Theses and Dissertations

Date of Award

5-2024

Document Type

Thesis

Degree Name

Master of Science in Interdisciplinary Studies (MSIS)

Department

Physics

First Advisor

Soma Mukherjee

Second Advisor

Edgar Corpuz

Third Advisor

Soumya Mohanty

Abstract

Core collapse supernovae (CCSN) are highly anticipated sources of gravitational waves (GW) during the on-going fourth observation run (O4) of GW detectors like LIGO and the future observation runs. The GW signal from the CCSN cannot be modeled mathematically. Several groups around the world have engaged in simulation of the predicted GW signals from CCSN sources. These simulations are carried out in supercomputers, and they incorporate general relativity, hydrodynamics, neutrino physics, mass and angular momentum of the stellar progenitor and nuclear equations of state (EoS). The output consists of simulated signals with varying duration, peak frequency, GW energy and time-frequency evolution pattern. In scientific literature, there are 55 large-scale CCSN simulations characterized by progenitor mass, duration, peak frequency, GW energy and angular momentum. The nature of these signals varies considerably because of physics inputs to describe various CCSN explosion scenarios. In this thesis, we have carried out a multivariate analysis of the available CCSN signal landscape to explore how these different explosion scenarios relate to the characteristic properties through a multivariate clustering method. The results show the existence of certain models forming statistically significant groups. We have varied the clustering parameters to further explore the robustness of such analysis. These results are important for selection of an optimum population of CCSN’s to carry out machine learning (ML) training sets which are used in CCSN search pipelines.

Comments

Copyright 2024 Raul Alberto Espinosa Perez.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/pqdtglobal1/dissertations-theses/multivariate-analysis-gravitational-wave-signal/docview/3085279183/sem-2?accountid=7119

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