The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911355636809728 |
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| author | Demasi, Gabriele Capurri, Giulia Lenti, Massimo Ricciardone, Angelo Patricelli, Barbara Mascioli, Adriano Frattale Piccari, Lorenzo Albuquerque, Saulo Guidi, Gianluca M. Pannarale, Francesco Stratta, Giulia Del Pozzo, Walter |
| author_facet | Demasi, Gabriele Capurri, Giulia Lenti, Massimo Ricciardone, Angelo Patricelli, Barbara Mascioli, Adriano Frattale Piccari, Lorenzo Albuquerque, Saulo Guidi, Gianluca M. Pannarale, Francesco Stratta, Giulia Del Pozzo, Walter |
| contents | Sequential Monte Carlo (SMC) methods have recently been applied to gravitational-wave inference as a powerful alternative to standard sampling techniques, such as Nested Sampling. At the same time, gradient-based Markov Chain Monte Carlo algorithms, most notably the No-U-Turn Sampler (NUTS), provide an efficient way to explore high-dimensional parameter spaces. In this work we present SHARPy, a Bayesian inference framework that combines the parallelism and evidence-estimation capabilities of SMC with the state-of-the-art sampling performance of NUTS. Moreover, SHARPy exploits the local geometric structure of the posterior to further improve efficiency. Built on JAX and accelerated on GPUs, SHARPy performs gravitational-wave inference on binary black-hole events in around ten minutes, yielding posterior samples and Bayesian evidence estimates that are consistent with those obtained through Nested Sampling. This work sets a new milestone in GW inference with likelihood-based methods and paves the way for model comparison tasks to be accomplished in minutes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02336 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference Demasi, Gabriele Capurri, Giulia Lenti, Massimo Ricciardone, Angelo Patricelli, Barbara Mascioli, Adriano Frattale Piccari, Lorenzo Albuquerque, Saulo Guidi, Gianluca M. Pannarale, Francesco Stratta, Giulia Del Pozzo, Walter General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Sequential Monte Carlo (SMC) methods have recently been applied to gravitational-wave inference as a powerful alternative to standard sampling techniques, such as Nested Sampling. At the same time, gradient-based Markov Chain Monte Carlo algorithms, most notably the No-U-Turn Sampler (NUTS), provide an efficient way to explore high-dimensional parameter spaces. In this work we present SHARPy, a Bayesian inference framework that combines the parallelism and evidence-estimation capabilities of SMC with the state-of-the-art sampling performance of NUTS. Moreover, SHARPy exploits the local geometric structure of the posterior to further improve efficiency. Built on JAX and accelerated on GPUs, SHARPy performs gravitational-wave inference on binary black-hole events in around ten minutes, yielding posterior samples and Bayesian evidence estimates that are consistent with those obtained through Nested Sampling. This work sets a new milestone in GW inference with likelihood-based methods and paves the way for model comparison tasks to be accomplished in minutes. |
| title | The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference |
| topic | General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2601.02336 |