Halo concentration strengthens dark matter constraints in galaxy-galaxy strong lensing analyses
Nicola C. Amorisco, James Nightingale, Qiuhan He, Aristeidis, Amvrosiadis, Xiaoyue Cao, Shaun Cole, Amy Etherington, Carlos S. Frenk, Ran, Li, Richard Massey, Andrew Robertson

TL;DR
This paper demonstrates that considering halo concentration significantly enhances the ability of galaxy-galaxy strong lensing analyses to constrain dark matter models, especially in distinguishing warm from cold dark matter.
Contribution
It reveals that high-concentration haloes are more detectable and that accounting for concentration scatter improves constraints on dark matter particle mass.
Findings
Detectability of haloes depends on their position relative to the lens.
Higher concentration haloes are more easily detected in lensing.
Including concentration scatter increases detection estimates by up to ten times.
Abstract
A defining prediction of the cold dark matter (CDM) 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 halos 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 halos 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
