PINT: Maximum-likelihood estimation of pulsar timing noise parameters
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929391762669568 |
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| author | Susobhanan, Abhimanyu Kaplan, David Archibald, Anne Luo, Jing Ray, Paul Pennucci, Timothy Ransom, Scott Agazie, Gabriella Fiore, William Larsen, Bjorn O'Neill, Patrick van Haasteren, Rutger Anumarlapudi, Akash Bachetti, Matteo Bhakta, Deven Champagne, Chloe Cromartie, H. Thankful Demorest, Paul Jennings, Ross Kerr, Matthew Levina, Sasha McEwen, Alexander Shapiro-Albert, Brent Swiggum, Joseph |
| author_facet | Susobhanan, Abhimanyu Kaplan, David Archibald, Anne Luo, Jing Ray, Paul Pennucci, Timothy Ransom, Scott Agazie, Gabriella Fiore, William Larsen, Bjorn O'Neill, Patrick van Haasteren, Rutger Anumarlapudi, Akash Bachetti, Matteo Bhakta, Deven Champagne, Chloe Cromartie, H. Thankful Demorest, Paul Jennings, Ross Kerr, Matthew Levina, Sasha McEwen, Alexander Shapiro-Albert, Brent Swiggum, Joseph |
| contents | PINT is a pure-Python framework for high-precision pulsar timing developed on top of widely used and well-tested Python libraries, supporting both interactive and programmatic data analysis workflows. We present a new frequentist framework within PINT to characterize the single-pulsar noise processes present in pulsar timing datasets. This framework enables the parameter estimation for both uncorrelated and correlated noise processes as well as the model comparison between different timing and noise models in a computationally inexpensive way. We demonstrate the efficacy of the new framework by applying it to simulated datasets as well as a real dataset of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PINT: Maximum-likelihood estimation of pulsar timing noise parameters Susobhanan, Abhimanyu Kaplan, David Archibald, Anne Luo, Jing Ray, Paul Pennucci, Timothy Ransom, Scott Agazie, Gabriella Fiore, William Larsen, Bjorn O'Neill, Patrick van Haasteren, Rutger Anumarlapudi, Akash Bachetti, Matteo Bhakta, Deven Champagne, Chloe Cromartie, H. Thankful Demorest, Paul Jennings, Ross Kerr, Matthew Levina, Sasha McEwen, Alexander Shapiro-Albert, Brent Swiggum, Joseph Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena PINT is a pure-Python framework for high-precision pulsar timing developed on top of widely used and well-tested Python libraries, supporting both interactive and programmatic data analysis workflows. We present a new frequentist framework within PINT to characterize the single-pulsar noise processes present in pulsar timing datasets. This framework enables the parameter estimation for both uncorrelated and correlated noise processes as well as the model comparison between different timing and noise models in a computationally inexpensive way. We demonstrate the efficacy of the new framework by applying it to simulated datasets as well as a real dataset of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature. |
| title | PINT: Maximum-likelihood estimation of pulsar timing noise parameters |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2405.01977 |