Using strong lensing to detect subhaloes with steep inner density profiles

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Authors: Kassidy E. Kollmann, J.W Nightingale, Mariangela Lisanti, Andrew Robertson, Oren Slone
Cited by: 2
References: 118

Abstract

ABSTRACT The inner region of a subhalo’s density distribution is particularly sensitive to dark matter microphysics, with alternative dark matter models leading to both cored and steeply-rising inner density profiles. This work investigates how the lensing signature and detectability of dark matter subhaloes in mock HST-, Euclid-, and JWST-like strong lensing observations depend on the subhalo’s radial density profile, especially with regards to the inner power-law slope, $\beta$. We demonstrate that the minimum subhalo mass detectable along the Einstein ring of a system is strongly dependent on $\beta$. In particular, we show that subhaloes with $\beta = 2.2$ can be detected down to masses over an order-of-magnitude lower than their Navarro–Frenk–White (NFW) counterparts with $\beta = 1$. Importantly, we find that the detectability of subhaloes with steep inner profiles is minimally affected by increasing the complexity of the main lens galaxy’s mass model. This is a notable characteristic of these subhaloes, as those with NFW or shallower profiles become essentially undetectable when multipole perturbations are added to the lens model. The results of this work highlight how the underlying dark matter physics can significantly impact the expected number of subhalo detections from strong gravitational lensing observations. This is important for testing Cold Dark Matter against alternative models, such as Self-Interacting Dark Matter, that predict a diverse range of subhalo inner density profiles.

Fields of Study & Subjects

Dark Matter and Cosmic Phenomena Galaxies: Formation Evolution Phenomena Astronomy and Astrophysical Research

Publication details

Journal / source
Monthly Notices of the Royal Astronomical Society
Publisher
Oxford University Press (OUP)
Volume
546
Issue
4
Publication date
13 January 2026
Language
EN