A Trans-dimensional Bayesian Approach to Pulsar Timing Noise Analysis

TL;DR

This paper introduces a trans-dimensional Bayesian framework using wavelets and adaptive spectral modeling to better characterize complex, non-stationary, and non-powerlaw noise in pulsar timing data, improving gravitational wave detection accuracy.

Contribution

It presents a novel trans-dimensional Bayesian approach that models non-stationary and non-powerlaw noise in pulsar timing residuals, outperforming standard methods.

Findings

Outperforms standard techniques in modeling complex noise.

Returns consistent results when no non-stationary noise is present.

Effectively captures non-powerlaw noise components.

Abstract

The modeling of intrinsic noise in pulsar timing residual data is of crucial importance for Gravitational Wave (GW) detection and pulsar timing (astro)physics in general. The noise budget in pulsars is a collection of several well studied effects including radiometer noise, pulse-phase jitter noise, dispersion measure (DM) variations, and low frequency spin noise. However, as pulsar timing data continues to improve, non-stationary and non-powerlaw noise terms are beginning to manifest which are not well modeled by current noise analysis techniques. In this work we use a trans-dimensional approach to model these non-stationary and non-powerlaw effects through the use of a wavelet basis and an interpolation based adaptive spectral modeling. In both cases, the number of wavelets and the number of control points in the interpolated spectrum are free parameters that are constrained by the data and then marginalized over in the final inferences, thus fully incorporating our ignorance of the noise model. We show that these new methods outperform standard techniques when non-stationary and non-powerlaw noise is present. We also show that these methods return results consistent with the standard analyses when no such signals are present.