Document Type
Article
Publication Date
9-17-2024
Abstract
In the existing body of literature, numerous waveforms of core collapse supernovae (CCSN) have emerged from extensive simulations conducted in high-performance computing facilities globally. These waveforms exhibit distinct characteristics related to their explosion mechanisms, influenced by parameters such as progenitor mass, angular momentum, gravitational wave energy, peak frequency, duration, and equation of state. Core collapse supernovae stand out as highly anticipated sources in LIGO’s fourth observation (O4) run, prompting dedicated efforts to detect them. The integration of machine learning, specifically convolutional neural networks (CNN), has become a pivotal avenue for analysis. This study addresses a fundamental query: how can a CNN be comprehensively trained to capture all potential CCSN signatures, optimizing accuracy? The investigation presents a multivariate classification of the entire supernova waveform landscape to strategically select training waveforms that maximize the feature space. Rigorously tested on both known and unknown waveforms, the method achieves a classification accuracy of ≥90%. This approach has been seamlessly incorporated into the multilayer signal enhancement with coherent wave burst and CNN (MuLaSEcC) analysis pipeline, showcasing promising outcomes using LIGO O3b data. Noteworthy improvements include a reduction in background by ≥99%, along with the calculation of detection efficiencies for ten contemporary explosion models. The paper evaluates the search pipeline’s performance by illustrating detection probability as a function of false alarm rate and false alarm probability. The results highlight a ≥50% detection efficiency within an SNR range of 20–35 for the ten analyzed models, whether trained or untrained. Time-frequency images of CCSN signals detected by the pipeline show broadband features of the CCSN waveforms that are predicted in the simulations.
Recommended Citation
Faisal, Shahrear K., Gaukhar Nurbek, Michael Benjamin, Bhawana Sedhai, and Soma Mukherjee. 2024. “Search for Core Collapse Supernovae Signals in LIGO’s Third Observation Run Using a Network of Gravitational Wave Detectors Integrated with a Multiclass Convolutional Neural Network.” Physical Review D 110 (6): 064055. https://doi.org/10.1103/PhysRevD.110.064055
Publication Title
Physical Review D
DOI
https://doi.org/10.1103/PhysRevD.110.064055
Comments
© 2024 American Physical Society. Original published version available at https://doi.org/10.1103/PhysRevD.110.064055