The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification Academic Article uri icon


  • 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 3.5percent, with a classification efficiency of 97.799.5percent. 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 M prior of 0.3110.010, we show that biases on w are >0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10percent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenets criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.0150.03). 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 10times smaller than the statistical uncertainties for the DES-SN sample.

published proceedings


altmetric score

  • 15.83

author list (cited authors)

  • Vincenzi, M., Sullivan, M., Moeller, A., Armstrong, P., Bassett, B. A., Brout, D., ... Wilkinson, R. D.

citation count

  • 8

complete list of authors

  • Vincenzi, M||Sullivan, M||Moeller, 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, A Carnero||Carretero, J||Castander, FJ||Costanzi, M||da Costa, LN||Pereira, MES||De Vicente, J||Desai, S||Diehl, HT||Doel, P||Everett, S||Ferrero, I||Flaugher, B||Fosalba, P||Frieman, J||Garcia-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-Chinchon, F||Pieres, A||Malagon, AA Plazas||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

publication date

  • November 2023