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Full-Text Articles in Physics
A Convolutional Neural Network Based Approach To Study The Gravitational Waves From Core-Collapse Supernovae In Ligo's Third Observation Run: Detection Efficiency And Parameter Estimation, Bhawana Sedhai
Theses and Dissertations
Core-Collapse Supernova (CCSN) is one of the most anticipated sources of Gravitational Waves (GW) arriving at the advanced LIGO detectors during the fourth observation run (O4). CCSN are rare, weak and unmodeled having a very low rate of occurrence in our galaxy (estimated 2 per century). Thus, detection of GW from CCSN is a challenging problem. An analysis pipeline used in this study is Multi-Layer Signal Enhancement with cWB and CNN or MuLaSEcC that combines Machine Learning methods with a network of Gravitational Wave detectors to identify and reconstruct signals from core collapse supernovae, while minimizing false alarms through the …
Search For Gravitational Waves From Core Collapse Supernovae In Ligo's Observation Runs Using A Network Of Detectors, Shahrear Khan Faisal
Search For Gravitational Waves From Core Collapse Supernovae In Ligo's Observation Runs Using A Network Of Detectors, Shahrear Khan Faisal
Theses and Dissertations
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 …
Automated Identification Of Lines In Data From Gravitational Wave Detectors, Thomas A. Cruz
Automated Identification Of Lines In Data From Gravitational Wave Detectors, Thomas A. Cruz
Theses and Dissertations
On the frontier of gravitational wave (GW) astronomy, the LIGO detectors record vast quantities of data that need to be analyzed constantly for rare and transient GW signals. A foundational problem in LIGO data analysis is the identification of spectral line features in the Power Spectral Density (PSD) of the data. Such line features correspond to high power terrestrial or instrumental signals that must be removed from the data before any search for GW signals can take place. In this study the method developed aims to automate the extraction of the frequencies and bandwidths of the lines, treated as sharp …