研究比较传统方法与深度学习在变星光变曲线分类上的表现
Feature-Based and Deep Learning Methods for Astronomical Light Curve Classification
arXiv 上这篇论文用 ZTF 的 4 万颗变星数据对比了两类方法，结论是经典特征模型更强，做时序分类的可以看看失败原因分析。
研究基于 Zwicky Transient Facility (ZTF) 的 StarEmbed 数据集，该数据集包含约 40,000 个天体、7 个不平衡类别，比较特征工程机器学习与端到端深度学习在变星分类上的表现。结果显示，基于物理动机特征训练的经典模型稳定优于直接吃原始光变曲线的深度网络。作者指出标签数据有限是性能差距的原因之一，光变曲线的多变量、不规则采样和形态多样也增加了深度模型的难度。论文主张通过扩大标注数据规模和在未标注巡天数据上做自监督预训练来改进。
Feature-Based and Deep Learning Methods for Astronomical Light Curve Classification
We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000 objects across seven imbalanced classes. Classical models trained on physically motivated features consistently outperform deep networks trained directly on raw light curves. We find that limited labeled data contributes to the performance gap, while the multivariate, irregularly sampled, and morphologically diverse nature of light curves poses additional challenges for deep learning models. While deep learning holds promise for learning representations directly from observations, our results indicate that current models fail to generalize with the available training set size. These findings motivate further investigation of the benefits of scaling labeled datasets and self-supervised pretraining on large unlabeled surveys. This work establishes a quantitative baseline for future research and highlights the need for further innovations to unlock the potential of deep learning in astronomical time series analysis.