Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.

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: One of the targets of future cosmic microwave background (CMB) and baryon acoustic oscillation measurements is to improve the current accuracy in the neutrino sector and reach a much better sensitivity on extra dark radiation in the early Universe. In this paper, we study how these improvements can be translated into constraining power for well-motivated extensions of the standard model of elementary particles that involve axions thermalized before the quantum chromodynamics (QCD) phase transition by scatterings with gluons. Assuming a fiducial Lambda cold dark matter cosmological model, we simulate future data for Stage-IV CMB-like and Dark Energy Spectroscopic Instrument (DESI)-like surveys and analyse a mixed scenario of axion and neutrino hot dark matter. We further account also for the effects of these QCD axions on the light element abundances predicted by big bang nucleosynthesis. The most constraining forecasted limits on the hot relic masses are m(a) less than or similar to 0.92 eV and n-ary sumation m(nu) less than or similar to 0.12 eV at 95 per cent Confidence Level, showing that future cosmic observations can substantially improve the current bounds, supporting multimessenger analyses of axion, neutrino, and primordial light element properties.

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: The origin of neutrino masses and the nature of dark matter are two in most pressing open questions in modern astro-particle physics. We consider here the possibility that these two problems are related, and review some theoretical scenarios which offer common solutions. A simple possibility is that the dark matter particle emerges in minimal realizations of the seesaw mechanism, as in the majoron and sterile neutrino scenarios. We present the theoretical motivation for both models and discuss their phenomenology, confronting the predictions of these scenarios with cosmological and astrophysical observations. Finally, we discuss the possibility that the stability of dark matter originates from a flavor symmetry of the leptonic sector. We review a proposal based on an A(4) flavor symmetry.