PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters

Abstract 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 data sets. This framework enables parameter estimation for both uncorrelated and correlated noise processes, as well as 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 data sets as well as a real data set of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature.

Authors

Publication Details

Journal
The Astrophysical Journal
Published
2024-08-01
DOI
https://doi.org/10.3847/1538-4357/ad59f7
Citations
16
Primary Topic
GNSS positioning and interference
Type
article
Field-Weighted Citation Impact
20.78

Funders

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article

PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters

Bjorn Larsen, H. Thankful Cromartie, Alexander McEwen, Chloe A. Champagne et al.
16 citations
The Astrophysical Journal
GNSS positioning and interference
20.78
article

PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters

Bjorn Larsen, H. Thankful Cromartie, Alexander McEwen, Chloe A. Champagne, Deven Bhakta, S. M. Ransom, Akash Anumarlapudi, David L. Kaplan, Anne M. Archibald, William Fiore, Abhimanyu Susobhanan, Patrick M. O'Neill, Brent J. Shapiro-Albert, Timothy T. Pennucci, Rutger van Haasteren, Ross J. Jennings, Paul B. Demorest, Matthew T. Kerr, Paul S. Ray, Matteo Bachetti, Jing Luo, Gabriella Agazie, Joseph K. Swiggum, Sasha Levina
article en
16 citations

Abstract

Abstract 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 data sets. This framework enables parameter estimation for both uncorrelated and correlated noise processes, as well as 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 data sets as well as a real data set of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature.

The Astrophysical JournalVol. 971(2)
National Science Foundation, Office of Naval Research
Openalex Percentile: Top 1%
GNSS positioning and interference
20.78
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