Light-curve embeddings

light_curve.embed provides pretrained neural-network models that map raw photometric time series to dense fixed-length vectors for downstream ML tasks: classification, anomaly detection, and similarity search.

Requirements

Inference uses ONNX Runtime, which you install separately because the right variant depends on your hardware:

pip install onnxruntime          # CPU
pip install onnxruntime-gpu      # NVIDIA GPU

Models are hosted on HuggingFace Hub and downloaded automatically by from_hf(). To enable automatic downloads:

pip install huggingface_hub

If you already have the ONNX model file locally, huggingface_hub is not required.

Available models

Model	Bands	Input	Embedding dim	Pretrained on
Astromer2	single (or per-band)	time, mag	256	MACHO
ATCAT	6 (ugrizY jointly)	time, flux, flux_err, band index	384	ELAsTiCC

Astromer1 is also available as a legacy model.

Single-band: Astromer2

Astromer2 accepts irregularly-sampled (time, mag) pairs and returns 256-dimensional embeddings:

import numpy as np
from light_curve.embed import Astromer2

model = Astromer2.from_hf(output="mean")

rng = np.random.default_rng(0)
time = np.sort(rng.uniform(0, 500, 120)).astype(np.float64)
mag  = rng.normal(15, 0.5, 120).astype(np.float64)

embedding = model(time, mag)
print(embedding.shape)  # (1, 1, 1, 256)
# squeeze to (256,) for a single object
vec = embedding.squeeze()

For multi-band data, pass bands=["g", "r"] to get one embedding per band:

model = Astromer2.from_hf(output="mean", bands=["g", "r"])
embedding = model(time, mag, band=band)
print(embedding.shape)  # (2, 1, 1, 256)

Multi-band: ATCAT

ATCAT processes all six LSST ugrizY bands jointly and returns 384-dimensional embeddings. Inputs are flux (AB, zero-point 31.4 by default), flux error, time, and integer band index (u=0, g=1, r=2, i=3, z=4, Y=5):

from light_curve.embed import ATCAT

model = ATCAT.from_hf(output="last")

rng = np.random.default_rng(2)
n = 120
time     = np.sort(rng.uniform(0, 500, n)).astype(np.float32)
flux     = rng.normal(100, 10, n).astype(np.float32)
flux_err = np.full(n, 5.0, dtype=np.float32)
band     = np.array([i % 6 for i in range(n)])  # ugrizY → 0–5

embedding = model(time, flux, flux_err, band)
print(embedding.shape)  # (1, 1, 1, 384)

Set mag_zp=27.5 for ELAsTiCC/SNANA FITS data, or mag_zp=8.9 for Jy.

GPU and alternative runtimes

Pass ort_session_kwargs to select an execution provider:

model = Astromer2.from_hf(
    output="mean",
    ort_session_kwargs={"providers": ["CUDAExecutionProvider"]},
)

See the onnxruntime install guide for provider options.

See the API reference for full signatures and reduction strategies.