학술논문
The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
Document Type
Working Paper
Author
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.
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Subject
Language
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 on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-year SN sample. As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. Depending on the choice of non-Ia SN models in both the simulated data sample and training sample, contamination ranges from 0.8-3.5 %, with the efficiency of the classification from 97.7-99.5 %. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension 'BEAMS with Bias Correction' (BBC), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects and we use it to constrain the dark energy equation-of-state, $w$. Assuming a flat universe with Gaussian $\Omega_M$ prior of $0.311\pm0.010$, we show that biases on $w$ are $<0.008$ when using SuperNNova and accounting for a wide range of non-Ia SN models in the simulations. Systematic uncertainties associated with contamination are estimated to be at most $\sigma_{w, \mathrm{syst}}=0.004$. This compares to an expected statistical uncertainty of $\sigma_{w,\mathrm{stat}}=0.039$ for the DES-SN sample, thus showing that contamination is not a limiting uncertainty in our analysis. We also measure biases due to contamination on $w_0$ and $w_a$ (assuming a flat universe), and find these to be $<$0.009 in $w_0$ and $<$0.108 in $w_a$, hence 5 to 10 times smaller than the statistical uncertainties expected from the DES-SN sample.