Flexible Analysis of Gravitational Wave Data: Extracting Information from Unmodeled or Partially Modeled Sources and Mitigating Instrument Glitches

California Institute Of Technology, Pasadena CA

Investigators

Abstract

The field of gravitational wave astrophysics is experiencing accelerating growth since its birth in 2015. This is the outcome of large improvements in the sensitivity of gravitational wave detectors as well as the continued development of advanced tools to interpret the data the detectors collect. The wealth of signals and information brings new challenges to be addressed both in the analysis of further signals from compact binaries, such as colliding black holes and neutron stars, and in the interpretation of anticipated novel signals made accessible with more sensitive detectors. One such example concerns noise artifacts in the detectors that can occur at the same time as astrophysical signals and jeopardize our ability to interpret them. Such noise artifacts have to be understood and ideally removed from the data before any further analysis of the astrophysical signals. This project aims to address some of these emerging challenges with flexible data analysis techniques that can handle the large expected variety of detector noise artifacts and yet unseen signals. This project concerns the use of flexible, morphology-independent analyses for data analysis during the fourth observing run of LIGO as well as the development of novel analyses for the interpretation of anticipated signals such as inspiral and post merger emission from neutron star binaries. Regarding the former, past experience indicates that the increased rate of detection expected during the fourth observing run will result in more instances of astrophysical signals overlapping with instrumental glitches. This project aims to improve upon the techniques already utilized by the LIGO and Virgo Collaborations to provide more efficient glitch subtraction on data that also include an astrophysical signal of interest. Regarding the latter, this project will explore ``hybrid" data analysis techniques capable of analyzing partially modeled signals for which we lack exact waveform templates. The aim is to analyze and extract information from signals such as neutron star mergers that carry important information about the neutron star equation of state. The result of these activities will facilitate analyses of LIGO/Virgo data for extraction of astrophysical information as well as novel techniques to meet the demands of the detector improved sensitivity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.