Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning

Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning

We conducted an in-depth exploration of the use of different machine learning (ML) for regression algorithms, including Linear, Ridge, LASSO, Bayesian Ridge, Decision Tree, and a variety of Deep Neural Network (DNN) architectures, to estimate the slope of the high-frequency feature (HFF), a prominen...

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Bibliographic Details
Main Authors: Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
gravitational waves
machine learning
high-frequency feature
core-collapse supernovae
parameter estimation
Online Access:https://www.mdpi.com/2076-3417/15/1/65
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