Browsing by Author "Frohmaier, C"
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Item Open Access First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: The Effect of Host Galaxy Properties on Supernova LuminositySmith, M; Sullivan, M; Wiseman, P; Kessler, R; Scolnic, D; Brout, D; D'Andrea, CB; Davis, TM; Foley, RJ; Frohmaier, C; Galbany, L; Gupta, RR; Gutiérrez, CP; Hinton, SR; Kelsey, L; Lidman, C; Macaulay, E; Möller, A; Nichol, RC; Nugent, P; Palmese, A; Pursiainen, M; Sako, M; Swann, E; Thomas, RC; Tucker, BE; Vincenzi, M; Carollo, D; Lewis, GF; Sommer, NE; Abbott, TMC; Aguena, M; Allam, S; Avila, S; Bertin, E; Bhargava, S; Brooks, D; Buckley-Geer, E; Burke, DL; Rosell, AC; Kind, MC; Costanzi, M; da Costa, LN; de Vicente, J; Desai, S; Diehl, HT; Doel, P; Eifler, TF; Everett, S; Flaugher, B; Fosalba, P; Frieman, J; García-Bellido, J; Gaztanaga, E; Glazebrook, K; Gruen, D; Gruendl, RA; Gschwend, J; Gutierrez, G; Hartley, WG; Hollowood, DL; Honscheid, K; James, DJ; Krause, E; Kuehn, K; Kuropatkin, N; Lima, M; MacCrann, N; Maia, MAG; Marshall, JL; Martini, P; Melchior, P; Menanteau, F; Miquel, R; Paz-Chinchón, F; Plazas, AA; Romer, AK; Roodman, A; Rykoff, ES; Sanchez, E; Scarpine, V; Schubnell, M; Serrano, S; Sevilla-Noarbe, I; Suchyta, E; Swanson, MEC; Tarle, G; Thomas, D; Tucker, DL; Varga, TN; Walker, ARWe present improved photometric measurements for the host galaxies of 206 spectroscopically confirmed type Ia supernovae discovered by the Dark Energy Survey Supernova Program (DES-SN) and used in the first DES-SN cosmological analysis. Fitting spectral energy distributions to the $griz$ photometric measurements of the DES-SN host galaxies, we derive stellar masses and star-formation rates. For the DES-SN sample, when considering a 5D ($z$, $x_1$, $c$, $\alpha$, $\beta$) bias correction, we find evidence of a Hubble residual `mass step', where SNe Ia in high mass galaxies ($>10^{10} \textrm{M}_{\odot}$) are intrinsically more luminous (after correction) than their low mass counterparts by $\gamma=0.040\pm0.019$mag. This value is larger by $0.031$mag than the value found in the first DES-SN cosmological analysis. This difference is due to a combination of updated photometric measurements and improved star formation histories and is not from host-galaxy misidentification. When using a 1D (redshift-only) bias correction the inferred mass step is larger, with $\gamma=0.066\pm0.020$mag. The 1D-5D $\gamma$ difference for DES-SN is $0.026\pm0.009$mag. We show that this difference is due to a strong correlation between host galaxy stellar mass and the $x_1$ component of the 5D distance-bias correction. To better understand this effect, we include an intrinsic correlation between light-curve width and stellar mass in simulated SN Ia samples. We show that a 5D fit recovers $\gamma$ with $-9$mmag bias compared to a $+2$mmag bias for a 1D fit. This difference can explain part of the discrepancy seen in the data. Improvements in modeling correlations between galaxy properties and SN is necessary to determine the implications for $\gamma$ and ensure unbiased precision estimates of the dark energy equation-of-state as we enter the era of LSST.Item Open Access Supernova Host Galaxies in the Dark Energy Survey: I. Deep Coadds, Photometry, and Stellar MassesWiseman, P; Smith, M; Childress, M; Kelsey, L; Möller, A; Gupta, RR; Swann, E; Angus, CR; Brout, D; Davis, TM; Foley, RJ; Frohmaier, C; Galbany, L; Gutiérrez, CP; Inserra, C; Kessler, R; Lewis, GF; Lidman, C; Macaulay, E; Nichol, RC; Pursiainen, M; Sako, M; Scolnic, D; Sommer, NE; Sullivan, M; Tucker, BE; Abbott, TMC; Aguena, M; Allam, S; Avila, S; Bertin, E; Brooks, D; Buckley-Geer, E; Burke, DL; Rosell, AC; Carollo, D; Kind, MC; da Costa, LN; de Vicente, J; Desai, S; Diehl, HT; Doel, P; Eifler, TF; Everett, S; Fosalba, P; Frieman, J; García-Bellido, J; Gaztanaga, E; Gerdes, DW; Gill, MSS; Glazebrook, K; Gruendl, RA; Gschwend, J; Hartley, WG; Hinton, SR; Hollowood, DL; Honscheid, K; James, DJ; Kuehn, K; Kuropatkin, N; Lima, M; Maia, MAG; March, M; Martini, P; Melchior, P; Menanteau, F; Miquel, R; Ogando, RLC; Paz-Chinchón, F; Plazas, AA; Romer, AK; Roodman, A; Sanchez, E; Scarpine, V; Serrano, S; Suchyta, E; Swanson, MEC; Tarle, G; Thomas, D; Tucker, DL; Varga, TN; Walker, AR; Wilkinson, RDThe five-year Dark Energy Survey supernova programme (DES-SN) is one of the largest and deepest transient surveys to date in terms of volume and number of supernovae. Identifying and characterising the host galaxies of transients plays a key role in their classification, the study of their formation mechanisms, and the cosmological analyses. To derive accurate host galaxy properties, we create depth-optimised coadds using single-epoch DES-SN images that are selected based on sky and atmospheric conditions. For each of the five DES-SN seasons, a separate coadd is made from the other 4 seasons such that each SN has a corresponding deep coadd with no contaminating SN emission. The coadds reach limiting magnitudes of order $\sim 27$ in $g$-band, and have a much smaller magnitude uncertainty than the previous DES-SN host templates, particularly for faint objects. We present the resulting multi-band photometry of host galaxies for samples of spectroscopically confirmed type Ia (SNe Ia), core-collapse (CCSNe), and superluminous (SLSNe) as well as rapidly evolving transients (RETs) discovered by DES-SN. We derive host galaxy stellar masses and probabilistically compare stellar-mass distributions to samples from other surveys. We find that the DES spectroscopically confirmed sample of SNe Ia selects preferentially fewer high mass hosts at high redshift compared to other surveys, while at low redshift the distributions are consistent. DES CCSNe and SLSNe hosts are similar to other samples, while RET hosts are unlike the hosts of any other transients, although these differences have not been disentangled from selection effects.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.