StarCLR: Contrastive Learning Representation for Astronomical Light Curves

TL;DR

StarCLR is a contrastive learning framework pretrained on large-scale light curves, improving variable star classification across multiple astronomical surveys by capturing temporal features.

Contribution

It introduces a novel contrastive pretraining method for light curves, demonstrating improved classification performance over models trained from scratch.

Findings

StarCLR achieves high macro-F1 and micro-F1 scores on TESS, ZTF, and Gaia datasets.

Pretraining enhances performance especially on sparsely sampled ZTF light curves.

Ablation studies confirm the effectiveness of the pretrained representations.

Abstract

With the rapid development of time-domain surveys, the availability of massive light curve data offers new opportunities for studying stellar evolution and variable star classification, while simultaneously posing challenges for feature extraction and modeling. We present StarCLR, a contrastive pretraining framework for large-scale light curves. By constructing positive pairs from partially overlapping sub-sequences, StarCLR encourages the model to learn temporal representations. We pretrain StarCLR on the TESS dataset and fine-tune it for variable star classification on three surveys with distinct observational characteristics, namely TESS (18 types), ZTF (11 types), and Gaia (24 types). StarCLR achieves macro-F1 scores of 84.35%, 87.82%, and 92.73%, and micro-F1 scores of 94.46%, 92.83%, and 99.49%, respectively. Compared with LSTM and Transformer trained from scratch, StarCLR performs better on TESS and ZTF, with the largest gains on sparsely sampled ZTF light curves, demonstrating promising generalization. For Gaia, which involves a broader class space, the evaluation is not directly comparable, and performance is likely influenced by astrophysical features, resulting in a more limited contribution from the pretrained backbone. Systematic ablations on embedding design, pooling strategy, and pretraining settings further indicate that the pretrained representations provide performance gains by capturing informative temporal characteristics of light curves. Looking ahead, with standardized datasets and more diverse labeling schemes, the generalization ability of StarCLR can be further enhanced.