Moline, A., Sanchez-Conde, M. A., Palomares-Ruiz, S., & Prada, F. (2017). Characterization of subhalo structural properties and implications for dark matter annihilation signals. Mon. Not. Roy. Astron. Soc., 466(4), 4974–4990.
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.
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Giare, W., Di Valentino, E., Melchiorri, A., & Mena, O. (2021). New cosmological bounds on hot relics: axions and neutrinos. Mon. Not. Roy. Astron. Soc., 505(2), 2703–2711.
Abstract: Axions, if realized in nature, can be copiously produced in the early universe via thermal processes, contributing to the mass-energy density of thermal hot relics. In light of the most recent cosmological observations, we analyse two different thermal processes within a realistic mixed hot dark matter scenario which includes also massive neutrinos. Considering the axion-gluon thermalization channel, we derive our most constraining bounds on the hot relic masses m(a) < 7.46 eV and Sigma m(nu) < 0.114 eV both at 95 percent CL; while studying the axion-pion scattering, without assuming any specific model for the axion-pion interactions, and remaining in the range of validity of the chiral perturbation theory, our most constraining bounds are improved to m(a) < 0.91 eV and Sigma m(nu) < 0.105 eV, both at 95 percent CL. Interestingly, in both cases, the total neutrino mass lies very close to the inverted neutrino mass ordering prediction. If future terrestrial double beta decay and/or long-baseline neutrino experiments find that the nature mass ordering is the inverted one, this could rule out a wide region in the currently allowed thermal axion window. Our results therefore, strongly support multi messenger searches of axions and neutrino properties, together with joint analyses of their expected sensitivities.
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Giare, W., Renzi, F., Melchiorri, A., Mena, O., & Di Valentino, E. (2022). Cosmological forecasts on thermal axions, relic neutrinos, and light elements. Mon. Not. Roy. Astron. Soc., 511(1), 1373–1382.
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.
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Gammaldi, V., Zaldivar, B., Sanchez-Conde, M. A., & Coronado-Blazquez, J. (2023). A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning. Mon. Not. Roy. Astron. Soc., 520(1), 1348–1361.
Abstract: Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.
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de los Rios, M., Petac, M., Zaldivar, B., Bonaventura, N. R., Calore, F., & Iocco, F. (2023). Determining the dark matter distribution in simulated galaxies with deep learning. Mon. Not. Roy. Astron. Soc., 525(4), 6015–6035.
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.
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