TY - JOUR

T1 - Improving significance of binary black hole mergers in Advanced LIGO data using deep learning

T2 - Confirmation of GW151216

AU - Jadhav, Shreejit

AU - Mukund, Nikhil

AU - Gadre, Bhooshan

AU - Mitra, Sanjit

AU - Abraham, Sheelu

N1 - Funding Information: Authors express thanks to Ninan Sajeeth Philip, Shivaraj Kandhasamy, and the LIGO-Virgo-KAGRA Collaboration for their valuable comments and suggestions. Some of the results in this work have been derived using the pesummary package . S. J. and N. M. acknowledge support of Council for Scientific and Industrial Research (CSIR), India. NM expresses thanks to the Max Planck Society and the Leibnitz University, Hannover. B. G. acknowledges support of the Max Planck Society and the University Grants Commission (UGC), India. We acknowledge the use of GWA and the LDG clusters at IUCAA (Sarathi) and Caltech for the computational work. The follow-up analysis for GW151216 is performed with the HPC clusters hypatia at the Max Planck Institute for Gravitational Physics, Potsdam-Golm. S. M. and S. A. acknowledge support from the Department of Science and Technology (DST), India, provided under the Swarna Jayanti Fellowships scheme. This document has been assigned The Inter-University Centre for Astronomy and Astrophysics (IUCAA) Preprint No. IUCAA-03/2020 and LIGO Document No. LIGO-P2000399. Funding Information: Council of Scientific and Industrial Research, India Max-Planck-Gesellschaft Leibnitz University University Grants Commission Department of Science and Technology, Ministry of Science and Technology, India IUCAA

PY - 2021/9/22

Y1 - 2021/9/22

N2 - We present a novel machine learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216, which was not significant enough to be included in the catalog. Moreover, we achieve this by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering numerous false alarms, is crucial to improving the sensitivity for detecting true events. The sheer volume of data and a large number of expected detections also prompts the use of ML techniques. We perform transfer learning to train “InceptionV3,” a pretrained deep neural network, along with curriculum learning to distinguish GW signals from noisy events by analyzing their continuous wavelet transform (CWT) maps. MLStat incorporates information from this ML classifier into the coincident search likelihood used by the standard pycbc search. This leads to at least an order of magnitude improvement in the inverse false-alarm-rate (IFAR) for the previously “low significance” events GW151012, GW170729, and GW151216. We also perform the parameter estimation of GW151216 using seobnrv4hm_rom. We carry out an injection study to show that MLStat brings substantial improvement to the detection sensitivity of Advanced LIGO for all compact binary coalescences. The average improvement in the sensitive volume is for low chirp masses () and for higher masses (). Performance in the lower masses may become even better if the training set for the ML classifier, currently restricted to black hole binaries with component masses in the range only, is expanded to include binaries with neutron stars. Considering the impressive ability of the statistic to distinguish signals from glitches, the list of marginal events from MLStat could be quite reliable for astrophysical population studies and further follow-up. This work demonstrates the immense potential and readiness of MLStat for finding new sources in current data and the possibility of its adaptation in similar searches.

AB - We present a novel machine learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216, which was not significant enough to be included in the catalog. Moreover, we achieve this by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering numerous false alarms, is crucial to improving the sensitivity for detecting true events. The sheer volume of data and a large number of expected detections also prompts the use of ML techniques. We perform transfer learning to train “InceptionV3,” a pretrained deep neural network, along with curriculum learning to distinguish GW signals from noisy events by analyzing their continuous wavelet transform (CWT) maps. MLStat incorporates information from this ML classifier into the coincident search likelihood used by the standard pycbc search. This leads to at least an order of magnitude improvement in the inverse false-alarm-rate (IFAR) for the previously “low significance” events GW151012, GW170729, and GW151216. We also perform the parameter estimation of GW151216 using seobnrv4hm_rom. We carry out an injection study to show that MLStat brings substantial improvement to the detection sensitivity of Advanced LIGO for all compact binary coalescences. The average improvement in the sensitive volume is for low chirp masses () and for higher masses (). Performance in the lower masses may become even better if the training set for the ML classifier, currently restricted to black hole binaries with component masses in the range only, is expanded to include binaries with neutron stars. Considering the impressive ability of the statistic to distinguish signals from glitches, the list of marginal events from MLStat could be quite reliable for astrophysical population studies and further follow-up. This work demonstrates the immense potential and readiness of MLStat for finding new sources in current data and the possibility of its adaptation in similar searches.

UR - http://www.scopus.com/inward/record.url?scp=85115966973&partnerID=8YFLogxK

U2 - 10.1103/PhysRevD.104.064051

DO - 10.1103/PhysRevD.104.064051

M3 - Article

AN - SCOPUS:85115966973

VL - 104

JO - Physical Review D

JF - Physical Review D

SN - 2470-0010

IS - 6

M1 - 064051

ER -