Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908626090721280 |
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| author | Villa, Eleonora Shaifullah, Golam Mohiuddin Possenti, Andrea Carbone, Carmelita |
| author_facet | Villa, Eleonora Shaifullah, Golam Mohiuddin Possenti, Andrea Carbone, Carmelita |
| contents | We present a detailed study of Bayesian inference workflows for pulsar timing array data with a focus on enhancing efficiency, robustness and speed through the use of normalizing flow-based nested sampling. Building on the Enterprise framework, we integrate the i-nessai sampler and benchmark its performance on realistic, simulated datasets. We analyze its computational scaling and stability, and show that it achieves accurate posteriors and reliable evidence estimates with substantially reduced runtime, by up to three orders of magnitude depending on the dataset configuration, with respect to conventional single-core parallel-tempering MCMC analyses. These results highlight the potential of flow-based nested sampling to accelerate PTA analyses while preserving the quality of the inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows Villa, Eleonora Shaifullah, Golam Mohiuddin Possenti, Andrea Carbone, Carmelita Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Machine Learning We present a detailed study of Bayesian inference workflows for pulsar timing array data with a focus on enhancing efficiency, robustness and speed through the use of normalizing flow-based nested sampling. Building on the Enterprise framework, we integrate the i-nessai sampler and benchmark its performance on realistic, simulated datasets. We analyze its computational scaling and stability, and show that it achieves accurate posteriors and reliable evidence estimates with substantially reduced runtime, by up to three orders of magnitude depending on the dataset configuration, with respect to conventional single-core parallel-tempering MCMC analyses. These results highlight the potential of flow-based nested sampling to accelerate PTA analyses while preserving the quality of the inference. |
| title | Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Machine Learning |
| url | https://arxiv.org/abs/2511.01958 |