Preparing for the fourth observing run of advanced LIGO-Virgo: Search for binary black hole mergers with the updated Coherent WaveBurst pipeline
ORAL
Abstract
The advanced LIGO-Virgo detectors have discovered gravitational wave (GW) signals from over 90 binary black hole mergers (BBH). With improved sensitivities of the advanced LIGO-Virgo detectors in the upcoming LIGO-Virgo-KAGRA (LVK) fourth observing run (O4), many more GW events are expected to be detected. In particular, we are interested in detecting exceptional high-mass BBH mergers, similar to the GW190521 event discovered in 2021 that provided the first direct evidence of an intermediate-mass black hole (IMBH). Since the signals from unprecedented events may not be well-modeled, template-independent search methods like the Coherent WaveBurst (cWB) are used in LVK data analysis. cWB can identify generic GW signals in the LIGO-Virgo data with minimal assumptions on the signal model. The cWB algorithm looks for excess power events in the time-frequency domain and uses a machine learning (ML) method to enhance the signal-noise classification of the identified events. For the O4 run, the cWB search was upgraded with a new time-frequency transform called wavescan that provides high-resolution time-frequency distributions for efficient recovery of transient GW signals. The upgraded cWB search allows for precise estimation of the signal properties, consequently improving the sensitivity of the ML-enhanced cWB search to BBH mergers. This study discusses the results of the proposed O4 cWB search on LIGO-Virgo data from the third LVK observing run (O3). We report GW detections with higher significance and demonstrate improvement in the detection efficiency by approximately 40% for simulated BBH events at a false alarm rate of less than one per year.
*This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has made use of data, software, and/or web tools obtained from the Gravitational Wave Open Science Center, a service of LIGO Laboratory, the LIGO Scientific Collaboration, and the Virgo Collaboration. This work was supported by the NSF Grant No. PHY 1806165 and PHY 2110060. We gratefully acknowledge the support of LIGO and Virgo for the provision of computational resources, especially LIGO Laboratory, which is supported by the NSF Grant No. PHY 0757058 and PHY 0823459.
–
Publication:Optimization of model independent gravitational wave search for binary black hole mergers using machine learning, Phys. Rev. D 104, 023014 Search for binary black hole mergers in the third observing run of Advanced LIGOVirgo using coherent WaveBurst enhanced with machine learning, Phys. Rev. D 105, 083018 Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning, arXiv:2210.01754 Wavescan: multiresolution regression of gravitational-wave data, arXiv:2201.01096 Planned paper: Preparing for the fourth observing run of advanced LIGO-Virgo: Search for binary black hole mergers with the updated Coherent WaveBurst pipeline
