A neural likelihood ratio estimator trained on simulated strong lensing images can infer the abundance and mass slope of dark matter subhalos from an ensemble of lenses.
Nonlinear Evolution of Cosmological Structures in Warm Dark Matter Models
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abstract
The dark energy dominated warm dark matter (WDM) model is a promising alternative cosmological scenario. We explore large-scale structure formation in this paradigm. We do this in two different ways: with the halo model approach and with the help of an ensemble of high resolution N-body simulations. Combining these quasi-independent approaches, leads to a physical understanding of the important processes which shape the formation of structures. We take a detailed look at the halo mass function, the concentrations and the linear halo bias of WDM. In all cases we find interesting deviations with respect to CDM. In particular, the concentration-mass relation displays a turnover for group scale dark matter haloes, for the case of WDM particles with masses of the order ~0.25 keV. This may be interpreted as a hint for top-down structure formation on small scales. We implement our results into the halo model and find much better agreement with simulations. On small scales the WDM halo model now performs as well as its CDM counterpart.
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astro-ph.CO 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
A neural likelihood ratio estimator trained on simulated strong lensing images can infer the abundance and mass slope of dark matter subhalos from an ensemble of lenses.