仅使用宿主星系光度学对天文瞬变进行分类
Classifying Astronomical Transients Using Only Host Galaxy Photometry
摘要：
空间和时间的遗产调查（LSST）在维拉C。鲁宾天文台将发现数以万计的银河系外瞬变每晚。大量的警报要求立即对短暂类型进行分类，以便在事件消失之前优先考虑观察性随访。我们使用宿主星系的特征来分类瞬变，从而在发现时提供分类。与过去的工作相比，过去的工作侧重于使用并不总是可访问的宿主星系特征（例如，形态学），我们仅基于来自$10$光学和IR光度带的19个宿主表观幅度和颜色来确定跨$12$瞬态类的相对似然性。我们开发了二进制和多类分类器，使用核密度估计估计的基本分布的主机星系属性为每个瞬态类。即使在这个试点研究中，忽略瞬时类频率的相对差异，我们也可以区分出纯度显著高于8.3%基线的8个瞬时类（基于均匀随机分配标签的分类器）：潮汐扰动事件（$48\%\pm27\%$，其中$\pm$表示95%置信限），SNe Ia-91 bg（$32\%\pm18\%$）、SNe Ia-91 T（$23\%\pm11\%$）、SNe Ib（$23\%\pm13\%$）、SNe II（$17\%\pm2\%$）、SNe IIn（$17\%\pm6\%$）、SNe II P（$16\%\pm4\%$）和SNe Ia（$10\%\pm1\%$）。我们证明，我们的模型是适用于LSST和估计，我们的方法可以准确地分类59%的LSST警报预计每年SNe Ia，Ia-91 bg，II，Ibc，SLSN-I，潮汐中断事件。我们的代码和数据集是免费提供的。
英文摘要：
The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to prioritize observational follow-ups before events fade away. We use host galaxy features to classify transients, thereby providing classification upon discovery. In contrast to past work that focused on distinguishing Type Ia and core-collapse supernovae (SNe) using host galaxy features that are not always accessible (e.g., morphology), we determine the relative likelihood across $12$ transient classes based on only 19 host apparent magnitudes and colors from $10$ optical and IR photometric bands. We develop both binary and multiclass classifiers, using kernel density estimation to estimate the underlying distribution of host galaxy properties for each transient class. Even in this pilot study, and ignoring relative differences in transient class frequencies, we distinguish eight transient classes at purities significantly above the 8.3% baseline (based on a classifier that assigns labels uniformly and at random): tidal disruption events ($48\%\pm27\%$, where $\pm$ indicates the 95% confidence limit), SNe Ia-91bg ($32\%\pm18\%$), SNe Ia-91T ($23\%\pm11\%$), SNe Ib ($23\%\pm13\%$), SNe II ($17\%\pm2\%$), SNe IIn ($17\%\pm6\%$), SNe II P ($16\%\pm4\%$), and SNe Ia ($10\%\pm1\%$). We demonstrate that our model is applicable to LSST and estimate that our approach may accurately classify 59% of LSST alerts expected each year for SNe Ia, Ia-91bg, II, Ibc, SLSN-I, and tidal disruption events. Our code and dataset are publically available.
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