Suchergebnisse

Aguena, M., et al. ; We present a new (2+1)D galaxy cluster finder based on photometric redshifts called Wavelet Z Photometric (WaZP) applied to DES first year (Y1A1) data. The results are compared to clusters detected by the South Pole Telescope (SPT) survey and the redMaPPer cluster finder, the latter based on the same photometric data. WaZP searches for clusters in wavelet-based density maps of galaxies selected in photometric redshift space without any assumption on the cluster galaxy populations. The comparison to other cluster samples was performed with a matching algorithm based on angular proximity and redshift difference of the clusters. It led to the development of a new approach to match two optical cluster samples, following an iterative approach to minimize incorrect associations. The WaZP cluster finder applied to DES Y1A1 galaxy survey (1511.13 deg2 up to mi = 23 mag) led to the detection of 60 547 galaxy clusters with redshifts 0.05 < z < 0.9 and richness Ngals ≥ 5. Considering the overlapping regions and redshift ranges between the DES Y1A1 and SPT cluster surveys, all sz based SPT clusters are recovered by the WaZP sample. The comparison between WaZP and redMaPPer cluster samples showed an excellent overall agreement for clusters with richness Ngals (λ for redMaPPer) greater than 25 (20), with 95 per cent recovery on both directions. Based on the cluster cross-match, we explore the relative fragmentation of the two cluster samples and investigate the possible signatures of unmatched clusters. ; The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2).

Vega-Ferrero, J., et al. ; We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date. ; The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract no. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. ; Peer reviewed

Despite vast improvements in the measurement of the cosmological parameters, the nature of dark energy and an accurate value of the Hubble constant (H-0) in the Hubble-Lemaitre law remain unknown. To break the current impasse, it is necessary to develop as many independent techniques as possible, such as the use of Type II supernovae (SNe II). The goal of this paper is to demonstrate the utility of SNe II for deriving accurate extragalactic distances, which will be an asset for the next generation of telescopes where more-distant SNe II will be discovered. More specifically, we present a sample from the Dark Energy Survey Supernova Program (DES-SN) consisting of 15 SNe II with photometric and spectroscopic information spanning a redshift range up to 0.35. Combining our DES SNe with publicly available samples, and using the standard candle method (SCM), we construct the largest available Hubble diagram with SNe II in the Hubble flow (70 SNe II) and find an observed dispersion of 0.27 mag. We demonstrate that adding a colour term to the SN II standardization does not reduce the scatter in the Hubble diagram. Although SNe II are viable as distance indicators, this work points out important issues for improving their utility as independent extragalactic beacons: find new correlations, define a more standard subclass of SNe II, construct new SN II templates, and dedicate more observing time to high-redshift SNe II. Finally, for the first time, we perform simulations to estimate the redshift-dependent distance-modulus bias due to selection effects. ; National Science Foundation (NSF) AST-1211916 TABASGO Foundation, Gary and Cynthia Bengier Christopher R. Redlich Fund Sylvia and Jim Katzman Foundation Miller Institute for Basic Research in Science (UC Berkeley) - European Union 839090 Spanish grant PGC2018-095317-B-C21 European Union (EU) EU/FP7-ERC grant 615929 National Science Foundation (NSF) Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan Princeton University Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) University of Tokyo High Energy Accelerator Research Organization (KEK) FIRST programme from the Japanese Cabinet Office Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science Japan Science & Technology Agency (JST) Toray Industries, Inc. Institute for Astronomy (the University of Hawaii) Max Planck Society Foundation CELLEX National Central University of Taiwan Space Telescope Science Institute National Aeronautics & Space Administration (NASA) NNX08AR22G National Aeronautics & Space Administration (NASA) National Science Foundation (NSF) AST-1238877 University of Maryland Eotvos Lorand University (ELTE) National Aeronautics & Space Administration (NASA) W.M. Keck Foundation National Research Council of Canada Centre National de la Recherche Scientifique (CNRS) Science & Technology Facilities Council (STFC) National Research Council Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) Australian Research Council National Council for Scientific and Technological Development (CNPq) Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) GN-2005A-Q11 GN-2005B-Q-7 GN-2006A-Q-7 GS-2005A-Q-11 GS-2005BQ-6 GS-2008B-Q-56 United States Department of Energy (DOE) Spanish Government Science & Technology Facilities Council (STFC) Higher Education Funding Council for England National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign Ohio State University Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University Ciencia Tecnologia e Inovacao (FINEP) Fundacao Carlos Chagas Filho de Amparo Conselho Nacional de Desenvolvimento Cient'tronomy at Texas AM University German Research Foundation (DFG) University of Portsmouth OzDES Membership Consortium National Science Foundation (NSF) AST-1138766 AST-1536171 AYA2015-71825 ESP2015-66861 FPA2015-68048 SEV2016-0588 SEV-2016-0597 European Union (EU) European Union - CERCA programme of the Generalitat de Catalunya European Research Council (ERC) European Research Council (ERC) 240672 291329 306478 National Council for Scientific and Technological Development (CNPq) 465376/2014-2 United States Department of Energy (DOE) United States Department of Energy (DOE) DE-AC02-05CH11231 United States Department of Energy (DOE) DE-AC02-05CH11231