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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2511.12642 |
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| _version_ | 1866914490405093376 |
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| author | Garg, Suyog Lin, Feng-Li Cannon, Kipp |
| author_facet | Garg, Suyog Lin, Feng-Li Cannon, Kipp |
| contents | Upgrades to current gravitational wave detectors for the next observation run and upcoming third-generation observatories, like the Einstein telescope, are expected to have enormous improvements in detection sensitivities and compact object merger event rates. Estimation of source parameters for a wider parameter space that these detectable signals will lie in, will be a computational challenge. Thus, it is imperative to have methods to speed-up the likelihood calculations with theoretical waveform predictions, which can ultimately make the parameter estimation faster and aid in rapid multi-messenger follow-ups. In this work we study auto-encoder models for gravitational waveform generation by adopting the best-performing architecture of Liao & Lin (2021) to approximate aligned-spin SEOBNRv4 inspiral-merger-ringdown waveforms. Our parameter space consists of four parameters, [$m_1$, $m_2$, $χ_1(z)$, $χ_2(z)$]. The masses are uniformly sampled in $[5,75]\,M_{\odot}$ with a mass ratio limit at $10\,M_{\odot}$, while the spins are uniform in $[-0.99,0.99]$. Our model is able to generate $10^3$ waveforms in $\sim 0.1$ second at an average speed of about 50 microsecond per waveform on a GPU. This is about 4 orders of magnitude faster than the native SEOBNRv4 implementation, and 2--3 orders of magnitude faster than existing non-machine-learning accelerated waveform variants. The median mismatch for the generated waveforms in the test dataset is $\sim10^{-2}$, with better performance in a restricted parameter space of $χ_{\rm eff}\in[-0.80,0.80]$. The latent sampling error of our model can be quantified at a median mismatch standard deviation of $4\times10^{-3}$. Although the accuracy of our model does not enable full production-use yet, the model could be useful wherever high-volume of approximate theoretical waveforms are required, for instance, for rapid sky localization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Auto-encoder model for faster generation of effective one-body gravitational waveform approximations Garg, Suyog Lin, Feng-Li Cannon, Kipp General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Machine Learning Upgrades to current gravitational wave detectors for the next observation run and upcoming third-generation observatories, like the Einstein telescope, are expected to have enormous improvements in detection sensitivities and compact object merger event rates. Estimation of source parameters for a wider parameter space that these detectable signals will lie in, will be a computational challenge. Thus, it is imperative to have methods to speed-up the likelihood calculations with theoretical waveform predictions, which can ultimately make the parameter estimation faster and aid in rapid multi-messenger follow-ups. In this work we study auto-encoder models for gravitational waveform generation by adopting the best-performing architecture of Liao & Lin (2021) to approximate aligned-spin SEOBNRv4 inspiral-merger-ringdown waveforms. Our parameter space consists of four parameters, [$m_1$, $m_2$, $χ_1(z)$, $χ_2(z)$]. The masses are uniformly sampled in $[5,75]\,M_{\odot}$ with a mass ratio limit at $10\,M_{\odot}$, while the spins are uniform in $[-0.99,0.99]$. Our model is able to generate $10^3$ waveforms in $\sim 0.1$ second at an average speed of about 50 microsecond per waveform on a GPU. This is about 4 orders of magnitude faster than the native SEOBNRv4 implementation, and 2--3 orders of magnitude faster than existing non-machine-learning accelerated waveform variants. The median mismatch for the generated waveforms in the test dataset is $\sim10^{-2}$, with better performance in a restricted parameter space of $χ_{\rm eff}\in[-0.80,0.80]$. The latent sampling error of our model can be quantified at a median mismatch standard deviation of $4\times10^{-3}$. Although the accuracy of our model does not enable full production-use yet, the model could be useful wherever high-volume of approximate theoretical waveforms are required, for instance, for rapid sky localization. |
| title | Auto-encoder model for faster generation of effective one-body gravitational waveform approximations |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2511.12642 |