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Your search keyword '"Irsic, V [0000-0002-5445-461X]"' showing total 2 results
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2 results on '"Irsic, V [0000-0002-5445-461X]"'

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. Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly α Systems

Author

Wang, B, Zou, J, Cai, Z, Prochaska, JX, Sun, Z, Ding, J, Font-Ribera, A, Gonzalez, A, Herrera-Alcantar, HK, Irsic, V, Lin, X, Brooks, D, Chabanier, S, Belsunce, RD, Palanque-Delabrouille, N, Tarle, G, Zhou, Z, Wang, B [0000-0003-4877-1659], Zou, J [0000-0001-9189-0368], Cai, Z [0000-0001-8467-6478], Prochaska, JX [0000-0002-7738-6875], Sun, Z [0000-0002-8246-7792], Ding, J [0000-0003-4651-8510], Herrera-Alcantar, HK [0000-0002-9136-9609], Irsic, V [0000-0002-5445-461X], Lin, X [0000-0001-6052-4234], Brooks, D [0000-0002-8458-5047], Chabanier, S [0000-0002-5692-5243], Palanque-Delabrouille, N [0000-0003-3188-784X], Tarle, G [0000-0003-1704-0781], and Apollo - University of Cambridge Repository

Subjects
5101 Astronomical Sciences, 51 Physical Sciences
Abstract

We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel−1. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.

Published
2022
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