Nonparametric estimation of galaxy cluster's emissivity and point source detection in astrophysics with two lasso penalties

TL;DR

This paper introduces a novel nonparametric method using dual lasso penalties for improved 3D gas emissivity estimation and point source detection in galaxy clusters from 2D telescope images, outperforming current techniques.

Contribution

It proposes a new Poisson GLM approach with two lasso penalties and universal thresholds, enhancing accuracy over existing two-step methods.

Findings

Method outperforms current techniques in simulations.

Effective sparse wavelet and point source recovery.

Improved accuracy in galaxy cluster emissivity estimation.

Abstract

Astrophysicists are interested in recovering the 3D gas emissivity of a galaxy cluster from a 2D image taken by a telescope. A blurring phenomenon and presence of point sources make this inverse problem even harder to solve. The current state-of-the-art technique is two step: first identify the location of potential point sources, then mask these locations and deproject the data. We instead model the data as a Poisson generalized linear model (involving blurring, Abel and wavelets operators) regularized by two lasso penalties to induce sparse wavelet representation and sparse point sources. The amount of sparsity is controlled by two quantile universal thresholds. As a result, our method outperforms the existing one.