch, 200, 1]
The normalised error column is not fed to the encoder. Errors appear only in the pretraining reconstruction loss.
