The NANOGrav 15-Year Data Set: Detector Characterization and Noise Budget

TL;DR

This paper analyzes the noise characteristics and sensitivity of the NANOGrav 15-year pulsar timing array data, providing a detailed noise model and sensitivity estimates for low-frequency gravitational wave detection.

Contribution

It introduces a comprehensive noise characterization method for PTA data and calculates the global sensitivity to stochastic gravitational wave backgrounds.

Findings

Minimum noise characteristic strain of 7×10⁻¹⁵ at 5 nHz

Sensitivity curves align with previous GW background analyses

Effective noise modeling preserves sensitivity to various GW signals

Abstract

Pulsar timing arrays (PTAs) are galactic-scale gravitational wave detectors. Each individual arm, composed of a millisecond pulsar, a radio telescope, and a kiloparsecs-long path, differs in its properties but, in aggregate, can be used to extract low-frequency gravitational wave (GW) signals. We present a noise and sensitivity analysis to accompany the NANOGrav 15-year data release and associated papers, along with an in-depth introduction to PTA noise models. As a first step in our analysis, we characterize each individual pulsar data set with three types of white noise parameters and two red noise parameters. These parameters, along with the timing model and, particularly, a piecewise-constant model for the time-variable dispersion measure, determine the sensitivity curve over the low-frequency GW band we are searching. We tabulate information for all of the pulsars in this data release and present some representative sensitivity curves. We then combine the individual pulsar sensitivities using a signal-to-noise-ratio statistic to calculate the global sensitivity of the PTA to a stochastic background of GWs, obtaining a minimum noise characteristic strain of $7\times 10^{-15}$ at 5 nHz. A power law-integrated analysis shows rough agreement with the amplitudes recovered in NANOGrav's 15-year GW background analysis. While our phenomenological noise model does not model all known physical effects explicitly, it provides an accurate characterization of the noise in the data while preserving sensitivity to multiple classes of GW signals.