Date of Award
Master of Science (MS)
Soumya D. Mohanty
Core collapse supernovae (CCSN) are highly anticipated sources of gravitational waves during the fourth observation run (O4). CCSN signals are weak and unmodeled and the rate of occurrence in our galaxy is very low. Because of this, they provide a greater challenge to detect than previously detected GW sources. CCSN simulations are used to test the detection pipeline in the event a CCSN is detected. CCSN GW signals are often indistinguishable from the noise sources present in GW data. We present a multi layered signal enhancement pipeline which we have applied Machine Learning (ML) techniques. We have used a Convolutional Neural Network (CNN) to train on 10 different simulated CCSN signals, and then tested the preformance of our pipeline on rotating CCSN signals.
Benjamin, Michael Gale, "Analyzing the on Source Window of Supernova SN2019EJJ With a Multi Layered Signal Enhancement Algorithm With Coherent Waveburst and a Convolutional Neural Network" (2023). Theses and Dissertations - UTRGV. 1285.