Doctoral Dissertations

Abstract

The discovery of the binary neutron star merger event GW170817 marked the dawn of Multi-Messenger Astronomy (MMA) with Gravitational Waves (GWs). Such multi-messenger events are of immense scientific interest due to the wealth of information they provide through joint observations across different messengers. In this rapidly evolving field, prompt identification and timely distribution of alerts is critical for follow-up observations.

This work offers a comprehensive overview of the LIGO-Virgo-KAGRA (LVK) Collaboration’s low-latency analysis pipeline for GW events, covering key stages from calibration and data analysis to the issuance of public alerts. I examine the latency and accuracy of each stage in the pipeline, emphasizing the performance of current data products. Furthermore, I address challenges in identifying compelling astrophysical merger candidates—such as electromagnetically bright mergers, mass-gap objects, and sub-solar mass objects—using machine learning techniques within the constraints of low-latency frameworks. Lastly, I present novel machine learning methods designed to provide reliable bounds on source parameters of merging binaries, highlighting their potential for real-time parameter estimation and their role in expanding data products to offer richer astrophysical insights to the astronomy community.

Advisor(s)

Cavaglia, Marco

Committee Member(s)

Fischer, Daniel
Ghosh, Shaon
Saito, Shun, 1982-
Vojta, Thomas

Department(s)

Physics

Degree Name

Ph. D. in Physics

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2025

Pagination

xv, 181 ages

Note about bibliography

Includes_bibliographical_references_(pages 157-177)

Rights

© 2025 Sushant Sharma Chaudhary , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12520

Download

Included in

Physics Commons

Share

 
COinS