Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

被引：101

作者：

Biswas, Rahul ^{[1
]}

Blackburn, Lindy ^{[2
]}

Cao, Junwei ^{[3
]}

Essick, Reed ^{[4
]}

Hodge, Kari Alison ^{[5
]}

Katsavounidis, Erotokritos ^{[4
]}

Kim, Kyungmin ^{[6
,7
]}

Kim, Young-Min ^{[7
,8
]}

Le Bigot, Eric-Olivier ^{[3
]}

Lee, Chang-Hwan ^{[8
]}

Oh, John J. ^{[7
]}

Oh, Sang Hoon ^{[7
]}

Son, Edwin J. ^{[7
]}

Tao, Ye ^{[9
]}

Vaulin, Ruslan ^{[4
]}

Wang, Xiaoge ^{[9
]}

机构：

[1] Univ Texas Brownsville, Brownsville, TX 78520 USA

[2] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA

[3] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Res Inst Informat Technol, Beijing 100084, Peoples R China

[4] MIT, LIGO, Cambridge, MA 02139 USA

[5] CALTECH, LIGO, Pasadena, CA 91125 USA

[6] Hanyang Univ, Seoul 133791, South Korea

[7] Natl Inst Math Sci, Taejon 305811, South Korea

[8] Pusan Natl Univ, Pusan 609735, South Korea

[9] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

来源：

PHYSICAL REVIEW D | 2013年 / 88卷 / 06期

基金：

美国国家科学基金会; 新加坡国家研究基金会;

关键词：

D O I：

10.1103/PhysRevD.88.062003

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These "glitches" can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described by this model. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the classification algorithms themselves. We discuss several plausible sources of such new information as well as the ways of propagating it through the classifiers into gravitational-wave searches.

引用

页数：24

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