# Stochastic gravitational wave background mapmaking using regularised deconvolution

## TL;DR

This paper presents a Bayesian regularisation approach for improving the accuracy and stability of reconstructing the stochastic gravitational wave background map from noisy LIGO data, especially for weak signals.

## Contribution

It introduces a regularised deconvolution method with a quadratic prior, demonstrating robustness and improved image quality in simulated data.

## Key findings

- Regularization significantly improves reconstruction quality.
- The method enhances stability of deconvolution for weak signals.
- Reconstruction quality is robust to the choice of regularization constant.

## 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.