A Bayesian parameter estimation approach to pulsar time-of-arrival analysis

TL;DR

This paper introduces Bayesian methods to improve pulsar time-of-arrival measurements by accurately estimating uncertainties, enhancing gravitational wave detection capabilities with pulsar timing arrays.

Contribution

The paper presents novel Bayesian techniques for pulsar TOA estimation from search-mode and pre-folded data, focusing on uncertainty quantification in period determination.

Findings

Bayesian methods produce posterior distributions for TOAs.

Application to simulated data demonstrates improved uncertainty estimates.

Methods are applicable to real pulsar timing data.

Abstract

The increasing sensitivities of pulsar timing arrays to ultra-low frequency (nHz) gravitational waves promises to achieve direct gravitational wave detection within the next 5-10 years. While there are many parallel efforts being made in the improvement of telescope sensitivity, the detection of stable millisecond pulsars and the improvement of the timing software, there are reasons to believe that the methods used to accurately determine the time-of-arrival (TOA) of pulses from radio pulsars can be improved upon. More specifically, the determination of the uncertainties on these TOAs, which strongly affect the ability to detect GWs through pulsar timing, may be unreliable. We propose two Bayesian methods for the generation of pulsar TOAs starting from pulsar "search-mode" data and pre-folded data. These methods are applied to simulated toy-model examples and in this initial work we focus on the issue of uncertainties in the folding period. The final results of our analysis are expressed in the form of posterior probability distributions on the signal parameters (including the TOA) from a single observation.