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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MDPI AG
2024-12-01
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| author | Alejandro Casallas-Lagos Javier M. Antelis Claudia Moreno Ramiro Franco-Hernández |
| author_facet | Alejandro Casallas-Lagos Javier M. Antelis Claudia Moreno Ramiro Franco-Hernández |
| author_sort | Alejandro Casallas-Lagos |
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| description | 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 prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). We created a data set of CCSN GW signals generated by an analytical model that mimics the characteristics of the signals obtained from numerical simulations, particularly the HFF. This enabled us to simulate a wide range of HFF slope values and analyze their properties. We opted to employ ML for regression techniques, particularly a supervised learning approach, to analyze the data set due to the parameter chosen for estimating the slope of the HFF. This type of architecture is ideal for this purpose as it can detect the connections between input and output data. In addition, it is suitable for handling high-dimensional input data and produces efficient results with low computational cost. We evaluated the efficiency and performance of the ML algorithms using a set of metrics to measure their ability to accurately predict the HFF slope within the data set. The results showed that a DNN algorithm for regression exhibits the highest accuracy in estimating the slope of the HFF. |
| format | Article |
| id | doaj-art-1a37d57828a3437aab40bb1346277a0f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1a37d57828a3437aab40bb1346277a0f2025-01-10T13:14:19ZengMDPI AGApplied Sciences2076-34172024-12-011516510.3390/app15010065Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine LearningAlejandro Casallas-Lagos0Javier M. Antelis1Claudia Moreno2Ramiro Franco-Hernández3Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, MexicoTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, MexicoDepartamento de Física, Universidad de Guadalajara, Guadalajara 44430, MexicoDepartamento de Física, Universidad de Guadalajara, Guadalajara 44430, MexicoWe 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 prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). We created a data set of CCSN GW signals generated by an analytical model that mimics the characteristics of the signals obtained from numerical simulations, particularly the HFF. This enabled us to simulate a wide range of HFF slope values and analyze their properties. We opted to employ ML for regression techniques, particularly a supervised learning approach, to analyze the data set due to the parameter chosen for estimating the slope of the HFF. This type of architecture is ideal for this purpose as it can detect the connections between input and output data. In addition, it is suitable for handling high-dimensional input data and produces efficient results with low computational cost. We evaluated the efficiency and performance of the ML algorithms using a set of metrics to measure their ability to accurately predict the HFF slope within the data set. The results showed that a DNN algorithm for regression exhibits the highest accuracy in estimating the slope of the HFF.https://www.mdpi.com/2076-3417/15/1/65gravitational wavesmachine learninghigh-frequency featurecore-collapse supernovaeparameter estimation |
| spellingShingle | Alejandro Casallas-Lagos Javier M. Antelis Claudia Moreno Ramiro Franco-Hernández Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning Applied Sciences gravitational waves machine learning high-frequency feature core-collapse supernovae parameter estimation |
| title | Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning |
| title_full | Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning |
| title_fullStr | Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning |
| title_full_unstemmed | Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning |
| title_short | Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning |
| title_sort | estimation of the high frequency feature slope in gravitational wave signals from core collapse supernovae using machine learning |
| topic | gravitational waves machine learning high-frequency feature core-collapse supernovae parameter estimation |
| url | https://www.mdpi.com/2076-3417/15/1/65 |
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