Photometric Classification of Early-time Supernova Light Curves with SCONE
Photometric Classification of Early-time Supernova Light Curves with SCONE
复制标题
DOI：
10.3847/1538-3881/ac39a1
复制
发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
H. Qu;M. Sako
中科院分区：
文献类型：
--
作者：
H. Qu;M. Sako
在这项工作中，我们提出了早期超新星光变曲线的分类结果从SCONE，光度分类器，使用卷积神经网络分类超新星（SNe）的类型使用光变曲线数据。SCONE能够从任何阶段的光变曲线中识别SN类型，从最初警报的夜晚到它们的寿命结束。模拟的LSST SNe光变曲线在触发日期后的0、5、15、25和50天被截断，并用于在波长和时间空间中训练高斯过程以产生波长-时间热图。SCONE使用这些热图来执行SN类型Ia、II、Ibc、Ia-91bg、Iax和SLSN-I之间的六向分类。SCONE是能够进行分类或没有红移，但我们表明，将红移信息提高了性能在每个时代。SCONE在触发日期达到75%的总体准确性（60%没有红移），在触发后50天达到89%的准确性（82%没有红移）。SCONE也在SNe的明亮子集（r <20 mag）上进行了测试，在触发日期产生了91%的准确性（83%没有红移），在触发后5天产生了95%的准确性（94.7%没有红移）。SCONE是卷积神经网络在早期光度瞬态分类问题中的首次应用。为本文开发的所有数据处理和模型代码可以在位于github. com/helenqu/scone（Qu 2021）的SCONE软件包11 www.example.com中找到。github.com/helenqu/scone
In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021). github.com/helenqu/scone