NAPTIME：用于鲁宾警报分类的神经过程框架
NAPTIME: A Neural-Process Framework for Rubin Alert Classification
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中文总结 AI 辅助
研究针对鲁宾天文台警报分类，提出NAPTIME神经过程框架，直接对不规则多波段光变曲线建模，结合多种信息。在两个模拟基准上评估，该框架为类似鲁宾的瞬变分类提供实用概率框架，对潮汐瓦解事件候选恢复也有效。
中文摘要 AI 辅助
维拉·C·鲁宾天文台时空遗产调查将产生大量不规则采样的多波段警报，只有少数源能得到光谱确认。潮汐瓦解事件罕见，其光变曲线可能与核变率等混淆。我们提出NAPTIME，一个用于在稀疏和部分观测背景下进行光度瞬变分类的神经过程框架。它直接对不规则多波段光变曲线建模，结合概率光变曲线重建、分类及可选的宿主星系背景和光度红移信息。我们在两个模拟基准上评估，结果表明神经过程为类似鲁宾的瞬变分类提供了实用概率框架，对聚焦潮汐瓦解事件的候选恢复也有效。
英文摘要
The Vera C. Rubin Observatory Legacy Survey of Space and Time will produce a high-volume stream of irregularly sampled multiband alerts for which spectroscopic confirmation will be available only for a small minority of sources. Tidal disruption events are rare phenomena that provide a direct probe of dormant massive black holes, but their light curves can be confused with nuclear variability and other transient subclasses. We present NAPTIME (Neural Astrophysical Photometric Transient Identification and Modeling Engine), a neural-process framework for photometric transient classification under sparse and partial observational context. NAPTIME models irregular multiband light curves directly, combining probabilistic light-curve reconstruction with classification and optional host-galaxy context, as well as photometric-redshift information. We evaluate on two simulated benchmarks: ELAsTiCC2, our primary Rubin-like broad-classification benchmark, and MALLORN, a photometry-only TDE-focused benchmark. On the 15-family ELAsTiCC2 task, the metadata-aware model reaches macro $\mathrm{F1} = 0.903$ and macro $\mathrm{AUROC} = 0.991$, while a matched photometry-only variant reaches 0.874 and 0.986. Viewed as a TDE-versus-rest ranking model, the classifier yields TDE average precision 0.985 with metadata and 0.979 without. Metadata is most valuable in the low-context regime. Using only the earliest 10\% of detected observations, macro F1 is $\sim$0.42 with metadata and $\sim$0.34 without it. On MALLORN, NAPTIME reaches macro $\mathrm{F1} = 0.693$ and macro $\mathrm{AUROC} = 0.958$. These results show that neural processes provide a practical probabilistic framework for Rubin-like transient classification and remain effective for TDE-focused candidate recovery.
