The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification

Journal Article (Academic article)

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 $\Omega_M$ prior of $0.311\pm0.010$, we show that biases on $w$ are $<0.008$ 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 $\sigma_{w, \mathrm{syst}}=0.004$. This compares to an expected statistical uncertainty of $\sigma_{w,\mathrm{stat}}=0.039$ 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 $<$0.009 in $w_0$ and $<$0.108 in $w_a$, hence 5 to 10 times smaller than the statistical uncertainties expected from the DES-SN sample.

Full Text

Duke Authors

Cited Authors

  • Vincenzi, M; Sullivan, M; Möller, A; Armstrong, P; Bassett, BA; Brout, D; Carollo, D; Carr, A; Davis, TM; Frohmaier, C; Galbany, L; Glazebrook, K; Graur, O; Kelsey, L; Kessler, R; Kovacs, E; Lewis, GF; Lidman, C; Malik, U; Nichol, RC; Popovic, B; Sako, M; Scolnic, D; Smith, M; Taylor, G; Tucker, BE; Wiseman, P; Aguena, M; Allam, S; Annis, J; Asorey, J; Bacon, D; Bertin, E; Brooks, D; Burke, DL; Rosell, AC; Carretero, J; Castander, FJ; Costanzi, M; Costa, LND; Pereira, MES; Vicente, JD; Desai, S; Diehl, HT; Doel, P; Everett, S; Ferrero, I; Flaugher, B; Fosalba, P; Frieman, J; García-Bellido, J; Gerdes, DW; Gruen, D; Gutierrez, G; Hinton, SR; Hollowood, DL; Honscheid, K; James, DJ; Kuehn, K; Kuropatkin, N; Lahav, O; Li, TS; Lima, M; Maia, MAG; Marshall, JL; Miquel, R; Morgan, R; Ogando, RLC; Palmese, A; Paz-Chinchón, F; Pieres, A; Malagón, AAP; Reil, K; Roodman, A; Sanchez, E; Schubnell, M; Serrano, S; Sevilla-Noarbe, I; Suchyta, E; Tarle, G; To, C; Varga, TN; Weller, J; Wilkinson, RD

Published Date

  • December 10, 2021