Theses and Dissertations - UTRGV

Date of Award


Document Type


Degree Name

Master of Science in Interdisciplinary Studies (MSIS)


Science and Technology

First Advisor

Dr. Soma Mukherjee

Second Advisor

Dr. Soumya Mohanty

Third Advisor

Dr. Malik Rakhmanov


A supernova is a star that flares up very suddenly and then slowly returns to its former luminosity or, explodes violently with energy $10^{52}$ erg. There are stars which are 10 times or more massive than the Sun, which usually end their lives going supernova. When there is no longer enough fuel for the fusion process in the core of the star and inward gravitational pull of the star’s great mass takes place, the star starts to explode. A series of nuclear reactions starts taking place after the star begins shrinking due to gravity. In the final phase of this gravitational collapse process, the core temperature rises to over 100 billion degrees, the core compresses and then recoils. Energy of the recoil is transferred to the envelope of the star which then explodes and produces a shock wave. The remaining mass of the original star can form a neutron star, and in case the original star is very massive, a black hole may also form.

In the post-detection era of Gravitational Wave astronomy, core collapse supernovae are an important source of signals for the Advanced LIGO detectors. Several methods have been developed and implemented to search for gravitational wave signals from the core collapse supernovae. One such recent method is based on a multi-stage, high accuracy spectral estimation to effectively achieve higher detection signal to noise ratio. The study has been further enhanced by incorporation of a convolutional neural network to significantly reduce false alarm rates. The combined pipeline is termed Multi-Layer Signal Estimation (MuLaSE) that works in an integrative manner with the coherent wave burst pipeline. This pipeline is termed ”MuLaSECC”. This thesis undertakes an extensive analysis with two families of core collapse supernova waveforms - Kuroda 2017 and the Ott 2013 - corresponding to three-dimensional, general relativistic supernova explosion models. The Kuroda waveforms have been extracted from the models with an approximate neutrino transport for three nonrotating progenitors (11.2, 15, and 40 M$\odot$) using different nuclear equations of state (EOSs). Ott 2013 waveform family is a simulation with three species neutrino leakage scheme created for the post-core-bounce phase of the collapse of a non-rotating star with 27 M$\odot$. The performance of the MuLaSECC method has been evaluated through receiver operating characteristics and the reconstruction of the detected signals. The MuLaSECC is found to have higher efficiency in low false alarm range and an improved reconstruction, especially in the lower frequency domain. This method has also been able to detect signals at low signal to noise ratio that was not detected by the coherent wave burst pipeline in this study.


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