Reconstruction of the dark matter density profile from cosmic positron anomaly data

TL;DR

This paper develops an algorithm to determine the dark matter distribution in space that could explain the cosmic positron anomaly, demonstrating the potential for dark matter models to account for observed data.

Contribution

It introduces a novel inverse problem approach with an algorithm to find dark matter distributions fitting positron anomaly data, expanding previous assumptions.

Findings

A unique algorithm successfully finds dark matter distributions fitting the data

A class of solutions consistent with observational positron data is identified

The method demonstrates the feasibility of explaining the positron anomaly through dark matter distribution variations

Abstract

In this work we continue our investigations of the possibility of explanation of the positron anomaly (PA) in cosmic rays with the help of annihilating or decaying dark matter (DM) component by varying its space distribution. In the contrast of our previous studies, where we first assumed some specific spatial distribution of DM component and looked at how it agrees with data, here we solve, in some sense, the inverse problem: we search for distribution, in a mathematical way, which satisfies observational data. A unique algorithm has been implemented which, using linear algebra and adaptive grid methods, adjusts distribution to the data. It allows telling in principle whether or not is possible to solve PA problem by variation of spatial distribution of DM sources. A positive result has been formally obtained. A class of solutions can be identified. Though the distributions obtained at the chosen injection spectra may seem slightly realistic, nonetheless it demonstrates a quite powerful possibility in explaining PA that could be realized in more realistic models.