PINT: Maximum-likelihood estimation of pulsar timing noise parameters

TL;DR

PINT introduces a new maximum-likelihood framework for estimating pulsar timing noise parameters, enabling efficient analysis of both simulated and real datasets within a Python environment.

Contribution

It provides a novel frequentist approach for noise characterization in pulsar timing, integrated into the PINT Python framework, with improved model comparison capabilities.

Findings

Effective noise parameter estimation demonstrated on simulated data.

Successful application to real pulsar dataset PSR B1855+09.

Enhanced features in PINT for pulsar timing analysis.

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 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.