Is this project an undergraduate, graduate, or faculty project?
Faculty
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Alani Miyoko, Senior Kya Schluterman, Junior Michele Zanolin - Faculty
Lead Presenter's Name
Alani Miyoko
Faculty Mentor Name
Michele Zanolin
Abstract
The goal of this paper is to quantify the capability of distributional tests to detect core-collapse supernovae using gravitational wave (GW) detectors. To date, no supernova GW detections have been made; the leading software for supernova signal analysis, Coherent Waveburst (cWB), looks only at the loudest ‘event’ in a period of time and forms its metrics for that single event. Our process instead looks to identify supernovae from a distribution of events. This statistical approach could extend the reach of the LIGO interferometers by giving more tolerance to low signal-to-noise ratios. With this increased range, the volume of supernovae we may be able to detect grows at a cubic rate. This method could produce a far more powerful supernovae detection pipeline that would be able to assist with not only new candidates, but analyzing the data collected in the past LIGO observation runs.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Detection of Gravitational Wave Signals from Core-Collapse Supernovae Using Distributional Methodologies
The goal of this paper is to quantify the capability of distributional tests to detect core-collapse supernovae using gravitational wave (GW) detectors. To date, no supernova GW detections have been made; the leading software for supernova signal analysis, Coherent Waveburst (cWB), looks only at the loudest ‘event’ in a period of time and forms its metrics for that single event. Our process instead looks to identify supernovae from a distribution of events. This statistical approach could extend the reach of the LIGO interferometers by giving more tolerance to low signal-to-noise ratios. With this increased range, the volume of supernovae we may be able to detect grows at a cubic rate. This method could produce a far more powerful supernovae detection pipeline that would be able to assist with not only new candidates, but analyzing the data collected in the past LIGO observation runs.