Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
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| Formato: | Preprint |
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2024
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| author | Nunes, Solange Escrig, Gabriel Freitas, Osvaldo G. Font, José A. Fernandes, Tiago Onofre, Antonio Torres-Forné, Alejandro |
| author_facet | Nunes, Solange Escrig, Gabriel Freitas, Osvaldo G. Font, José A. Fernandes, Tiago Onofre, Antonio Torres-Forné, Alejandro |
| contents | We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter inference of the maximum (peak) frequency and the GW strain amplitude ($Δh$) multiplied by the luminosity distance ($D$) attained at core bounce, respectively, $(f_{peak})$ and $(D \cdot Δh)$. Our datasets are built from a catalog of numerically generated CCSN waveforms assembled by Richers et al. 2017. Those waveforms are injected into noise from the Advanced Laser Interferometer Gravitational Wave Observatory and Advanced Virgo detectors corresponding to the O2 and O3a observing runs. For a network signal-to-noise ratio (SNR) above 5, our classification network using time series detects Galactic CCSN GW signals buried in detector noise with a false positive rate of 0.10% and a 98% accuracy, being able to detect all signals with SNR>10. The inference of $f_{peak}$ is more accurate than for $D \cdot Δh $, particularly for our datasets with the shortest time window (0.25 s) and for a minimum SNR=15. From the calibration plots of predicted versus true values of the two parameters, the standard deviation ($σ$) and the slope deviation with respect to the ideal value are computed. We find $σ_{D \cdot Δh} = 52.6$ cm and $σ_{f_{peak}} = 18.3$ Hz, with respective slope deviations of 11.6% and 8.3%. Our best model is also tested on waveforms from a recent CCSN catalog built by Mitra et al. 2023, different from the one used for the training. For these new waveforms, the true values of the two parameters are mostly within the $1σ$ band around the network's predicted values. Our results show that DL techniques hold promise to infer physical parameters of Galactic rotational CCSN events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04938 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae Nunes, Solange Escrig, Gabriel Freitas, Osvaldo G. Font, José A. Fernandes, Tiago Onofre, Antonio Torres-Forné, Alejandro High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter inference of the maximum (peak) frequency and the GW strain amplitude ($Δh$) multiplied by the luminosity distance ($D$) attained at core bounce, respectively, $(f_{peak})$ and $(D \cdot Δh)$. Our datasets are built from a catalog of numerically generated CCSN waveforms assembled by Richers et al. 2017. Those waveforms are injected into noise from the Advanced Laser Interferometer Gravitational Wave Observatory and Advanced Virgo detectors corresponding to the O2 and O3a observing runs. For a network signal-to-noise ratio (SNR) above 5, our classification network using time series detects Galactic CCSN GW signals buried in detector noise with a false positive rate of 0.10% and a 98% accuracy, being able to detect all signals with SNR>10. The inference of $f_{peak}$ is more accurate than for $D \cdot Δh $, particularly for our datasets with the shortest time window (0.25 s) and for a minimum SNR=15. From the calibration plots of predicted versus true values of the two parameters, the standard deviation ($σ$) and the slope deviation with respect to the ideal value are computed. We find $σ_{D \cdot Δh} = 52.6$ cm and $σ_{f_{peak}} = 18.3$ Hz, with respective slope deviations of 11.6% and 8.3%. Our best model is also tested on waveforms from a recent CCSN catalog built by Mitra et al. 2023, different from the one used for the training. For these new waveforms, the true values of the two parameters are mostly within the $1σ$ band around the network's predicted values. Our results show that DL techniques hold promise to infer physical parameters of Galactic rotational CCSN events. |
| title | Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae |
| topic | High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2403.04938 |