Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning
Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning
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DOI：
10.1103/physrevd.99.082002
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发表时间：
2019-04-16
影响因子：
5
通讯作者：
Kalogera, V.
中科院分区：
文献类型：
--
作者：
Coughlin, S.;Bahaadini, S.;Kalogera, V.
激光干涉引力波天文台（LIGO）和处女座（Virgo）对致密双星并合产生的引力波的观测开启了天文学的新纪元。进行探测的一个关键挑战是确定数据中强烈的瞬态特征是由引力波引起的，还是由仪器或环境因素导致的。公民科学项目“引力间谍”（Gravity Spy）已被证明是一种有效的基础设施，它通过公民志愿者和机器学习所进行的数据分析相结合，对已知类型的噪声瞬态（毛刺）进行分类。我们介绍该项目的下一次迭代，利用相似性指数使公民科学家能够创建未知瞬态的大型数据集，进而可用于促进有监督的机器学习特征描述。这一新的发展旨在缓解一个困扰公民科学和仪器探测器工作的长期挑战：构建相对罕见事件的大量样本的能力。利用在LIGO第二次观测运行期间意外出现的两类瞬态噪声，我们展示了相似性指数在“引力间谍”项目中对发现这些新的毛刺类型可能产生的影响。
The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run, we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.