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Serenelli, A. M., Haxton, W. C., & Pena-Garay, C. (2011). Solar Models With Accretion. I. Application To The Solar Abundance Problem. Astrophys. J., 743(1), 24–20pp.
Abstract: We generate new standard solar models using newly analyzed nuclear fusion cross sections and present results for helioseismic quantities and solar neutrino fluxes. The status of the solar abundance problem is discussed. We investigate whether nonstandard solar models with accretion from the protoplanetary disk might alleviate this problem. We examine a broad range of models, analyzing metal-enriched and metal-depleted accretion and three scenarios for the timing of accretion. Only partial solutions are found. Formetal-rich accreted material (Z(ac) greater than or similar to 0.018) there exist combinations of accreted mass and metallicity that bring the depth of the convective zone into agreement with the helioseismic value. For the surface helium abundance, the helioseismic value is reproduced if metal-poor or metal-free accretion is assumed (Z(ac) less than or similar to 0.09). In both cases a few percent of the solar mass must be accreted. Precise values depend on when accretion takes place. We do not find a simultaneous solution to both problems but speculate that changing the hydrogen-to-helium mass ratio in the accreted material may lead to more satisfactory solutions. We also show that, with current data, solar neutrinos are already a very competitive source of information about the solar core and can help constraining possible accretion histories. Even without helioseismic constraints, solar neutrinos rule out the possibility that more than 0.02 M(circle dot) from the protoplanetary disk were accreted after the Sun settled on the main sequence. Finally, we discuss how measurements of neutrinos from the CN cycle could shed light on the interaction between the early Sun and its protoplanetary disk.
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Trotta, R., Johannesson, G., Moskalenko, I. V., Porter, T. A., Ruiz de Austri, R., & Strong, A. W. (2011). Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis. Astrophys. J., 729(2), 106–16pp.
Abstract: Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.
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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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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2023). The CAMELS Project: Public Data Release. Astrophys. J. Suppl. Ser., 265(2), 54–14pp.
Abstract: The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lya spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at .
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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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