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

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.; Vicente, J. De; 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.

doi:10.1093/mnras/stac1404

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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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引用

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. C., Carretero, J., Castander, F. J., Costanzi, M., da Costa, L. N., Pereira, M. E. S., Vicente, J. D., 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. P., 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. (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.

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

要旨

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