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
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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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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Möller, A.
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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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Abbott, T.M.C.
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Aguena, M.
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Allam, S.
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Alves, O.
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Andrade-Oliveira, F.
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Annis, J.
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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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19 December 2024
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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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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Möller, A.
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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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Abbott, T.M.C.
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Aguena, M.
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Allam, S.
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Alves, O.
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Andrade-Oliveira, F.
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Annis, J.
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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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