Bernal, N., Munoz-Albornoz, V., Palomares-Ruiz, S., & Villanueva-Domingo, P. (2022). Current and future neutrino limits on the abundance of primordial black holes. J. Cosmol. Astropart. Phys., 10(10), 068–38pp.
Abstract: Primordial black holes (PBHs) formed in the early Universe are sources of neutrinos emitted via Hawking radiation. Such astrophysical neutrinos could be detected at Earth and constraints on the abundance of comet-mass PBHs could be derived from the null observation of this neutrino flux. Here, we consider non-rotating PBHs and improve constraints using Super-Kamiokande neutrino data, as well as we perform forecasts for next-generation neutrino (Hyper-Kamiokande, JUNO, DUNE) and dark matter (DARWIN, ARGO) detectors, which we compare. For PBHs less massive than " few x 1014 g, PBHs would have already evaporated by now, whereas more massive PBHs would still be present and would constitute a fraction of the dark matter of the Universe. We consider monochromatic and extended (log-normal) mass distributions, and a PBH mass range spanning from 1012 g to ti 1016 g. Finally, we also compare our results with previous ones in the literature.
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Villanueva-Domingo, P., Villaescusa-Navarro, F., Genel, S., Angles-Alcazar, D., Hernquist, L., Marinacci, F., et al. (2023). Weighing the Milky Way and Andromeda galaxies with artificial intelligence. Phys. Rev. D, 107(10), 103003–8pp.
Abstract: We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on 2,000 state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods, within our derived posterior standard deviation.
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Villanueva-Domingo, P., Gnedin, N. Y., & Mena, O. (2018). Warm Dark Matter and Cosmic Reionization. Astrophys. J., 852(2), 139–7pp.
Abstract: In models with dark matter made of particles with keV masses, such as a sterile neutrino, small-scale density perturbations are suppressed, delaying the period at which the lowest mass galaxies are formed and therefore shifting the reionization processes to later epochs. In this study, focusing on Warm Dark Matter (WDM) with masses close to its present lower bound, i.e., around the 3. keV region, we derive constraints from galaxy luminosity functions, the ionization history and the Gunn-Peterson effect. We show that even if star formation efficiency in the simulations is adjusted to match the observed UV galaxy luminosity functions in both CDM and WDM models, the full distribution of Gunn-Peterson optical depth retains the strong signature of delayed reionization in the WDM model. However, until the star formation and stellar feedback model used in modern galaxy formation simulations is constrained better, any conclusions on the nature of dark matter derived from reionization observables remain model-dependent.
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Lopez-Honorez, L., Mena, O., & Villanueva-Domingo, P. (2019). Dark matter microphysics and 21 cm observations. Phys. Rev. D, 99(2), 023522–12pp.
Abstract: Dark matter interactions with massless or very light standard model particles, as photons or neutrinos, may lead to a suppression of the matter power spectrum at small scales and of the number of low mass haloes. Bounds on the dark matter scattering cross section with light degrees of freedom in such interacting dark matter (IDM) scenarios have been obtained from e.g., early time cosmic microwave background physics and large scale structure observations. Here we scrutinize dark matter microphysics in light of the claimed 21 cm EDGES 78 MHz absorption signal. IDM is expected to delay the 21 cm absorption features due to collisional damping effects. We identify the astrophysical conditions under which the existing constraints on the dark matter scattering cross section could be largely improved due to the IDM imprint on the 21 cm signal, providing also an explicit comparison to the WDM scenario.
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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2022). The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence. Astrophys. J. Suppl. Ser., 259(2), 61–14pp.
Abstract: We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span similar to 100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.
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