# Fingerprint matching of beyond-WIMP dark matter: neural network approach

## TL;DR

This paper introduces a neural network approach to analyze and compare beyond-WIMP dark matter models by capturing complex suppression patterns in galactic structure formation.

## Contribution

It proposes using neural networks to effectively characterize and communicate the suppression features of various beyond-WIMP dark matter models.

## Key findings

- Neural networks can model complex suppression shapes in matter power spectra.
- The approach facilitates comparison across different beyond-WIMP models.
- Demonstrated on a simplified light feebly interacting massive particles model.

## Abstract

Galactic-scale structure is of particular interest since it provides important clues to dark matter properties and its observation is improving. Weakly interacting massive particles (WIMPs) behave as cold dark matter on galactic scales, while beyond-WIMP candidates suppress galactic-scale structure formation. Suppression in the linear matter power spectrum has been conventionally characterized by a single parameter, the thermal warm dark matter mass. On the other hand, the shape of suppression depends on the underlying mechanism. It is necessary to introduce multiple parameters to cover a wide range of beyond-WIMP models. Once multiple parameters are introduced, it becomes harder to share results from one side to the other. In this work, we propose adopting neural network technique to facilitate the communication between the two sides. To demonstrate how to work out in a concrete manner, we consider a simplified model of light feebly interacting massive particles.