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

DES Collaboration, M. Vincenzi, O. Graur, L. Kelsey, R. C. Nichol, D. Bacon

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)


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-year SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8–3.5 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 marginalised 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.
Original languageEnglish
Pages (from-to)1106-1127
JournalMonthly Notices of the Royal Astronomical Society
Issue number1
Early online date3 Jun 2022
Publication statusPublished - 21 Nov 2022


  • surveys
  • supernovae: general
  • cosmology: obervations
  • UKRI
  • STFC
  • ST/P006760/1
  • ST/R000506/1
  • MR/T01881X/1


Dive into the research topics of 'The Dark Energy Survey Supernova Program: cosmological biases from supernova photometric classification'. Together they form a unique fingerprint.

Cite this