ATCAT

HuggingFace: light-curve/atcat

Paper

Tung, Z. (2025). ATCAT: Astronomical Timeseries CAusal Transformer. arXiv:2511.00614.

@article{tung2025atcat,
  author = {Tung, Zora},
  title = {{ATCAT}: Astronomical Timeseries CAusal Transformer},
  journal = {arXiv preprint arXiv:2511.00614},
  year = {2025}
}

Original code

https://codeberg.org/zorat/atcat (git submodule at models/atcat/code/)

License

ATCAT is distributed upstream under a modified MIT license with a non-military-use restriction. See LICENSE and the upstream README.md for the exact terms.

Model overview

This integration exports the upstream ATCAT light-curve-only ELAsTiCC classifier as an ONNX embedding model. ATCAT is a causal transformer for irregularly sampled astronomical time series. The exported wrapper uses the real upstream light-curve embedder and transformer stack from the lc_only(split=0) checkpoint, and exposes hidden representations before the final classifier head.

The current export targets the upstream LC-only core model (results/elasticc/CORE/lc_only_cv_0). The LC+metadata variant is intentionally not wrapped yet because the upstream README notes that the saved metadata preprocessing artifacts are incomplete for out-of-the-box reuse.

Inputs

Tensor	Shape	Description
flux	[batch, 243]	Padded calibrated flux values
flux_err	[batch, 243]	Padded flux uncertainties
time	[batch, 243]	Padded observation times
mask	[batch, 243]	1 for valid points, 0 for padding
channel_index	[batch, 243]	LSST band indices in ATCAT order: u=0, g=1, r=2, i=3, z=4, Y=5

Outputs (ONNX)

Two files are produced with the same three named outputs:

Two files are produced, both with the same three named outputs:

Output name	Shape	Aggregation
last	[batch, 384]	Hidden state at the last valid LC observation (position num_lc_points-1)
mean	[batch, 384]	Masked mean pool of transformer outputs
sequence	[batch, 243, 384]	Per-timestep transformer features (last is the final valid element of this)

atcat_bf16.onnx is the direct export (bfloat16 weights). atcat_f32.onnx is generated automatically by prep-models atcat export by stripping the bfloat16 casts.

Request only the output(s) you need via session.run(["token"], feed) — onnxruntime will prune unused computation.

Preprocessing steps

1. Use the upstream ATCAT ELAsTiCC-derived Parquet data format or convert your data into the same padded-per-object sequence fields.
2. Keep sequence order chronological as expected by the upstream preprocessing.
3. Pad sequences to length 243 and set mask=0 for padding positions.
4. Encode LSST bands as u, g, r, i, z, Y -> 0, 1, 2, 3, 4, 5.

Weights

Source: Google Drive archive linked from the upstream ATCAT README (atcat_derived_data.tar)

Model path used by this wrapper: results/elasticc/CORE/lc_only_cv_0/checkpoints/model_40000.pt

Dataset used by this wrapper: data_parquet/split_0/test_*.parquet