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

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Varga,  T. N.
Optical and Interpretative Astronomy, MPI for Extraterrestrial Physics, Max Planck Society;

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Weller,  J.
Optical and Interpretative Astronomy, MPI for Extraterrestrial Physics, Max Planck Society;

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Citation

Vincenzi, M., Sullivan, M., Möller, A., Armstrong, P., Bassett, B. A., Brout, D., et al. (2022). The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification. Monthly Notices of the Royal Astronomical Society, 518(1), 1106-1127. doi:10.1093/mnras/stac1404.


Cite as: https://hdl.handle.net/21.11116/0000-000C-D155-8
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.