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...

Полное описание

Сохранить в:

Библиографические подробности
Главные авторы:	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
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IOP Publishing 2024-01-01
Серии:	The Astrophysical Journal
Предметы:	Pulsars Astronomy software Astronomy data analysis
Online-ссылка:	https://doi.org/10.3847/1538-4357/ad59f7
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters

Схожие документы