A Trans-dimensional 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 (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.

11 pages, 11 figures, plan to submit to PRD

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Cited by in corpus (11)

• The NANOGrav 11-year Data Set: High-precision timing of 45 Millisecond Pulsars
• Detection methods for stochastic gravitational-wave backgrounds: a unified treatment
• The BayesWave analysis pipeline in the era of gravitational wave observations
• Frequency-Dependent Template Profiles for High Precision Pulsar Timing
• The NANOGrav 11-Year Data Set: Evolution of Gravitational Wave Background Statistics
• Model Dependence of Bayesian Gravitational-Wave Background Statistics for Pulsar Timing Arrays
• Exponential shapelets: basis functions for data analysis of isolated features
• Bayesian search for gravitational wave bursts in pulsar timing array data
• Use Model Averaging instead of Model Selection in Pulsar Timing
• The NANOGrav 15 yr Data Set: Customized Chromatic Noise Models
• Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array