SPLASH：一种用于大视场时域巡天的基于宿主的快速超新星分类器
SPLASH: A Rapid Host-Based Supernova Classifier for Wide-Field Time-Domain Surveys
AI总结：
针对LSST海量超新星警报下光谱资源不足的问题，提出基于宿主星系测光的分类管道SPLASH，通过深度学习和随机森林推断并分类超新星，实现每秒约500个的高效分类，并能提供高纯度子集用于后续观测及恒星死亡群体研究。
AI中文摘要：
Vera C. Rubin天文台即将开展的时空遗产巡天（LSST）将探测数百万颗超新星（SNe）并产生数百万条夜间警报，远远超过现有的光谱资源。因此，快速、可扩展的测光分类方法对于识别年轻SNe进行后续观测以及开展大规模种群研究至关重要。我们提出了SPLASH，这是一种基于宿主的分类管道，仅使用宿主星系测光来推断超新星类别。SPLASH首先将SNe与其宿主关联（得出红移估计），然后使用深度学习推断宿主星系的恒星质量和恒星形成率，最后使用在这些推断属性上训练的随机森林，结合宿主-SN角分离和红移对SNe进行分类。SPLASH实现了76%的二元（Ia型与核心坍缩型）分类准确率和69%的F1分数，与其他最先进的方法相当。通过仅选择最自信的预测，SPLASH可以返回所有主要SN类型的高纯度子集，使其非常适合有针对性的后续观测。其高效的设计允许每秒分类约500个SNe，使其成为下一代巡天的理想选择。此外，其中间推断步骤允许根据宿主环境选择暂现源，提供的工具不仅用于分类，还可用于探测恒星死亡的群体特征。
英文摘要：
The upcoming Legacy Survey of Space and Time (LSST) conducted by the Vera C. Rubin Observatory will detect millions of supernovae (SNe) and generate millions of nightly alerts, far outpacing available spectroscopic resources. Rapid, scalable photometric classification methods are therefore essential for identifying young SNe for follow-up and enabling large-scale population studies. We present SPLASH, a host-based classification pipeline that infers supernova classes using only host galaxy photometry. SPLASH first associates SNe with their hosts (yielding a redshift estimate), then infers host galaxy stellar mass and star formation rate using deep learning, and finally classifies SNe using a random forest trained on these inferred properties, along with host-SN angular separation and redshift. SPLASH achieves a binary (Type Ia vs. core-collapse) classification accuracy of $76\%$ and an F1-score of $69\%$, comparable to other state-of-the-art methods. By selecting only the most confident predictions, SPLASH can return highly pure subsets of all major SN types, making it well-suited for targeted follow-up. Its efficient design allows classification of $\sim 500$ SNe per second, making it ideal for next-generation surveys. Moreover, its intermediate inference step enables selection of transients by host environment, providing a tool not only for classification but also for probing the demographics of stellar death.
