| Autor: |
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. |
| Rok vydání: |
2021 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1093/mnras/stac1404 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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