The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

Journal Article · 03 June 2022 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stac1404 · OSTI ID:1899179

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.

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 w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-07CH11359
• OSTI ID: 1899179
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 1 Vol. 518; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English