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

Abstract

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 per cent. 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 ΩM 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 per cent 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 w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 03, 2022
Source ID
10.1093/mnras/stac1404

Entities

People

  • (des Collaboration)
  • A Carnero Rosell
  • A Carr
  • A Palmese
  • A Pieres
  • A Roodman
  • Anais Möller
  • Andrés A. Plazas Malagón
  • B A Bassett
  • B E Tucker
  • B Flaugher
  • B. Popovic
  • C To
  • Chris Lidman
  • Christopher Frohmaier
  • D Carollo
  • D L Hollowood
  • D W Gerdes
  • D. Bacon
  • D. J. James
  • D. L. Burke
  • D. Scolnic
  • Daniel Gruen
  • Daniel I. Brooks
  • Dillon Brout
  • E Bertin
  • E. Kovacs
  • E. R. Sánchez
  • Eric Suchyta
  • F J Castander
  • F Paz-chinchón
  • G Tarlé
  • G. Gutierrez
  • G. Taylor
  • Geraint F. Lewis
  • H T Diehl
  • I Sevilla-noarbe
  • Ismael Ferrero
  • J Annis
  • J Asorey
  • J De Vicente
  • J Frieman
  • J. L. Marshall
  • Jorge Carretero Palacios
  • Juan García-Bellido
  • Juliane Weller
  • K Glazebrook
  • K Honscheid
  • K Kuehn
  • K Reil
  • L Kelsey
  • L N Da Costa
  • Lluís Galbany
  • M A G Maia
  • M E S Pereira
  • M Schubnell
  • M. Sako
  • M. Vincenzi
  • Manuela Lima
  • Mark Sullivan
  • Mathew Smith
  • Matteo Costanzi
  • Michel Aguena
  • Nikolay Kuropatkin
  • O Lahav
  • Or Graur
  • P Doel
  • P Fosalba
  • P. Armstrong
  • Philip Wiseman
  • R C Nichol
  • R D Wilkinson
  • R L C Ogando
  • R Miquel
  • R. Kessler
  • Robert W. Morgan
  • S R Hinton
  • S Serrano
  • S. Allam
  • S. Everett
  • Shruti Desai
  • T N Varga
  • T. S. Li
  • Tamara M. Davis
  • U Malik

Organizations

  • African Institute for Mathematical Sciences
  • American Museum of Natural History
  • Arctic Sciences
  • Argonne National Laboratory
  • Australian National University
  • Autonomous University of Madrid
  • Barcelona Institute for Science and Technology
  • Carnegie Institution for Science
  • Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
  • Clermont Auvergne University
  • Duke University
  • European Research Council
  • Fermilab
  • Harvard–Smithsonian Center for Astrophysics
  • Indian Institute of Technology Hyderabad
  • Institute for Fundamental Physics of the Universe
  • Institute of Space Studies of Catalonia
  • Lowell Observatory
  • Ludwig-Maximilians-Universität München
  • Max Planck Institute for Extraterrestrial Physics
  • Ministry of Science of Spain
  • Ministry of Science, Innovation and Universities
  • National Aeronautics and Space Administration
  • National Center for Supercomputing Applications
  • National Council for Scientific and Technological Development
  • National Institute for Astrophysics
  • National Science Foundation
  • Oak Ridge National Laboratory
  • Ohio State University
  • Princeton University
  • SLAC National Accelerator Laboratory
  • Santa Cruz Institute for Particle Physics
  • Science and Technology Facilities Council
  • Sorbonne Universités
  • South African Astronomical Observatory
  • Spanish National Research Council
  • Stanford University
  • Swinburne University of Technology
  • Texas A&M University
  • United States Department of Energy
  • University College London
  • University of California
  • University of Cambridge
  • University of Cape Town
  • University of Chicago
  • University of Hamburg
  • University of Michigan
  • University of Oslo
  • University of Pennsylvania
  • University of Portsmouth
  • University of Queensland
  • University of Southampton
  • University of Sussex
  • University of Sydney
  • University of São Paulo
  • University of Trieste
  • University of Wisconsin–Madison

Tags

Readers

  • Astronomy/Astrophysics
  • Radar Systems Engineering.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference