The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

M. Vincenzi; M. Sullivan; A. Moeller; P. Armstrong; B. A. Bassett; D. Brout; D. Carollo; A. Carr; T. M. Davis; C. Frohmaier; L. Galbany; K. Glazebrook; O. Graur; L. Kelsey; R. Kessler; E. Kovacs; G. F. Lewis; C. Lidman; U. Malik; R. C. Nichol; B. Popovic; M. Sako; D. Scolnic; M. Smith; G. Taylor; B. E. Tucker; P. Wiseman; M. Aguena; S. Allam; J. Annis; J. Asorey; D. Bacon; E. Bertin; D. Brooks; D. L. Burke; A. Carnero Rosell; J. Carretero; F. J. Castander; M. Costanzi; L. N. da Costa; M. E. S. Pereira; J. De Vicente; S. Desai; H. T. Diehl; P. Doel; S. Everett; Ferrero; B. Flaugher; P. Fosalba; J. Frieman; J. Garcia-Bellido; D. W. Gerdes; D. Gruen; G. Gutierrez; S. R. Hinton; D. L. Hollowood; K. Honscheid; D. J. James; K. Kuehn; N. Kuropatkin; O. Lahav; T. S. Li; M. Lima; M. A. G. Maia; J. L. Marshall; R. Miquel; R. Morgan; R. L. C. Ogando; A. Palmese; F. Paz-Chinchon; A. Pieres; A. A. Plazas Malagon; K. Reil; A. Roodman; E. Sanchez; M. Schubnell; S. Serrano; Sevilla-Noarbe; E. Suchyta; G. Tarle; C. To; T. N. Varga; J. Weller; R. D. Wilkinson; DES Collaboration

doi:10.1093/mnras/stac1404

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The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

Journal article

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The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

M. Vincenzi, M. Sullivan, A. Moeller, P. Armstrong, B. A. Bassett, D. Brout, D. Carollo, A. Carr, T. M. Davis, C. Frohmaier, …

Monthly notices of the Royal Astronomical Society, Vol.518(1), pp.1106-1127

01/01/2023

DOI: https://doi.org/10.1093/mnras/stac1404

Abstract

Astronomy & Astrophysics

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Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7-99.5 percent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC ('BEAMS with Bias Correction'), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian Omega(M) prior of 0.311 +/- 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 percent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet's criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015-0.03). Finally, we measure biases due to contamination on w(0) and w(a) (assuming a flat universe), and find these to be <0.009 in w(0) and <0.108 in w(a), 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

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Title: The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification
Creators: M. Vincenzi - University of Southampton
M. Sullivan - University of Southampton
A. Moeller - Swinburne University of Technology
P. Armstrong - Australian National University
B. A. Bassett - African Institute for Mathematical Sciences
D. Brout - Center for Astrophysics Harvard & Smithsonian
D. Carollo - Osservatorio Astrofisico di Torino
A. Carr - University of Queensland
T. M. Davis - University of Queensland
C. Frohmaier - University of Southampton
L. Galbany - Institut d'Estudis Espacials de Catalunya
K. Glazebrook - Swinburne University of Technology
O. Graur - University of Portsmouth
L. Kelsey - University of Portsmouth
R. Kessler - University of Chicago
E. Kovacs - Argonne National Laboratory
G. F. Lewis - University of Sydney
C. Lidman - Australian National University
U. Malik - Australian National University
R. C. Nichol - University of Portsmouth
B. Popovic - Duke University
M. Sako - University of Pennsylvania
D. Scolnic - Duke University
M. Smith - University of Southampton
G. Taylor - Australian National University
B. E. Tucker - Australian National University
P. Wiseman - University of Southampton
M. Aguena - Laboratório Interinstitucional de e-Astronomia
S. Allam - Fermi National Accelerator Laboratory
J. Annis - Fermi National Accelerator Laboratory
J. Asorey - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
D. Bacon - University of Portsmouth
E. Bertin - Institut d'Astrophysique de Paris
D. Brooks - University College London
D. L. Burke - SLAC National Accelerator Laboratory
A. Carnero Rosell - Laboratório Interinstitucional de e-Astronomia
J. Carretero - Institute for High Energy Physics
F. J. Castander - Institut d'Estudis Espacials de Catalunya
M. Costanzi - University of Trieste
L. N. da Costa - Laboratório Interinstitucional de e-Astronomia
M. E. S. Pereira - University of Michigan–Ann Arbor
J. De Vicente - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
S. Desai - Indian Institute of Technology Hyderabad
H. T. Diehl - Fermi National Accelerator Laboratory
P. Doel - University College London
S. Everett - Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA
Ferrero - Inst Theoret Astrophys, POB 1029 Blindern, NO-0315 Oslo, Norway
B. Flaugher - Fermi National Accelerator Laboratory
P. Fosalba - Institute of Space Sciences
J. Frieman - University of Chicago
J. Garcia-Bellido - Autonomous University of Madrid
D. W. Gerdes - University of Michigan–Ann Arbor
D. Gruen - Ludwig-Maximilians-Universität München
G. Gutierrez - Fermi National Accelerator Laboratory
S. R. Hinton - University of Queensland
D. L. Hollowood - Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA
K. Honscheid - The Ohio State University
D. J. James - Center for Astrophysics Harvard & Smithsonian
K. Kuehn - Australian Astronomical Observatory
N. Kuropatkin - Fermi National Accelerator Laboratory
O. Lahav - University College London
T. S. Li - Carnegie Observatories
M. Lima - Universidade de São Paulo
M. A. G. Maia - Laboratório Interinstitucional de e-Astronomia
J. L. Marshall - Mitchell Institute
R. Miquel - Institute for High Energy Physics
R. Morgan - University of Wisconsin–Madison
R. L. C. Ogando - Valongo Observatory
A. Palmese - University of California, Berkeley
F. Paz-Chinchon - University of Cambridge
A. Pieres - Laboratório Interinstitucional de e-Astronomia
A. A. Plazas Malagon - Princeton University
K. Reil - SLAC National Accelerator Laboratory
A. Roodman - Kavli Institute for Particle Astrophysics and Cosmology
E. Sanchez - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
M. Schubnell - University of Michigan–Ann Arbor
S. Serrano - Institut d'Estudis Espacials de Catalunya
Sevilla-Noarbe - Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid 28040, Spain
E. Suchyta - Oak Ridge National Laboratory
G. Tarle - University of Michigan–Ann Arbor
C. To - Kavli Institute for Particle Astrophysics and Cosmology
T. N. Varga - Max Planck Institute for Extraterrestrial Physics
J. Weller - Max Planck Institute for Extraterrestrial Physics
R. D. Wilkinson - University of Sussex
DES Collaboration
Publication Details: Monthly notices of the Royal Astronomical Society, Vol.518(1), pp.1106-1127
Publisher: Oxford Univ Press
Number of pages: 22
Date published: 01/01/2023
Grant note: FL180100168 / ARC; Australian Research Council ST/R000506/1 / STFC; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC) 80GSFC17M0002; NNH15ZDA001N-WFIRST / NASA; National Aeronautics & Space Administration (NASA) PID2020-115253GA-I00 / Spanish MICIU project ST/P006760/1 / Science and Technology Facilities Council through the DISCnet Centre for Doctoral Training RYC2019-027683 / Spanish Ministry of Science, Innovation and Universities (MICIU); Spanish Government DE-FOA-0001781 / DOE; United States Department of Energy (DOE) 615929 / EU/FP7-ERC grant MR/T01881X/1 / UKRI Future Leaders Fellowship; UK Research & Innovation (UKRI)
Identifiers: 99817243402346
Academic Unit: FEPS Central Faculty Admin
Language: English
Resource Type: Journal article

The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

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