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Abstract Over the past few decades, the measurement precision of some pulsar timing experiments has advanced from ∼10 μ s to ∼10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT ( P INT I s N ot T empo3 ), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on the Python Package Index (PyPI) as pint-pulsar . PINT is well tested, validated, object oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the N um P y and A stropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., Tempo / Tempo2 ) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, use, and validation of PINT , and we compare timing results between it and Tempo and Tempo2 .
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PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters
Abstract PINTis 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 withinPINTto 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 inPINTsince it was first described in the literature.
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- Award ID(s):
- 2020265
- PAR ID:
- 10533242
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 971
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 150
- Size(s):
- Article No. 150
- Sponsoring Org:
- National Science Foundation
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