The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
Creators: name: Vincenzi, M.
name: Sullivan, M.
name: Möller, A.
name: Armstrong, P.
name: Bassett, B. A.
name: Brout, D.
name: Carollo, D.
name: Carr, A.
name: Davis, T. M.
name: Frohmaier, C.
name: Galbany, L.
name: Glazebrook, K.
name: Graur, O.
name: Kelsey, L.
name: Kessler, R.
name: Kovacs, E.
name: Lewis, G. F.
name: Lidman, C.
name: Malik, U.
name: Nichol, R. C.
name: Popovic, B.
name: Sako, M.
name: Scolnic, D.
name: Smith, M.
name: Taylor, G.
name: Tucker, B. E.
name: Wiseman, P.
name: Aguena, M.
name: Allam, S.
name: Annis, J.
name: Asorey, J.
name: Bacon, D.
name: Bertin, E.
name: Brooks, D.
name: Burke, D. L.
name: Rosell, A. Carnero
name: Carretero, J.
name: Castander, F. J.
name: Costanzi, M.
name: da Costa, L. N.
name: Pereira, M. E. S.
name: De Vicente, J.
name: Desai, S.
name: Diehl, H. T.
name: Doel, P.
name: Everett, S.
name: Ferrero, I.
name: Flaugher, B.
name: Fosalba, P.
name: Frieman, J.
name: García-Bellido, J.
name: Gerdes, D. W.
name: Gruen, D.
name: Gutierrez, G.
name: Hinton, S. R.
name: Hollowood, D. L.
name: Honscheid, K.
name: James, D. J.
name: Kuehn, K.
name: Kuropatkin, N.
name: Lahav, O.
name: Li, T. S.
name: Lima, M.
name: Maia, M. A. G.
name: Marshall, J. L.
name: Miquel, R.
name: Morgan, R.
name: Ogando, R. L. C.
name: Palmese, A.
name: Paz-Chinchón, F.
name: Pieres, A.
name: Malagón, A. A. Plazas
name: Reil, K.
name: Roodman, A.
name: Sanchez, E.
name: Schubnell, M.
name: Serrano, S.
name: Sevilla-Noarbe, I.
name: Suchyta, E.
name: Tarle, G.
name: To, C.
name: Varga, T. N.
name: Weller, J.
name: Wilkinson, R. D.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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 on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-year SN sample. As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. Depending on the choice of non-Ia SN models in both the simulated data sample and training sample, contamination ranges from 0.8-3.5 %, with the efficiency of the classification from 97.7-99.5 %. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension 'BEAMS with Bias Correction' (BBC), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects and we use it to constrain the dark energy equation-of-state, $w$. Assuming a flat universe with Gaussian $Ω_M$ prior of $0.311\pm0.010$, we show that biases on $w$ are $&lt;0.008$ when using SuperNNova and accounting for a wide range of non-Ia SN models in the simulations. Systematic uncertainties associated with contamination are estimated to be at most $σ_{w, \mathrm{syst}}=0.004$. This compares to an expected statistical uncertainty of $σ_{w,\mathrm{stat}}=0.039$ for the DES-SN sample, thus showing that contamination is not a limiting uncertainty in our analysis. We also measure biases due to contamination on $w_0$ and $w_a$ (assuming a flat universe), and find these to be $&lt;$0.009 in $w_0$ and $&lt;$0.108 in $w_a$, hence 5 to 10 times smaller than the statistical uncertainties expected from the DES-SN sample.