Multibaseline gravitational wave radiometry

TL;DR

This paper introduces a new optimal detection statistic for stochastic gravitational wave backgrounds using a network of multiple detectors, improving sensitivity and parameter estimation without deconvolution errors.

Contribution

It develops the maximized-likelihood ratio (MLR) statistic for multi-baseline radiometry, enabling quantitative comparison of baseline sensitivities and improved detection of weak signals.

Findings

MLR statistic is optimal in Gaussian noise.

Network of baselines enhances measurement accuracy.

Method is applicable to real data.

Abstract

We present a statistic for the detection of stochastic gravitational wave backgrounds (SGWBs) using radiometry with a network of multiple baselines. We also quantitatively compare the sensitivities of existing baselines and their network to SGWBs. We assess how the measurement accuracy of signal parameters, e.g., the sky position of a localized source, can improve when using a network of baselines, as compared to any of the single participating baselines. The search statistic itself is derived from the likelihood ratio of the cross correlation of the data across all possible baselines in a detector network and is optimal in Gaussian noise. Specifically, it is the likelihood ratio maximized over the strength of the SGWB, and is called the maximized-likelihood ratio (MLR). One of the main advantages of using the MLR over past search strategies for inferring the presence or absence of a signal is that the former does not require the deconvolution of the cross correlation statistic. Therefore, it does not suffer from errors inherent to the deconvolution procedure and is especially useful for detecting weak sources. In the limit of a single baseline, it reduces to the detection statistic studied by Ballmer [Class. Quant. Grav. 23, S179 (2006)] and Mitra et al. [Phys. Rev. D 77, 042002 (2008)]. Unlike past studies, here the MLR statistic enables us to compare quantitatively the performances of a variety of baselines searching for a SGWB signal in (simulated) data. Although we use simulated noise and SGWB signals for making these comparisons, our method can be straightforwardly applied on real data.