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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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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., 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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Mena, O., Razzaque, S., & Villaescusa-Navarro, F. (2011). Signatures of photon and axion-like particle mixing in the gamma-ray burst jet. J. Cosmol. Astropart. Phys., 02(2), 030–16pp.
Abstract: Photons couple to Axion-Like Particles (ALPs) or more generally to any pseudo Nambu-Goldstone boson in the presence of an external electromagnetic field. Mixing between photons and ALPs in the strong magnetic field of a Gamma-Ray Burst (GRB) jet during the prompt emission phase can leave observable imprints on the gamma-ray polarization and spectrum. Mixing in the intergalactic medium is not expected to modify these signatures for ALP mass > 10(-14) eV and/or for < nG magnetic field. We show that the depletion of photons due to conversion to ALPs changes the linear degree of polarization from the values predicted by the synchrotron model of gamma ray emission. We also show that when the magnetic field orientation in the propagation region is perpendicular to the field orientation in the production region, the observed synchrotron spectrum becomes steeper than the theoretical prediction and as detected in a sizable fraction of GRB sample. Detection of the correlated polarization and spectral signatures from these steep-spectrum GRBs by gamma-ray polarimeters can be a very powerful probe to discover ALPs. Measurement of gamma-ray polarization from GRBs in general, with high statistics, can also be useful to search for ALPs.
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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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