Explaining Dark Matter Halo Density Profiles with Neural Networks

被引：10

作者：

Lucie-Smith, Luisa ^{[1
]}

Peiris, Hiranya, V ^{[2
,3
]}

Pontzen, Andrew ^{[2
]}

机构：

[1] Max Planck Inst Astrophys, Karl Schwarzschild Str 1, D-85748 Garching, Germany

[2] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England

[3] Stockholm Univ, AlbaNova, Oskar Klein Ctr Cosmoparticle Phys, S-10691 Stockholm, Sweden

来源：

PHYSICAL REVIEW LETTERS | 2024年 / 132卷 / 03期

基金：

美国国家科学基金会; 欧洲研究理事会;

关键词：

CONCENTRATION-REDSHIFT RELATION; LARGE-SCALE STRUCTURE; ACCRETION HISTORY; GALAXY; COLD; UNIVERSAL; INFORMATION; SIMULATION; COSMOLOGY; EVOLUTION;

D O I：

10.1103/PhysRevLett.132.031001

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a lowdimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network recovers the known relation between the early time assembly and the inner profile and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machineassisted scientific in datasets.

引用

页数：7

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