PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters
Abhimanyu Susobhanan, D. L. Kaplan, Anne M. Archibald, Jing Luo, Paul S. Ray, Timothy T. Pennucci, S. M. Ransom, Gabriella Agazie, William Fiore, Bjorn Larsen, Patrick M. O'Neill, Rutger van Haasteren, Akash Anumarlapudi, Matteo Bachetti, Deven Bhakta, Chloe A. Champagne, H. Thankful Cromartie, Paul B. Demorest, Ross J. Jennings, M. Kerr, Sasha Levina, Alexander McEwen, Brent J. Shapiro-Albert, Joseph K. Swiggum
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.