Bulletin of the American Physical Society
2023 Conferences for Undergraduate Women in Physics
Volume 68, Number 1
Saturday–Sunday, February 11–12, 2023; Virtual
Session A01: Poster Session (4:00-4:45pm ET)
4:00 PM,
Sunday, February 12, 2023
Abstract: A01.00001 : GspyNetTree: distinguishing gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run.*
Presenter:
Sofía Álvarez-López
(UNIVERSIDAD DE LOS ANDES)
Author:
Sofía Álvarez-López
(UNIVERSIDAD DE LOS ANDES)
Collaborations:
LIGO, LIGO-Virgo-KAGRA
We developed a new signal-vs-glitch classifier method that leverages a Convolutional Neural Network (CNN) image classifier, Gravity Spy, that successfully classified LIGO and Virgo glitches in previous observing runs. In order to automate validation of LIGO-Virgo GW event candidates, a signal-vs-glitch classifier must be robust to a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. In response to these new challenges, we present GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN classifier using CNNs in a decision tree sorted via total GW candidate mass.
*I thank Mitacs for funding my Globalink Research Internship and allowing me to pursue research at the University of British Columbia's gravitational-wave research group. Additionally, these findings are based upon work supported by the LIGO Laboratory, a major facility funded by the National Science Foundation.
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