论文标题
使用LSTM神经网络对IA型超新星的光谱研究
Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks
论文作者
论文摘要
我们提出了一种基于长期记忆（LSTM）神经网络的数据驱动方法，以分析IA型超新星（SNE IA）的光谱时间序列。该数据集包括361个单个sne ia的3091个光谱。该方法允许根据最大光周围观察到的单个光谱准确地重建SN IA的光谱序列。光谱重建的精度随光谱时间覆盖范围的增加而增加，但是在光学最大值附近的多个时期数据的显着好处仅对于分隔超过一周的观测值才能明显。该方法在提取SNE IA的光谱信息方面显示出很大的力量，并表明可以从光学最大值周围的单个频谱中得出最关键的信息。我们开发的算法对于使用LSST/Rubin和WFIRST/ROMAN望远镜对光谱镜的后续观察计划很重要。
We present a data-driven method based on long short-term memory (LSTM) neural networks to analyze spectral time series of Type Ia supernovae (SNe Ia). The dataset includes 3091 spectra from 361 individual SNe Ia. The method allows for accurate reconstruction of the spectral sequence of an SN Ia based on a single observed spectrum around maximum light. The precision of the spectral reconstruction increases with more spectral time coverages, but the significant benefit of multiple epoch data at around optical maximum is only evident for observations separated by more than a week. The method shows great power in extracting the spectral information of SNe Ia, and suggests that the most critical information of an SN Ia can be derived from a single spectrum around the optical maximum. The algorithm we have developed is important for the planning of spectroscopic follow-up observations of future SN surveys with the LSST/Rubin and the WFIRST/Roman telescopes.
