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 datasets. This framework enables the parameter estimation for both uncorrelated and correlated noise processes as well as the 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 datasets as well as a real dataset of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature.
@article{arxiv.2405.01977,
title = {PINT: Maximum-likelihood estimation of pulsar timing noise parameters},
author = {Abhimanyu Susobhanan and David Kaplan and Anne Archibald and Jing Luo and Paul Ray and Timothy Pennucci and Scott Ransom and Gabriella Agazie and William Fiore and Bjorn Larsen and Patrick O'Neill and Rutger van Haasteren and Akash Anumarlapudi and Matteo Bachetti and Deven Bhakta and Chloe Champagne and H. Thankful Cromartie and Paul Demorest and Ross Jennings and Matthew Kerr and Sasha Levina and Alexander McEwen and Brent Shapiro-Albert and Joseph Swiggum},
journal= {arXiv preprint arXiv:2405.01977},
year = {2024}
}