PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters

PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters

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 processe...

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Autors principals:	Abhimanyu Susobhanan, David L. Kaplan, Anne M. Archibald, Jing Luo, Paul S. Ray, Timothy T. Pennucci, Scott M. Ransom, Gabriella Agazie, William Fiore, Bjorn Larsen, Patrick O’Neill, Rutger van Haasteren, Akash Anumarlapudi, Matteo Bachetti, Deven Bhakta, Chloe A. Champagne, H. Thankful Cromartie, Paul B. Demorest, Ross J. Jennings, Matthew Kerr, Sasha Levina, Alexander McEwen, Brent J. Shapiro-Albert, Joseph K. Swiggum
Format:	Article
Idioma:	English
Publicat:	IOP Publishing 2024-01-01
Col·lecció:	The Astrophysical Journal
Matèries:	Pulsars Astronomy software Astronomy data analysis
Accés en línia:	https://doi.org/10.3847/1538-4357/ad59f7

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