Supernovae Detection with Fully Convolutional One-Stage Framework

Kai Yin, Juncheng Jia, Xing Gao, TIANRUI SUN, Zhengyin Zhou

Abstract:

A series of sky surveys were launched in search of supernovae and generated a tremendous
amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous
burden to manually identify and report supernovae, because such data have huge quantity and
sparse positives. While the traditional machine learning methods can be used to deal with such
data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful
adaptability in this area. However, most data in the existing works are either simulated or without
generality. How do the state-of-the-art object detection algorithms work on real supernova data is
largely unknown, which greatly hinders the development of this field. Furthermore, the existing
works of supernovae classification usually assume the input images are properly cropped with a
single candidate located in the center, which is not true for our dataset. Besides, the performance of
existing detection algorithms can still be improved for the supernovae detection task. To address these
problems, we collected and organized all the known objectives of the Panoramic Survey Telescope
and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in
two datasets, and then compared several detection algorithms on them. After that, the selected Fully
Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data
augmentation, attention mechanism, and small object detection technique. Extensive experiments
demonstrate the great performance enhancement of our detection algorithm with the new datasets.