领域信息引导的多视角自蒸馏用于基于JEPA的天文光变曲线表示学习
Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
AI总结：
针对天文光变曲线不规则采样、复杂噪声和多物理时间尺度问题，提出结合语义保持视图、不确定性感知分词和多视角自蒸馏的JEPA框架，在StarEmbed分类基准上16个指标中15个超越手工特征，并在跨域适应中表现优异。
摘要：
光变曲线描述天体亮度随时间的变化。学习光变曲线的鲁棒表示对于动态宇宙的大规模自动发现至关重要，但现有的时间序列基础模型通常难以处理天文观测中特征性的不均匀采样、复杂噪声和广泛的物理时间尺度。我们提出了一种基于联合嵌入预测架构（JEPA）的领域信息表示学习框架，用于不规则天文时间序列，结合了语义保持视图、不确定性感知分词和多视角自蒸馏。编码器通过LEAVES数据集上的LeJEPA正则化进行多视角自蒸馏训练，并在StarEmbed分类基准上进行评估。在StarEmbed上，我们的模型在16个分类指标中的15个上优于手工特征。在少样本线性探测中，每类一个样本时宏F1得分为42.56 ± 7.21，每类100个样本时为63.58 ± 1.20，持续优于手工特征。除了变星分类，学习到的表示还支持相似性搜索、参数估计和测光零点漂移检测。我们进一步在来自PYRREGULAR的12个异构不规则时间序列数据集上评估跨域适应，适应变体在5个数据集上达到或超过先前最先进性能，而任何单个先前基线最多仅赢得3个数据集。这些结果表明，领域信息引导的多视角自蒸馏是学习不规则时间序列表示的有效策略，同时也突显了成功的时间序列表示学习需要领域特定的归纳偏置，而非普遍最优的架构。
英文摘要：
Light curves describe temporal variations in the brightness of celestial objects. Learning robust representations of light curves is essential for large-scale automatic discovery in the dynamic universe, but existing time-series foundation models often struggle with the uneven sampling, complex noise, and wide range of physical timescales that characterize astronomical observations. We propose a domain-informed representation learning framework for irregular astronomical time series with Joint-Embedding predictive architecture (JEPA), combining semantics-preserving views, uncertainty-aware tokenization, and multi-view self-distillation. The encoders are trained with multi-view self-distillation using LeJEPA regularization on the LEAVES dataset and evaluated on the StarEmbed classification benchmark. On StarEmbed, our model outperforms hand-crafted features on 15 of 16 classification metrics. In few-shot linear probing, it achieves macro-F1 scores of 42.56 $\pm$ 7.21 with one sample per class and 63.58 $\pm$ 1.20 with 100 samples per class, consistently improving over hand-crafted features. Beyond variable-star classification, the learned representation supports similarity search, parameter estimation, and photometric zero-point drift detection. We further evaluate cross-domain adaptation on 12 heterogeneous irregular time-series datasets from PYRREGULAR, where the adapted variant matches or exceeds previous state-of-the-art performance on 5 datasets, compared with at most 3 wins by any single prior baseline. These results demonstrate that domain-informed multi-view self-distillation is an effective strategy for learning representations of irregular time series, while also highlighting that successful time-series representation learning requires domain-specific inductive biases rather than a universally optimal architecture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
