iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors

• DOI: 10.1088/2632-2153/abab5f
• Publication Date: 2020-07-31T22:32:13Z
• Authors: Reed Essick; Patrick Godwin; Chad Hanna; Lindy Blackburn; Erik Katsavounidis
• Keywords: Gaussian Noise

Gravitational-wave detectors are exquisitely sensitive instruments and routinely enable ground-breaking observations of novel astronomical phenomena. However, they also witness non-stationary, non-Gaussian noise that can be mistaken for astrophysical sources, lower detection confidence, or simply complicate the extraction signal parameters from noisy data. To address this, we present iDQ, a supervised learning framework to autonomously detect artifacts in gravitational-wave based only on auxiliary degrees freedom insensitive gravitational waves. iDQ has operated low latency throughout advanced detector era at each two LIGO interferometers, providing invaluable data quality information about date real-time. We document algorithm, describing statistical possible applications within searches. In particular, construct likelihood-ratio test simultaneously accounts presence utilizes both observed strain thousands freedom. several examples iDQ's performance with modern showing ability reproduce known monitors identify not flagged by other analyses.