The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification. Issue 1 (3rd June 2022)

• Record Type: Journal Article
• Authors: Vincenzi, M; Sullivan, M; Möller, A; Armstrong, P; Bassett, B A; Brout, D; Carollo, D; Carr, A; Davis, T M; Frohmaier, C; Galbany, L; Glazebrook, K; Graur, O; Kelsey, L; Kessler, R; Kovacs, E; Lewis, G F; Lidman, C; Malik, U; Nichol, R C; Popovic, B; Sako, M; Scolnic, D; Smith, M; Taylor, G; Tucker, B E; Wiseman, P; Aguena, M; Allam, S; Annis, J; Asorey, J; Bacon, D; Bertin, E; Brooks, D; Burke, D L; Rosell, A Carnero; Carretero, J; Castander, F J; Costanzi, M; da Costa, L N; Pereira, M E S; De Vicente, J; Desai, S; Diehl, H T; Doel, P; Everett, S; Ferrero, I; Flaugher, B; Fosalba, P; Frieman, J; García-Bellido, J; Gerdes, D W; Gruen, D; Gutierrez, G; Hinton, S R; Hollowood, D L; Honscheid, K; James, D J; Kuehn, K; Kuropatkin, N; Lahav, O; Li, T S; Lima, M; Maia, M A G; Marshall, J L; Miquel, R; Morgan, R; Ogando, R L C; Palmese, A; Paz-Chinchón, F; Pieres, A; Malagón, A A Plazas; Reil, K; Roodman, A; Sanchez, E; Schubnell, M; Serrano, S; Sevilla-Noarbe, I; Suchyta, E; Tarle, G; To, C; Varga, T N; Weller, J; Wilkinson, R D;
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 518:Issue 1(2023)
• Page Start: 1106
• Page End: 1127
• Publication Date: 2022-06-03
• Subjects: surveys -- supernovae: general -- cosmology: observations; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac1404
• Languages: English
• ISSNs: 0035-8711

Abstract

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 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. 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 ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet's criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w 0 and wa (assuming a flat universe), and find these to be <0.009 in w 0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.