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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., 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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Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2021). Removing Astrophysics in 21 cm Maps with Neural Networks. Astrophys. J., 907(1), 44–14pp.
Abstract: Measuring temperature fluctuations in the 21 cm signal from the epoch of reionization and the cosmic dawn is one of the most promising ways to study the universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a nontrivial manner. We run a suite of 1000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts 10 <= z <= 20. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields not only that resemble the true ones visually but whose statistical properties agree with the true ones within a few percent down to scales 2 Mpc(-1). We demonstrate that our neural network retains astrophysical information that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps the network is using in order to determine the value of the astrophysical parameters.
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Villaescusa-Navarro, F., Vogelsberger, M., Viel, M., & Loeb, A. (2013). Neutrino signatures on the high-transmission regions of the Lyman alpha forest. Mon. Not. Roy. Astron. Soc., 431(4), 3670–3677.
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.
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Villaescusa-Navarro, F., Miralda-Escude, J., Pena-Garay, C., & Quilis, V. (2011). Neutrino halos in clusters of galaxies and their weak lensing signature. J. Cosmol. Astropart. Phys., 06(6), 027–14pp.
Abstract: We study whether non-linear gravitational effects of relic neutrinos on the development of clustering and large-scale structure may be observable by weak gravitational lensing. We compute the density profile of relic massive neutrinos in a spherical model of a cluster of galaxies, for several neutrino mass schemes and cluster masses. Relic neutrinos add a small perturbation to the mass profile, making it more extended in the outer parts. In principle, this non-linear neutrino perturbation is detectable in an all-sky weak lensing survey such as EUCLID by averaging the shear profile of a large fraction of the visible massive clusters in the universe, or from its signature in the general weak lensing power spectrum or its cross-spectrum with galaxies. However, correctly modeling the distribution of mass in baryons and cold dark matter and suppressing any systematic errors to the accuracy required for detecting this neutrino perturbation is severely challenging.
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