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...

全面介紹

書目詳細資料

Principais autores:	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
格式:	Artigo
語言:	Inglês
出版:	IOP Publishing 2024-01-01
叢編:	The Astrophysical Journal
主題:	Pulsars Astronomy software Astronomy data analysis
在線閱讀:	https://doi.org/10.3847/1538-4357/ad59f7
標簽:	添加標簽 沒有標簽, 成為第一個標記此記錄!