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Villanueva-Domingo, P., Villaescusa-Navarro, F., Angles-Alcazar, D., Genel, S., Marinacci, F., Spergel, D. N., et al. (2022). Inferring Halo Masses with Graph Neural Networks. Astrophys. J., 935(1), 30–15pp.
Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).
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Jordan, D., Tain, J. L., Algora, A., Agramunt, J., Domingo-Pardo, C., Gomez-Hornillos, M. B., et al. (2013). Measurement of the neutron background at the Canfranc Underground Laboratory LSC. Astropart Phys., 42, 1–6.
Abstract: The energy distribution of the neutron background was measured for the first time at Hall A of the Canfranc Underground Laboratory. For this purpose we used a novel approach based on the combination of the information obtained with six large high-pressure He-3 proportional counters embedded in individual polyethylene blocks of different size. In this way not only the integral value but also the flux distribution as a function of neutron energy was determined in the range from 1 eV to 10 MeV. This information is of importance because different underground experiments show different neutron background energy dependence. The high sensitivity of the setup allowed to measure a neutron flux level which is about four orders of magnitude smaller that the neutron background at sea level. The integral value obtained is Phi(Hall A) = (3.44 +/- 0.35) x 10(-6) cm(-2) s(-1).
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Aguilar, J. A., Bigongiari, C., Calvo Diaz-Aldagalan, D., Emanuele, U., Gomez-Gonzalez, J. P., et al. (2013). Detection potential of the KM3NeT detector for high-energy neutrinos from the Fermi bubbles. Astropart Phys., 42, 7–14.
Abstract: A recent analysis of the Fermi Large Area Telescope data provided evidence for a high-intensity emission of high-energy gamma rays with a E-2 spectrum from two large areas, spanning 50 above and below the Galactic centre (the “Fermi bubbles”). A hadronic mechanism was proposed for this gamma-ray emission making the Fermi bubbles promising source candidates of high-energy neutrino emission. In this work Monte Carlo simulations regarding the detectability of high-energy neutrinos from the Fermi bubbles with the future multi-km(3) neutrino telescope KM3NeT in the Mediterranean Sea are presented. Under the hypothesis that the gamma-ray emission is completely due to hadronic processes, the results indicate that neutrinos from the bubbles could be discovered in about one year of operation, for a neutrino spectrum with a cutoff at 100 TeV and a detector with about 6 km(3) of instrumented volume. The effect of a possible lower cutoff is also considered.
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