Noiseless Gravitational Lensing Simulations

TL;DR

This paper introduces Recursive-TCM, a novel method for producing low-noise gravitational lensing predictions from N-body simulations, enabling better analysis of dark matter substructures.

Contribution

The paper presents Recursive-TCM, a new technique that reduces discreteness noise in lensing simulations without free parameters, improving the accuracy of substructure predictions.

Findings

Recursive-TCM significantly reduces noise compared to traditional methods.

Application to DM halos reveals differences in substructure between warm and cold DM.

Method enhances the interpretability of gravitational lensing observations.

Abstract

The microphysical properties of the DM particle can, in principle, be constrained by the properties and abundance of substructures in DM halos, as measured through strong gravitational lensing. Unfortunately, there is a lack of accurate theoretical predictions for the lensing signal of substructures, mainly because of the discreteness noise inherent to N-body simulations. Here we present Recursive-TCM, a method that is able to provide lensing predictions with an arbitrarily low discreteness noise, without any free parameters or smoothing scale. This solution is based on a novel way of interpreting the results of N-body simulations, where particles simply trace the evolution and distortion of Lagrangian phase-space volume elements. We discuss the advantages of this method over the widely used cloud-in-cells and adaptive-kernel smoothing density estimators. Applying the new method to a cluster-sized DM halo simulated in warm and cold DM scenarios, we show how the expected differences in their substructure population translate into differences in the convergence and magnification maps. We anticipate that our method will provide the high-precision theoretical predictions required to interpret and fully exploit strong gravitational lensing observations.