论文标题
超新星时间序列分类的卷积神经网络方法
A Convolutional Neural Network Approach to Supernova Time-Series Classification
论文作者
论文摘要
超新星（SNE）是宇宙中最亮的物体之一，是标志着恒星一生的末端的强大爆炸。超新星（SN）类型是由光谱发射线定义的，但是获得光谱法在逻辑上通常是不可行的。因此，仅使用时间序列图像数据鉴定SNE的能力至关重要，尤其是鉴于即将到来的望远镜的广度和深度的增加。我们提出了一种用于快速超新星时间序列分类的卷积神经网络方法，观察到的亮度数据在波长和时间方向上都通过高斯过程回归平滑。我们将此方法应用于完整持续时间和截断的SN时间序列，以模拟回顾性和实时分类性能。回顾性分类用于区分宇宙学上有用的IA SNE与其他SN类型的类型，并且此方法在此任务上的精度> 99％。我们还能够在只有两个晚上的数据和98％的准确度回顾性的情况下以60％精度区分6种SN类型。
One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emission lines, but obtaining spectroscopy is often logistically unfeasible. Thus, the ability to identify SNe by type using time-series image data alone is crucial, especially in light of the increasing breadth and depth of upcoming telescopes. We present a convolutional neural network method for fast supernova time-series classification, with observed brightness data smoothed in both the wavelength and time directions with Gaussian process regression. We apply this method to full duration and truncated SN time-series, to simulate retrospective as well as real-time classification performance. Retrospective classification is used to differentiate cosmologically useful Type Ia SNe from other SN types, and this method achieves >99% accuracy on this task. We are also able to differentiate between 6 SN types with 60% accuracy given only two nights of data and 98% accuracy retrospectively.
