使用ASTRANet捕捉伪装的瞬变：异常感知光谱分类与共形校准
Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration
AI总结：
研究针对时域巡天中罕见特殊物体光谱识别受限问题，提出ASTRANet框架，含分层光谱分类器、异常检测层和共形不确定性量化层，经实验验证其有效性，还引入AD-MCP，为置信度感知光谱发现建立了方法学基础设施。
摘要：
时域巡天每年发现数千个瞬变，但罕见和物理特性特殊物体的光谱识别仍受限于闭集分类器，其将每个输入自信地分配到已知类别，包括真正不属于任何已知类别的光谱。我们提出ASTRANet框架，这是一个用于光谱瞬变分类的置信度感知基础设施，由三个耦合模块组成：直接对观测者帧光谱操作的分层光谱分类器；非线性组合来自四个物理动机族的16个嵌入空间异常分数的异常检测层（ASTRANet-Sentinel）；共形不确定性量化层（ASTRANet-CP）。我们在289个罕见且超出分类法的瞬变的保留评估集上验证了该框架，这些瞬变跨越11个类别且故意被排除在训练之外，涵盖罕见异常群体的完整物理多样性。通过闭集分类器的五种天体物理上不同的失败模式，我们表明分类器内部不确定性和基于嵌入的异常检测是置信度的结构互补轴，而非同一估计器的替代实现。我们还在ASTRANet-CP中引入了AD分层蒙德里安共形预测（AD-MCP），在普通蒙德里安在操作区域覆盖不足的异常分数层中实现了均匀条件覆盖。这为维拉·C·鲁宾天文台时代的置信度感知光谱发现建立了方法学基础设施。
英文摘要：
Time-domain surveys discover thousands of transients per year, but the spectroscopic identification of rare and physically peculiar objects remains rate-limited by closed-set classifiers that confidently assign every input to a known class -- including spectra that genuinely belong to no known class. We present the \texttt{ASTRANet} framework, a confidence-aware infrastructure for spectroscopic transient classification built around three coupled modules: a hierarchical spectral classifier that operates directly on observer-frame spectra without requiring host-galaxy redshift or spectral phase as inputs; an anomaly detection layer (\texttt{ASTRANet-Sentinel}) that non-linearly combines $16$ embedding-space anomaly scores spanning four physically motivated families; and a conformal uncertainty quantification layer (\texttt{ASTRANet-CP}). We validate the framework on a held-out evaluation set of $289$ rare and out-of-taxonomy transients spanning $11$ classes deliberately excluded from training, chosen to span the full physical diversity of the rare-anomaly population: AGN-related outliers, GRB-related events, gap transients, novae, and peculiar supernovae. Through five astrophysically distinct failure modes of closed-set classifiers, we show that classifier-internal uncertainty and embedding-based anomaly detection are structurally complementary axes of confidence rather than alternative implementations of the same estimator. We further introduce AD-stratified Mondrian conformal prediction (AD-MCP) within \texttt{ASTRANet-CP}, achieving uniform conditional coverage across anomaly-score strata where vanilla Mondrian under-covers in the operational regime. This establishes the methodological infrastructure for confidence-aware spectroscopic discovery in the Vera C.\ Rubin Observatory era.
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