The NANOGrav 12.5-year Data Set: Chromatic Noise Characterization & Mitigation with Time-Domain Kernels

TL;DR

This paper introduces a new time-domain Gaussian process modeling approach for chromatic noise in pulsar timing arrays, offering effective mitigation and computational advantages over traditional Fourier-domain methods, enhancing gravitational wave background detection.

Contribution

The paper presents a novel class of time-domain kernel models for chromatic noise in PTA data, enabling Bayesian model selection and improved noise mitigation strategies.

Findings

Time-domain kernels effectively mitigate chromatic noise.

Models are computationally efficient for high-frequency PTA data.

Bayesian model selection guides optimal kernel choice.

Abstract

Pulsar timing arrays (PTAs) have recently entered the detection era, quickly moving beyond the goal of simply improving sensitivity at the lowest frequencies for the sake of observing the stochastic gravitational wave background (GWB), and focusing on its accurate spectral characterization. While all PTA collaborations around the world use Fourier-domain Gaussian processes to model the GWB and intrinsic long time-correlated (red) noise, techniques to model the time-correlated radio frequency-dependent (chromatic) processes have varied from collaboration to collaboration. Here we test a new class of models for PTA data, Gaussian processes based on time-domain kernels that model the statistics of the chromatic processes starting from the covariance matrix. As we will show, these models can be effectively equivalent to Fourier-domain models in mitigating chromatic noise. This work presents a method for Bayesian model selection across the various choices of kernel as well as deterministic chromatic models for non-stationary chromatic events and the solar wind. As PTAs turn towards high frequency (>1/yr) sensitivity, the size of the basis used to model these processes will need to increase, and these time-domain models present some computational efficiencies compared to Fourier-domain models.