ABSTRACT Self-interacting dark matter (SIDM) cosmologies admit an enormous diversity of dark matter (DM) halo density profiles, from low-density cores to high-density core-collapsed cusps. The possibility of the growth of high central density in low-mass haloes, accelerated if haloes are subhaloes of larger systems, has intriguing consequences for small-halo searches with substructure lensing. However, following the evolution of ${\lesssim}10^8 \, \mathrm{M}_\odot$ subhaloes in lens-mass systems (${\sim}10^{13}\, \mathrm{M}_\odot$) is computationally expensive with traditional N-body simulations. In this work, we develop a new hybrid semi-analytical + N-body method to study the evolution of SIDM subhaloes with high fidelity, from core formation to core-collapse, in staged simulations. Our method works best for small subhaloes (≲1/1000 host mass), for which the error caused by dynamical friction is minimal. We are able to capture the evaporation of subhalo particles by interactions with host halo particles, an effect that has not yet been fully explored in the context of subhalo core-collapse. We find three main processes drive subhalo evolution: subhalo internal heat outflow, host-subhalo evaporation, and tidal effects. The subhalo central density grows only when the heat outflow outweighs the energy gain from evaporation and tidal heating. Thus, evaporation delays or even disrupts subhalo core-collapse. Wemore »
Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Strong gravitational lensing has emerged as a promising approach for probing dark matter (DM) models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhaloes through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhaloes. We demonstrate that our method is capable of harnessing the statistical power of multiple subhaloes (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of DM perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.
- Award ID(s):
- 2019786
- Publication Date:
- NSF-PAR ID:
- 10378167
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 517
- Issue:
- 3
- Page Range or eLocation-ID:
- p. 4317-4326
- ISSN:
- 0035-8711
- Publisher:
- Oxford University Press
- Sponsoring Org:
- National Science Foundation
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