Probing Supernovae through gravitational wave entropy
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911259122728960 |
|---|---|
| author | Sakan, Aknur Ussipov, Nurzhan Abdikamalov, Ernazar Akhmetali, Almat Zaidyn, Marat Zhunuskanov, Alisher Font, José A. Edwards, Matthew C. Abylkairov, Sultan |
| author_facet | Sakan, Aknur Ussipov, Nurzhan Abdikamalov, Ernazar Akhmetali, Almat Zaidyn, Marat Zhunuskanov, Alisher Font, José A. Edwards, Matthew C. Abylkairov, Sultan |
| contents | We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we compute four entropy measures -- Shannon, exponential, Rényi, and Tsallis -- and apply three feature selection methods to identify the most informative features. We then train machine-learning classifiers on these features to compare the performance of different methodological combinations. We find that the combination of Rényi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probing Supernovae through gravitational wave entropy Sakan, Aknur Ussipov, Nurzhan Abdikamalov, Ernazar Akhmetali, Almat Zaidyn, Marat Zhunuskanov, Alisher Font, José A. Edwards, Matthew C. Abylkairov, Sultan High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we compute four entropy measures -- Shannon, exponential, Rényi, and Tsallis -- and apply three feature selection methods to identify the most informative features. We then train machine-learning classifiers on these features to compare the performance of different methodological combinations. We find that the combination of Rényi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals. |
| title | Probing Supernovae through gravitational wave entropy |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2511.08010 |