Abstract: We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris-TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass similar to 10(11)-10(13)M(circle dot) from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below approximate to 0.25 when using photometrical and spectroscopic information. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations.

Abstract: A prediction of the standard Lambda cold dark matter cosmology is that dark matter (DM) haloes are teeming with numerous self-bound substructure or subhaloes. The precise properties of these subhaloes represent important probes of the underlying cosmological model. We use data from Via Lactea II and Exploring the Local Volume in Simulations N-body simulations to learn about the structure of subhaloes with masses 10(6)-10(11) h(-1) M circle dot. Thanks to a superb subhalo statistics, we study subhalo properties as a function of distance to host halo centre and subhalo mass, and provide a set of fits that accurately describe the subhalo structure. We also investigate the role of subhaloes on the search for DM annihilation. Previous work has shown that subhaloes are expected to boost the DM signal of their host haloes significantly. Yet, these works traditionally assumed that subhaloes exhibit similar structural properties than those of field haloes, while it is known that subhaloes are more concentrated. Building upon our N-body data analysis, we refine the substructure boost model of Sanchez-Conde & Prada (2014), and find boosts that are a factor 2-3 higher. We further refine the model to include unavoidable tidal stripping effects on the subhalo population. For field haloes, this introduces a moderate (similar to 20-30 per cent) suppression. Yet, for subhaloes like those hosting dwarf galaxy satellites, tidal stripping plays a critical role, the boost being at the level of a few tens of percent at most. We provide a parametrization of the boost for field haloes that can be safely applied over a wide halo mass range.

Abstract: We quantify the impact of massive neutrinos on the statistics of low-density regions in the intergalactic medium as probed by the Lyman alpha forest at redshifts z = 2.2-4. Based on mock but realistic quasar (QSO) spectra extracted from hydrodynamic simulations with cold dark matter, baryons and neutrinos, we find that the probability distribution of weak Lyman alpha absorption features, as sampled by Lyman alpha flux regions at high transmissivity, is strongly affected by the presence of massive neutrinos. We show that systematic errors affecting the Lyman alpha forest reduce but do not erase the neutrino signal. Using the Fisher matrix formalism, we conclude that the sum of the neutrino masses can be measured, using the method proposed in this paper, with a precision smaller than 0.4 eV using a catalogue of 200 high-resolution (signal-to-noise ratio similar to 100) QSO spectra. This number reduces to 0.27 eV by making use of reasonable priors in the other parameters that also affect the statistics of the high-transitivity regions of the Lyman alpha forest. The constraints obtained with this method can be combined with independent bounds from the cosmic microwave background, large-scale structures and measurements of the matter power spectrum from the Lyman alpha forest to produce tighter upper limits on the sum of the masses of the neutrinos.