Amorisco, Nicola C. and Nightingale, James and He, Qiuhan and Amvrosiadis, Aristeidis and Cao, Xiaoyue and Cole, Shaun and Etherington, Amy and Frenk, Carlos S. and Li, Ran and Massey, Richard and Robertson, Andrew (2022) 'Halo concentration strengthens dark matter constraints in galaxy-galaxy strong lensing analyses.', Monthly Notices of the Royal Astronomical Society, 510 . pp. 2464-2479.
A defining prediction of the cold dark matter cosmological model is the existence of a very large population of low-mass haloes. This population is absent in models in which the dark matter particle is warm (WDM). These alternatives can, in principle, be distinguished observationally because haloes along the line of sight can perturb galaxy–galaxy strong gravitational lenses. Furthermore, the WDM particle mass could be deduced because the cut-off in their halo mass function depends on the mass of the particle. We systematically explore the detectability of low-mass haloes in WDM models by simulating and fitting mock lensed images. Contrary to previous studies, we find that haloes are harder to detect when they are either behind or in front of the lens. Furthermore, we find that the perturbing effect of haloes increases with their concentration: Detectable haloes are systematically high-concentration haloes, and accounting for the scatter in the mass–concentration relation boosts the expected number of detections by as much as an order of magnitude. Haloes have lower concentration for lower particle masses and this further suppresses the number of detectable haloes beyond the reduction arising from the lower halo abundances alone. Taking these effects into account can make lensing constraints on the value of the mass function cut-off at least an order of magnitude more stringent than previously appreciated.
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|Publisher Web site:||https://doi.org/10.1093/mnras/stab3527|
|Publisher statement:||© The Author(s) 2021. Published by Oxford University Press on behalf of Royal Astronomical Society This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Date accepted:||30 November 2021|
|Date deposited:||10 February 2022|
|Date of first online publication:||07 December 2021|
|Date first made open access:||10 February 2022|
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