Browsing by Author "Graur, O"
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Item Open Access The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classificationVincenzi, M; Sullivan, M; Möller, A; Armstrong, P; Bassett, BA; Brout, D; Carollo, D; Carr, A; Davis, TM; Frohmaier, C; Galbany, L; Glazebrook, K; Graur, O; Kelsey, L; Kessler, R; Kovacs, E; Lewis, GF; Lidman, C; Malik, U; Nichol, RC; Popovic, B; Sako, M; Scolnic, D; Smith, M; Taylor, G; Tucker, BE; Wiseman, P; Aguena, M; Allam, S; Annis, J; Asorey, J; Bacon, D; Bertin, E; Brooks, D; Burke, DL; Rosell, A Carnero; Carretero, J; Castander, FJ; Costanzi, M; Costa, LN da; Pereira, MES; Vicente, J De; Desai, S; Diehl, HT; Doel, P; Everett, S; Ferrero, I; Flaugher, B; Fosalba, P; Frieman, J; García-Bellido, J; Gerdes, DW; Gruen, D; Gutierrez, G; Hinton, SR; Hollowood, DL; Honscheid, K; James, DJ; Kuehn, K; Kuropatkin, N; Lahav, O; Li, TS; Lima, M; Maia, MAG; Marshall, JL; Miquel, R; Morgan, R; Ogando, RLC; Palmese, A; Paz-Chinchón, F; Pieres, A; Malagón, AA Plazas; Reil, K; Roodman, A; Sanchez, E; Schubnell, M; Serrano, S; Sevilla-Noarbe, I; Suchyta, E; Tarle, G; To, C; Varga, TN; Weller, J; Wilkinson, RDCosmological 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.Item Open Access The Dark Energy Survey Supernova Program: Modelling selection efficiency and observed core collapse supernova contaminationVincenzi, M; Sullivan, M; Graur, O; Brout, D; Davis, TM; Frohmaier, C; Galbany, L; Gutiérrez, CP; Hinton, SR; Hounsell, R; Kelsey, L; Kessler, R; Kovacs, E; Kuhlmann, S; Lasker, J; Lidman, C; Möller, A; Nichol, RC; Sako, M; Scolnic, D; Smith, M; Swann, E; Wiseman, P; Asorey, J; Lewis, GF; Sharp, R; Tucker, BE; Aguena, M; Allam, S; Avila, S; Bertin, E; Brooks, D; Burke, DL; Rosell, AC; Kind, MC; Carretero, J; Castander, FJ; Choi, A; Costanzi, M; Da Costa, LN; Pereira, MES; De Vicente, J; Desai, S; Diehl, HT; Doel, P; Everett, S; Ferrero, I; Fosalba, P; Frieman, J; Garciá-Bellido, J; Gaztanaga, E; Gerdes, DW; Gruen, D; Gruendl, RA; Gutierrez, G; Hollowood, DL; Honscheid, K; Hoyle, B; James, DJ; Kuehn, K; Kuropatkin, N; Maia, MAG; Martini, P; Menanteau, F; Miquel, R; Morgan, R; Palmese, A; Paz-Chinchón, F; Plazas, AA; Romer, AK; Sanchez, E; Scarpine, V; Serrano, S; Sevilla-Noarbe, I; Soares-Santos, M; Suchyta, E; Tarle, G; Thomas, D; To, C; Varga, TN; Walker, AR; Wilkinson, RDThe analysis of current and future cosmological surveys of type Ia supernovae (SNe Ia) at high-redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-year photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 5.8 to 9.3 per cent, with an average of 7.0 per cent and r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty.