Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning

被引：36

作者：

Coughlin, S. ^{[1
,2
]}

Bahaadini, S. ^{[3
]}

Rohani, N. ^{[3
]}

Zevin, M. ^{[2
]}

Patane, O. ^{[4
]}

Harandi, M. ^{[5
]}

Jackson, C. ^{[5
]}

Noroozi, V. ^{[6
]}

Allen, S. ^{[7
]}

Areeda, J. ^{[4
]}

Coughlin, M. ^{[8
]}

Ruiz, P. ^{[3
]}

Berry, C. P. L. ^{[2
]}

Crowston, K. ^{[5
]}

Katsaggelos, A. K. ^{[3
]}

Lundgren, A. ^{[9
]}

Osterlund, C. ^{[5
]}

Smith, J. R. ^{[4
]}

Trouille, L. ^{[7
]}

Kalogera, V. ^{[2
]}

机构：

[1] Cardiff Univ, Phys & Astron, Cardiff CF10 2FH, S Glam, Wales

[2] Northwestern Univ, Ctr Interdisciplinary Explorat & Res CIERA, Evanston, IL 60208 USA

[3] Northwestern Univ, Elect Engn & Comp Sci, Evanston, IL 60201 USA

[4] Calif State Univ Fullerton, Dept Phys, Fullerton, CA 92831 USA

[5] Syracuse Univ, Sch Informat Studies, Syracuse, NY 13210 USA

[6] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA

[7] Adler Planetarium, Chicago, IL 60605 USA

[8] CALTECH, Div Phys Math & Astron, Pasadena, CA 91125 USA

[9] Univ Portsmouth, Inst Cosmol & Gravitat, Portsmouth PO1 2UP, Hants, England

来源：

PHYSICAL REVIEW D | 2019年 / 99卷 / 08期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1103/PhysRevD.99.082002

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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.

引用

页数：8

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