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Evaluating cosmological biases using photometric redshifts for type Ia supernova cosmology with the dark energy survey supernova program

Evaluating cosmological biases using photometric redshifts for type Ia supernova cosmology with the dark energy survey supernova program
Evaluating cosmological biases using photometric redshifts for type Ia supernova cosmology with the dark energy survey supernova program
Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying
the type of supernova and obtaining reliable redshifts to measure the distance–redshift relation. While obtaining a host-galaxy
spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming
large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of
millions of SNe. Here, we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts
(photo-z) inferred directly from the SN light curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z
estimates. Using the DES 5-yr photometrically classified SN sample, we consider several photo-z algorithms as host-galaxy
photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3 × 2 point analyses. With detailed catalogue-level
simulations of the DES 5-yr sample, we find that the simulated w can be recovered within ±0.02 when using SN+SOMPZ or
DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases inw consistent with simulations within ∼1σ for three of the five photo-z variants. We further evaluate how photo-z systematics
interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays
the foundation for next-generation fully photometric SNe Ia cosmological analyses.
astro-ph.CO
1365-2966
1948-1966
Chen, R.C.
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Scolnic, D.
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Vincenzi, M.
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Rykoff, E.S.
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Kessler, R.
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Popovic, B.
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Armstrong, P.
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Brout, D.
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Davis, T.M.
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Lee, J.
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da Costa, L.N.
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Pereira, M.E.S.
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Diehl, H.T.
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Doel, P.
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Flaugher, B.
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Frieman, J.
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García-Bellido, J.
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Gatti, M.
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Gaztanaga, E.
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Gutierrez, G.
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et al.
DES Collaboration
Chen, R.C.
c09874ee-9efb-49ff-8536-c258eea813ec
Scolnic, D.
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Vincenzi, M.
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Rykoff, E.S.
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Myles, J.
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Kessler, R.
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Popovic, B.
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Sako, M.
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Smith, M.
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Armstrong, P.
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Brout, D.
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Davis, T.M.
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Galbany, L.
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Lee, J.
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Lidman, C.
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Sánchez, B.O.
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Sullivan, M.
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Qu, H.
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Wiseman, P.
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Aguena, M.
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Alves, O.
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Bacon, D.
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Brooks, D.
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Rosell, A. Carnero
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Carretero, J.
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Choi, A.
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Conselice, C.
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da Costa, L.N.
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Pereira, M.E.S.
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Diehl, H.T.
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Doel, P.
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Everett, S.
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Ferrero, I.
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Flaugher, B.
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Frieman, J.
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García-Bellido, J.
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Gatti, M.
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Gaztanaga, E.
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Giannini, G.
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Gruen, D.
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Gruendl, R.A.
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Gutierrez, G.
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Herner, K.
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Hinton, S.R.
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Hollowood, D.L.
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Chen, R.C., Scolnic, D. and Vincenzi, M. , et al. and DES Collaboration (2024) Evaluating cosmological biases using photometric redshifts for type Ia supernova cosmology with the dark energy survey supernova program. Monthly Notices of the Royal Astronomical Society, 536 (2), 1948-1966. (doi:10.1093/mnras/stae2703).

Record type: Article

Abstract

Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying
the type of supernova and obtaining reliable redshifts to measure the distance–redshift relation. While obtaining a host-galaxy
spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming
large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of
millions of SNe. Here, we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts
(photo-z) inferred directly from the SN light curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z
estimates. Using the DES 5-yr photometrically classified SN sample, we consider several photo-z algorithms as host-galaxy
photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3 × 2 point analyses. With detailed catalogue-level
simulations of the DES 5-yr sample, we find that the simulated w can be recovered within ±0.02 when using SN+SOMPZ or
DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases inw consistent with simulations within ∼1σ for three of the five photo-z variants. We further evaluate how photo-z systematics
interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays
the foundation for next-generation fully photometric SNe Ia cosmological analyses.

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Accepted/In Press date: 4 December 2024
e-pub ahead of print date: 9 December 2024
Published date: 19 December 2024
Keywords: astro-ph.CO
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Identifiers

Local EPrints ID: 498122
URI: http://eprints.soton.ac.uk/id/eprint/498122
DOI: doi:10.1093/mnras/stae2703
ISSN: 1365-2966
PURE UUID: 3a1cadc2-9b32-4619-9bb6-74a9e18c14ff
ORCID for M. Sullivan: ORCID iD orcid.org/0000-0001-9053-4820
ORCID for P. Wiseman: ORCID iD orcid.org/0000-0002-3073-1512

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Date deposited: 10 Feb 2025 17:54
Last modified: 22 Aug 2025 02:21

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Contributors

Author: R.C. Chen
Author: D. Scolnic
Author: M. Vincenzi
Author: E.S. Rykoff
Author: J. Myles
Author: R. Kessler
Author: B. Popovic
Author: M. Sako
Author: M. Smith
Author: P. Armstrong
Author: D. Brout
Author: T.M. Davis
Author: L. Galbany
Author: J. Lee
Author: C. Lidman
Author: A. Möller
Author: B.O. Sánchez
Author: M. Sullivan ORCID iD
Author: H. Qu
Author: P. Wiseman ORCID iD
Author: T.M.C. Abbott
Author: M. Aguena
Author: S. Allam
Author: O. Alves
Author: F. Andrade-Oliveira
Author: J. Annis
Author: D. Bacon
Author: D. Brooks
Author: A. Carnero Rosell
Author: J. Carretero
Author: A. Choi
Author: C. Conselice
Author: L.N. da Costa
Author: M.E.S. Pereira
Author: H.T. Diehl
Author: P. Doel
Author: S. Everett
Author: I. Ferrero
Author: B. Flaugher
Author: J. Frieman
Author: J. García-Bellido
Author: M. Gatti
Author: E. Gaztanaga
Author: G. Giannini
Author: D. Gruen
Author: R.A. Gruendl
Author: G. Gutierrez
Author: K. Herner
Author: S.R. Hinton
Author: D.L. Hollowood
Corporate Author: et al.
Corporate Author: DES Collaboration

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