Stochastic gravitational wave background mapmaking using regularised deconvolution

Abstract

Obtaining a faithful source intensity distribution map of the sky from noisy data demands incorporating known information of the expected signal, especially when the signal is weak compared to the noise. We introduce a widely used procedure to incorporate these priors through a Bayesian regularisation scheme in the context of map-making of the anisotropic stochastic GW background (SGWB). Specifically, we implement the quadratic form of regularizing function with varying strength of regularization and study its effect on image restoration for different types of the injected source intensity distribution in simulated LIGO data. We find that regularization significantly enhances the quality of reconstruction, especially when the intensity of the source is weak, and dramatically improves the stability of deconvolution. We further study the quality of reconstruction as a function of regularization constant. While in principle this constant is dependent on the data set, we show that the deconvolution process is robust against the choice of the constant, as long as it is chosen from a broad range of values obtained by the method presented here.

14 pages, 8 figures and 1 table

References in corpus (4)

• Gravitational wave radiometry: Mapping a stochastic gravitational wave background
• Rank deficiency and Tikhonov regularization in the inverse problem for gravitational-wave bursts
• Robust Bayesian detection of unmodelled bursts
• All-sky, narrowband, gravitational-wave radiometry with folded data

Cited by in corpus (11)

• Unified mapmaking for an anisotropic stochastic gravitational wave background
• Targeted search for the stochastic gravitational-wave background from the galactic millisecond pulsar population
• Upper limits on persistent gravitational waves using folded data and the full covariance matrix from Advanced LIGO${}^{\prime }$s first two observing runs
• Measurement of the Cross-Correlation Angular Power Spectrum Between the Stochastic Gravitational Wave Background and Galaxy Over-Density
• Jointly setting upper limits on multiple components of an anisotropic stochastic gravitational-wave background
• Angular power spectra of anisotropic stochastic gravitational wave background: developing statistical methods and analyzing data from ground-based detectors
• Untargeted Bayesian search of anisotropic gravitational-wave backgrounds through the analytical marginalization of the posterior
• Model-independent search for anisotropies in stochastic gravitational-wave backgrounds and application to LIGO-Virgo's first three observing Runs
• Unbiased estimation of gravitational-wave anisotropies from noisy data
• Efficient Search for Detection Candidates Using a Peak Finder Strategy for All-Sky-All-Frequency Gravitational Wave Radiometer
• On Validating Angular Power Spectral Models for the Stochastic Gravitational-Wave Background Without Distributional Assumptions