Sushant Sharma Chaudhary, a doctoral candidate in physics, will defend their dissertation titled “The Real-Time Detection Infrastructure of LIGO, Virgo, and KAGRA: Data Products, Current Performance, and Future Developments.” Their advisor, Dr. Marco Cavaglia, is a professor in the physics department.  The dissertation abstract is provided below.

The discovery of the binary neutron star merger event GW170817 marked the dawn of multi-messenger astronomy 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 thesis offers a comprehensive overview of the LIGO-Virgo-KAGRA Collabo-ration’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.