Generalized optimal statistic for characterizing multiple correlated signals in pulsar timing arrays

TL;DR

This paper introduces the multiple component optimal statistic (MCOS), a new method for analyzing pulsar timing array data that improves the detection and characterization of multiple correlated signals, including gravitational wave backgrounds.

Contribution

The paper presents MCOS, a novel extension of the optimal statistic that can simultaneously fit multiple spatial correlations in pulsar timing data, reducing false detections.

Findings

MCOS more accurately recovers injected signals in simulations.

MCOS reduces false detection risk of incorrect correlations.

MCOS can recover multiple correlated signals simultaneously.

Abstract

The optimal statistic (OS) is a frequentist estimator for the amplitude and significance of a spatially-correlated signal in pulsar timing array (PTA) data, and it is widely used to search for the gravitational wave background (GWB). However, the OS cannot perfectly distinguish between different spatial correlations. In this paper, we introduce the multiple component optimal statistic (MCOS): a generalization of the OS that allows for multiple correlations to be simultaneously fit to the data. We use simulated data to show that this method more accurately recovers injected spatially correlated signals, and in particular reduces the risk of a false detection of a signal with the wrong spatial correlation. We also demonstrate that this method can be used to recover multiple correlated signals.