Transdimensional Bayesian approach to pulsar timing noise analysis

Abstract

The modeling of intrinsic noise in pulsar timing residual data is of crucial importance for gravitational wave 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 variations, and low-frequency spin noise. However, as pulsar timing data continue to improve, nonstationary and non-power-law noise terms are beginning to manifest which are not well modeled by current noise analysis techniques. In this work, we use a transdimensional approach to model these nonstationary and non-power-law 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 nonstationary and non-power-law noise is present. We also show that these methods return results consistent with the standard analyses when no such signals are present.