First cosmology results using type Ia supernovae from the dark energy survey: The effect of host galaxy properties on supernova luminosity

Smith, M and Sullivan, M and Desai, Shantanu and et al, . (2020) First cosmology results using type Ia supernovae from the dark energy survey: The effect of host galaxy properties on supernova luminosity. Monthly Notices of the Royal Astronomical Society, 494 (3). pp. 1-22. ISSN 0035-8711

[img] Text
Monthly_Notices.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB)

Abstract

We present improved photometric measurements for the host galaxies of 206 spectroscopically confirmed type Ia supernovae discovered by the Dark Energy Survey Supernova Program (DES-SN) and used in the first DES-SN cosmological analysis. For the DES-SN sample, when considering a 5D (z, x1, c, α, β) bias correction, we find evidence of a Hubble residual 'mass step', where SNe Ia in high-mass galaxies (>1010M☉) are intrinsically more luminous (after correction) than their low-mass counterparts by γ = 0.040 ± 0.019 mag. This value is larger by 0.031 mag than the value found in the first DES-SN cosmological analysis. This difference is due to a combination of updated photometric measurements and improved star formation histories and is not from host-galaxy misidentification. When using a 1D (redshift-only) bias correction the inferred mass step is larger, with γ = 0.066 ± 0.020 mag. The 1D−5D γ difference for DES-SN is 0.026 ± 0.009 mag. We show that this difference is due to a strong correlation between host galaxy stellar mass and the x1 component of the 5D distance-bias correction. Including an intrinsic correlation between the observed properties of SNe Ia, stretch and colour, and stellar mass in simulated SN Ia samples, we show that a 5D fit recovers γ with −9 mmag bias compared to a +2 mmag bias for a 1D fit. This difference can explain part of the discrepancy seen in the data. Improvements in modelling correlations between galaxy properties and SN is necessary to ensure unbiased precision estimates of the dark energy equation of state as we enter the era of LSST. © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

[error in script]
IITH Creators:
IITH CreatorsORCiD
Desai, Shantanuhttp://orcid.org/0000-0002-0466-3288
Item Type: Article
Additional Information: We acknowledge support from EU/FP7-ERC grant no. 615929. LG was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 839090. The UCSC team is supported in part by NASA grant no. NNG17PX03C, NSF grant nos AST-1518052 and AST-1815935, the Gordon & Betty Moore Foundation, the Heising-Simons Foundation, and by fellowships from the Alfred P. Sloan Foundation and the David and Lucile Packard Foundation to RJF. This work was completed in part with resources provided by the University of Chicago Research Computing Center. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. This study is based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant nos AST-1138766 and AST-1536171.
Uncontrolled Keywords: Cosmology: observations; Distance scale; Supernovae: general; Surveys; Transients: supernovae
Subjects: Physics
Divisions: Department of Physics
Depositing User: . LibTrainee 2021
Date Deposited: 05 Nov 2022 08:55
Last Modified: 05 Nov 2022 08:55
URI: http://raiithold.iith.ac.in/id/eprint/11168
Publisher URL: https://doi.org/10.1093/mnras/staa946
OA policy: https://v2.sherpa.ac.uk/id/publication/24618
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 11168 Statistics for this ePrint Item