Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
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DOI：
10.3847/1538-3881/ac9409
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发表时间：
2022-08
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影响因子：
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通讯作者：
Xiaolong Li;F. Bianco;G. Dobler;Roee Partoush;A. Rest;Tatiana Acero-Cuellar;Riley Clarke;W. Fortino;S. Khakpash;Ming Lian
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作者：
Xiaolong Li;F. Bianco;G. Dobler;Roee Partoush;A. Rest;Tatiana Acero-Cuellar;Riley Clarke;W. Fortino;S. Khakpash;Ming Lian
光回波（LE）是星际尘埃对天体物理瞬变的反射。它们是令人着迷的天文现象，使人们能够研究散射的尘埃以及原始的瞬变。然而，LE是罕见的，并且极难检测，因为它们看起来是微弱的、扩散的、随时间变化的特征。LE的检测仍然主要依赖于人类对图像的检查，这是一种在大型天气调查时代不可行的方法。维拉C。Rubin Observatory Legacy Survey of Space and Time（LSST）将以高空间分辨率、精致的图像质量和超过数万平方度的天空生成前所未有的天文成像数据：这是LE的理想调查。然而，Rubin数据处理管道针对点源的检测进行了优化，并且将完全错过LE。在过去的几年里，人工智能（AI）对象检测框架已经实现并超越了实时、人类级别的性能。在这项工作中，我们利用来自小行星陆地撞击最后警报系统望远镜的数据集来测试一个流行的人工智能物体检测框架，即计算机视觉社区开发的You Only Look Once或YOLO，以展示人工智能在天文图像中检测LE的潜力。我们发现，人工智能框架即使在大小和质量有限的数据集上也可以达到人类水平的性能。我们探索并强调了挑战，包括类别不平衡和标签不完整，并制定了路线图，以建立一个端到端的管道，用于在高通量天文调查中自动检测和研究LE。
Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys.