Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)


Interdisciplinary Studies

First Advisor

Dr. Soma Mukherjee

Second Advisor

Dr. Soumya Mohanty

Third Advisor

Dr. Malik Rakhmanov


Core-Collapse Supernova (CCSN) is one of the most anticipated sources of Gravitational Waves (GW) in the fourth observation run (O4) of LIGO and other network of GW detectors. A very low rate of galactic CCSN, coupled with the fact that the CCSN waveforms are unmodeled, make detection of these signals extremely challenging. Mukherjee et. al. have developed a new burst search pipeline, the Multi-Layer Signal Enhancement with cWB and CNN or MuLaSEcC, that integrates a non-parametric signal estimation and Machine Learning. MuLaSEcC operates on GW data from a network of detectors and enhances the detection probability while reducing the false alarm significantly. The aim of this research is to analyze the detection probability of CCSN during O4 and how well the signals may be reconstructed for parameter estimation. CCSN waveforms are generated in supercomputers by the implementation of complex physics. The CCSN GW waveforms used in this analysis correspond to various explosion scenarios. These are Powell and Muller s18, Scheidegger R3E1AC_L, Ott 2013_s27_fheat1d00, Mezzacappa 2020_c15_3D, Morozova 2018_M13_SFHo_multipole, Andresen 2019 s15fr, Kuroda 2016_TM1, Kuroda 2017 s11.2 and Richers 2017 A300w0_50_HSDD2. The study has demonstrated improved result in terms of reduction in the false alarm rate and broadband reconstruction of the detected signals. Efficiency of the pipeline as a function of distance has been seen to be sensitive up to the galactic range. Receiver operating characteristics have been generated to demonstrate the performance of the pipeline in comparison to other standard operating pipelines within the GW community.


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