Finding Fast Transients in Real Time Using a Novel Light-curve Analysis Algorithm

Finding Fast Transients in Real Time Using a Novel Light-curve Analysis Algorithm
复制标题

DOI:
10.3847/1538-3881/ac441b
复制
发表时间:
2021-09
期刊:
The Astronomical Journal
影响因子:
--
通讯作者:
R. Strausbaugh;A. Cucchiara;Michael Dow Jr.;S. Webb;Jielai Zhang;S. Goode;J. Cooke
R. Strausbaugh;A. Cucchiara;Michael Dow Jr.;S. Webb;Jielai Zhang;S. Goode;J. Cooke
中科院分区:
其他
文献类型:
--
作者:
R. Strausbaugh;A. Cucchiara;Michael Dow Jr.;S. Webb;Jielai Zhang;S. Goode;J. Cooke

文献摘要

相似文献

目前的数据采集率的天文瞬态调查和承诺显着更高的利率在未来十年需要开发新的方法来分析天文数据集,并迅速检测感兴趣的对象。更深、更广、更快(Deeper,Wider,Faster)计划是一项调查,重点是识别快速演变的瞬变,如快速射电爆发、伽马射线爆发和超新星冲击爆发。它采用多频同时覆盖天空的同一部分超过几个数量级。利用安装在4米布兰科望远镜上的暗能量照相机,天文学家每分钟捕捉20秒的g波段曝光,典型的视宁度为1.1“,空气质量为1.5。这些光学数据是与整个电磁波谱(从无线电到γ射线)以及宇宙射线观测同时收集的。在本文中,我们提出了一种新的实时光变曲线分析算法,旨在检测瞬态的光学数据,该算法的功能独立于,或结合,图像减法。我们提出了一个快速瞬变检测我们的算法,以及假阳性分析的样本。我们的算法是可定制的,可以调整到不同的时间尺度和通量范围内演变的瞬态敏感。
The current data acquisition rate of astronomical transient surveys and the promise for significantly higher rates in the next decade necessitate the development of novel approaches to analyze astronomical data sets and promptly detect objects of interest. The Deeper, Wider, Faster (DWF) program is a survey focused on the identification of fast-evolving transients, such as fast radio bursts, gamma-ray bursts, and supernova shock breakouts. It employs multifrequency simultaneous coverage of the same part of the sky over several orders of magnitude. Using the Dark Energy Camera mounted on the 4 m Blanco telescope, DWF captures a 20 s g-band exposure every minute, at a typical seeing of ∼1″ and an air mass of ∼1.5. These optical data are collected simultaneously with observations conducted over the entire electromagnetic spectrum—from radio to γ-rays—as well as cosmic-ray observations. In this paper, we present a novel real-time light-curve analysis algorithm, designed to detect transients in the DWF optical data; this algorithm functions independently from, or in conjunction with, image subtraction. We present a sample of fast transients detected by our algorithm, as well as a false-positive analysis. Our algorithm is customizable and can be tuned to be sensitive to transients evolving over different timescales and flux ranges.