20-22 March 2018
University of Washington Seattle
US/Pacific timezone

Machine Learning for transient noise event classification in LIGO and Virgo

20 Mar 2018, 10:00
25m
Physics-Astronomy Auditorium A118 (University of Washington Seattle)

Physics-Astronomy Auditorium A118

University of Washington Seattle

Oral 6: Beyond the conventional tracking Session1

Speakers

Elena Cuoco Elena Cuoco (INFN - National Institute for Nuclear Physics)Dr Elena Cuoco (EGO & INFN Pisa)

Description

Noise of non-astrophysical origin contaminates science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory and Advanced Virgo gravitational-wave detectors. Characterization of instrumental and environmental noise transients has proven critical in identifying false positives in the first observing runs. Machine-Learning techniques have, in recent years, become more and more reliable and can be efficiently applied to our problems.

Different teams in LIGO/Virgo have applied machine-learning and deep-Learning methods to different aims, from control-lock acquisition, to GW-Signal detection, to noise-Event classification.

After a general introduction to the LIGO and Virgo detectors and the Data-Analysis framework, I will describe how machine learning methods are used in Transient-Signal classification. Following an introduction to the problem, I will go through the main algorithms and the technical solutions which we have efficiently used up to now and how we plan to develop the idea in the future.

Primary author

Dr Elena Cuoco (EGO & INFN Pisa)

Presentation Materials

Peer reviewing

Paper