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1. The CAMELS Project: Public Data Release

Author

Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar, Lucia A. Perez, Pablo Villanueva-Domingo, Digvijay Wadekar, Helen Shao, Faizan G. Mohammad, Sultan Hassan, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Andrina Nicola, Leander Thiele, Yongseok Jo, Oliver H. E. Philcox, Benjamin D. Oppenheimer, Megan Tillman, ChangHoon Hahn, Neerav Kaushal, Alice Pisani, Matthew Gebhardt, Ana Maria Delgado, Joyce Caliendo, Christina Kreisch, Kaze W. K. Wong, William R. Coulton, Michael Eickenberg, Gabriele Parimbelli, Yueying Ni, Ulrich P. Steinwandel, Valentina La Torre, Romeel Dave, Nicholas Battaglia, Daisuke Nagai, David N. Spergel, Lars Hernquist, Blakesley Burkhart, Desika Narayanan, Benjamin Wandelt, Rachel S. Somerville, Greg L. Bryan, Matteo Viel, Yin Li, Vid Irsic, Katarina Kraljic, Federico Marinacci, Mark Vogelsberger, Villaescusa-Navarro, F [0000-0002-4816-0455], Genel, S [0000-0002-3185-1540], Anglés-Alcázar, D [0000-0001-5769-4945], Perez, LA [0000-0002-8449-1956], Villanueva-Domingo, P [0000-0002-0936-4279], Wadekar, D [0000-0002-2544-7533], Shao, H [0000-0002-0152-6747], Mohammad, FG [0000-0001-9243-7434], Hassan, S [0000-0002-1050-7572], Moser, E [0000-0003-1593-1505], Lau, ET [0000-0001-8914-8885], Machado Poletti Valle, LF [0000-0002-1948-3562], Thiele, L [0000-0003-2911-9163], Jo, Y [0000-0003-3977-1761], Philcox, OHE [0000-0002-3033-9932], Oppenheimer, BD [0000-0002-3391-2116], Tillman, M [0000-0002-1185-4111], Hahn, CH [0000-0003-1197-0902], Kaushal, N [0000-0003-4786-2348], Pisani, A [0000-0002-6146-4437], Caliendo, J [0000-0001-9719-7177], Kreisch, C [0000-0002-5061-7805], Wong, KWK [0000-0001-8432-7788], Coulton, WR [0000-0002-1297-3673], Parimbelli, G [0000-0002-2539-2472], Steinwandel, UP [0000-0001-8867-5026], Dave, R [0000-0003-2842-9434], Nagai, D [0000-0002-6766-5942], Spergel, DN [0000-0002-5151-0006], Hernquist, L [0000-0001-6950-1629], Burkhart, B [0000-0001-5817-5944], Narayanan, D [0000-0002-7064-4309], Wandelt, B [0000-0002-5854-8269], Somerville, RS [0000-0002-6748-6821], Bryan, GL [0000-0003-2630-9228], Viel, M [0000-0002-2642-5707], Li, Y [0000-0002-0701-1410], Irsic, V [0000-0002-5445-461X], Kraljic, K [0000-0001-6180-0245], Marinacci, F [0000-0003-3816-7028], Vogelsberger, M [0000-0001-8593-7692], Apollo - University of Cambridge Repository, HEP, INSPIRE, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and CAMELS

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Computer Science - Artificial Intelligence, Hydrodynamical simulations, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, [INFO] Computer Science [cs], Machine Learning (cs.LG), Astrostatistics, Settore FIS/05 - Astronomia e Astrofisica, Cosmology, Galaxy formation, [INFO]Computer Science [cs], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics::Galaxy Astrophysics, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, Artificial Intelligence (cs.AI), Space and Planetary Science, [PHYS.PHYS.PHYS-INS-DET] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Astrophysics of Galaxies (astro-ph.GA), [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Instrumentation and Methods for Astrophysics, [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics
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, Lyα 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 https://camels.readthedocs.io., The Astrophysical Journal. Supplement Series, 265 (2), ISSN:1538-4365, ISSN:0067-0049

Published
2023
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2. Fast, Slow, Early, Late: Quenching Massive Galaxies at z ∼ 0.8

Author

Sandro Tacchella, Charlie Conroy, S. M. Faber, Benjamin D. Johnson, Joel Leja, Guillermo Barro, Emily C. Cunningham, Alis J. Deason, Puragra Guhathakurta, Yicheng Guo, Lars Hernquist, David C. Koo, Kevin McKinnon, Constance M. Rockosi, Joshua S. Speagle, Pieter van Dokkum, Hassen M. Yesuf, Tacchella, S [0000-0002-8224-4505], Conroy, C [0000-0002-1590-8551], Johnson, BD [0000-0002-9280-7594], Leja, J [0000-0001-6755-1315], Barro, G [0000-0001-6813-875X], Cunningham, EC [0000-0002-6993-0826], Deason, AJ [0000-0001-6146-2645], Guhathakurta, P [0000-0001-8867-4234], Guo, Y [0000-0003-2775-2002], Hernquist, L [0000-0001-6950-1629], Koo, DC [0000-0003-3385-6799], McKinnon, K [0000-0001-7494-5910], Speagle, JS [0000-0003-2573-9832], Van Dokkum, P [0000-0002-8282-9888], Yesuf, HM [0000-0002-4176-9145], and Apollo - University of Cambridge Repository

Subjects
Space and Planetary Science, High Energy Physics::Lattice, Astrophysics of Galaxies (astro-ph.GA), FOS: Physical sciences, Astrophysics::Solar and Stellar Astrophysics, Astronomy and Astrophysics, 5109 Space Sciences, Astrophysics::Earth and Planetary Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 51 Physical Sciences, Astrophysics - Astrophysics of Galaxies, Astrophysics::Galaxy Astrophysics
Abstract

We investigate the stellar populations for a sample of 161 massive, mainly quiescent galaxies at $\langle z_{\rm obs} \rangle=0.8$ with deep Keck/DEIMOS rest-frame optical spectroscopy (HALO7D survey). With the fully Bayesian framework Prospector, we simultaneously fit the spectroscopic and photometric data with an advanced physical model (including non-parametric star-formation histories, emission lines, variable dust attenuation law, and dust and AGN emission) together with an uncertainty and outlier model. We show that both spectroscopy and photometry are needed to break the dust-age-metallicity degeneracy. We find a large diversity of star-formation histories: although the most massive ($M_{\star}>2\times10^{11}~M_{\odot}$) galaxies formed the earliest (formation redshift of $z_{\rm f}\approx5-10$ with a short star-formation timescale of $��_{\rm SF}\lesssim1~\mathrm{Gyr}$), lower-mass galaxies have a wide range of formation redshifts, leading to only a weak trend of $z_{\rm f}$ with $M_{\star}$. Interestingly, several low-mass galaxies with have formation redshifts of $z_{\rm f}\approx5-8$. Star-forming galaxies evolve about the star-forming main sequence, crossing the ridgeline several times in their past. Quiescent galaxies show a wide range and continuous distribution of quenching timescales ($��_{\rm quench}\approx0-5~\mathrm{Gyr}$) with a median of $\langle��_{\rm quench}\rangle=1.0_{-0.9}^{+0.8}~\mathrm{Gyr}$ and of quenching epochs of $z_{\rm quench}\approx0.8-5.0$ ($\langle z_{\rm quench}\rangle=1.3_{-0.4}^{+0.7}$). This large diversity of quenching timescales and epochs points toward a combination of internal and external quenching mechanisms. In our sample, rejuvenation and "late bloomers" are uncommon. In summary, our analysis supports the "grow & quench" framework and is consistent with a wide and continuously-populated diversity of quenching timescales., 30 pages, 12 figures + appendix, accepted by ApJ

Published
2022

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