Distribution Methods for Detecting Supernova Gravitational Waves
Faculty Mentor Name
Michele Zanolin
Format Preference
Poster
Abstract
The goal of the project is to investigate the potential of different distributional methods in the detection of Core-Collapse supernova gravitational waves for quiet signals that would have been previously missed. To date, no supernova GW detections have been made; the leading software for supernova signal analysis, coherent waveburst, looks only at the loudest ‘event’ in a span of time data and forms its metrics for the one event. Our process instead looks at larger distributions of candidates. This statistical approach allows us to look for GWs at the strongest events as well as those at far lower signal-to-noise ratios (SNR). With the Cogherent Waveburst software, the SNR is inversely proportional to the distance from the source. This means our method could extend the reach of the LIGO interferometers and with this increased range, the volume of SN we consider as candidates goes at a cubic rate. With this project, we may be able to create the method for a far more powerful SN detection pipeline. This method would be able to assist with not only new SN Candidates, but all candidates in the past LIGO observation runs.
Distribution Methods for Detecting Supernova Gravitational Waves
The goal of the project is to investigate the potential of different distributional methods in the detection of Core-Collapse supernova gravitational waves for quiet signals that would have been previously missed. To date, no supernova GW detections have been made; the leading software for supernova signal analysis, coherent waveburst, looks only at the loudest ‘event’ in a span of time data and forms its metrics for the one event. Our process instead looks at larger distributions of candidates. This statistical approach allows us to look for GWs at the strongest events as well as those at far lower signal-to-noise ratios (SNR). With the Cogherent Waveburst software, the SNR is inversely proportional to the distance from the source. This means our method could extend the reach of the LIGO interferometers and with this increased range, the volume of SN we consider as candidates goes at a cubic rate. With this project, we may be able to create the method for a far more powerful SN detection pipeline. This method would be able to assist with not only new SN Candidates, but all candidates in the past LIGO observation runs.