First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy

Smith, M.; D’Andrea, C. B.; Sullivan, M.; Möller, A.; Nichol, R. C.; Thomas, R. C.; Kim, A. G.; Šako, M.; Castander, F. J.; Filippenko, A. V.; Foley, R. J.; Galbany, L.; González–Gaitán, S.; Kasai, E.; Kirshner, R.; Lidman, C.; Scolnic, D.; Brout, D.; Davis, Tamara M.; Gupta, R.; Hinton, S. R.; Keßler, R.; Lasker, J.; Macaulay, E.; Wolf, R. C.; Zhang, B.; Asorey, J.; Avelino, Arturo; Bassett, Bruce A.; Calcino, Josh; Carollo, D.; Casas, R.; Challis, Peter; Childress, M.; Clocchiatti, A.; Crawford, S.; Frohmaier, C.; Glazebrook, Karl; Goldstein, Daniel A.; Graham, M. L.; Hoormann, J. K.; Kuêhn, K.; Lewis, Geraint F.; Mandel, Kaisey S.; Morganson, E.; Muthukrishna, Daniel; Nugent, P.; Pan, Y. C.; Pursiainen, M.; Sharp, Robert; Sommer, N. E.; Swann, E.; Thomas, B. P.; Tucker, B.; Uddin, S. A.; Wiseman, P.; Zheng, W.; Abbott, T. M. C.; Annis, J.; Ávila, S.; Bechtol, K.; Bernstein, G. M.; Bertin, E.; Brooks, D.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Cunha, C. E.; Costa, L. N. da; Davis, C.; Vicente, J. De; Diehl, H. T.; Eifler, T. F.; Estrada, J.; Frieman, Joshua A.; García-Bellido, J.; Gaztañaga, E.; Gerdes, D. W.; Gruen, David; Gruendl, R. A.; Gschwend, J.; Gutiérrez, G.; Hartley, W. G.; Hollowood, D. L.; Honscheid, K.; Hoyle, B.; James, D. J.; Johnson, M. W. G.; Johnson, Michael D.; Kuropatkin, N.; Li, T. S.; Lima, M.; Maia, M. A. G.; March, M.; Marshall, J. L.; Martini, Paul; Menanteau, F.; Miller, Christopher J.; Miquel, R.; Neilsen, Eric H.; Ogando, R. L. C.; Plazas, A. A.; Romer, A. K.; Sánchez, E.; Scarpine, V.; Schubnell, M.; Serrano, S.; Sevilla-Noarbe, I.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Tarlè, G.; Tucker, D. L.; Wester, W.

doi:10.3847/1538-3881/abc01b

Cited by 46 publications

(49 citation statements)

References 85 publications

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“…The 207 supernovae used in this analysis were discovered via repeated deep-field observations of a 27 deg 2 region of the sky taken between August 2013 and February 2016, and are in the redshift range 0.07 < z < 0.85. A series of papers describe the search and discovery [86,106,107], calibration [108,109], photometry [110], spectroscopic follow-up [111], simulations [112], selection effects [113], and analysis methodology [72] that went into those results. Following the DES supernova analysis [33,34] (but not the fiducial choices of the multiprobe analysis of Ref.…”

Section: Des Y3 + Low-z Supernovaementioning

confidence: 99%

DES Y1 results: Splitting growth and geometry to test ΛCDM

Muir¹,

Baxter²,

Miranda³

et al. 2021

Phys. Rev. D

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Section: Des Y3 + Low-z Supernovaementioning

confidence: 99%

DES Y1 results: Splitting growth and geometry to test ΛCDM

Muir¹,

Baxter²,

Miranda³

et al. 2021

Phys. Rev. D

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“…These include thousands of high-redshift SNe Ia, of which only several hundred have been spectroscopically classified. The first three years of the DES-SN detected and spectroscopically classified 251 SNe Ia (Smith et al 2020). Together with low redshift SNe from the Harvard-Smithsonian Center for Astrophysics surveys (CfA3, CfA4; Hicken et al 2009Hicken et al , 2012 and the Carnegie Supernova Project (CSP; Contreras et al 2010;Stritzinger et al 2011), these SNe were used to constrain cosmological parameters (Dark Energy Survey 2019).…”

Section: Introductionmentioning

confidence: 99%

The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae

Möller,

Smith,

Sako

et al. 2022

Preprint

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As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework S NN (SNN; Möller & de Boissière 2019) trained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1,863 SNe Ia from which we select 1,484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < 𝑧 < 1.14. We find good agreement between the light-curve properties of the photometrically-selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).

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“…The development of photometric classification has been motivated by the recent and future large SN surveys like the Sloan Digital Sky Survey (SDSS) SN Survey (Sako et al 2018), the Pan-STARRS Medium Deep Survey (Jones et al 2017(Jones et al , 2018, the Dark Energy Survey SN program (Bernstein et al 2012;Smith et al 2020b) and the future Legacy Survey of Space and Time (Ivezić et al 2019, LSST). These SN imaging surveys motivated large spectroscopic follow-up programs to measure host-galaxy redshifts for the majority of discovered SNe, and use them for cosmological measurements.…”

Section: Introductionmentioning

confidence: 99%

“…This paper investigates biases in the measurement of cosmological parameters that are introduced in the use of photometric SN classification algorithms within the BBC framework. Our focus is on the Dark Energy Survey 1 (DES) SN program (DES-SN; Smith et al 2020b) dataset. DES-SN is a state-of-the-art sample for SN Ia cosmology analysis, with approximately 2000 likely SNe Ia in the final '5-year' sample: ∼ 20 per cent of the SNe have follow-up spectroscopy of the SN itself (e.g., Smith et al 2020b), and most of the remaining events have a host galaxy spectroscopic redshift (see Lidman et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Our focus is on the Dark Energy Survey 1 (DES) SN program (DES-SN; Smith et al 2020b) dataset. DES-SN is a state-of-the-art sample for SN Ia cosmology analysis, with approximately 2000 likely SNe Ia in the final '5-year' sample: ∼ 20 per cent of the SNe have follow-up spectroscopy of the SN itself (e.g., Smith et al 2020b), and most of the remaining events have a host galaxy spectroscopic redshift (see Lidman et al 2020). Vincenzi et al (2021, hereafter V21) previously presented large simulations of DES-SN that generate realistic samples of transients that accurately describe DES-SN data.…”

Section: Introductionmentioning

confidence: 99%

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The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification

Vincenzi,

Sullivan,

Möller

et al. 2021

Preprint

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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 per cent, with the efficiency of the classification from 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 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 Ω 𝑀 prior of 0.311 ± 0.010, 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 𝜎 𝑤 ,syst = 0.004. This compares to an expected statistical uncertainty of 𝜎 𝑤 ,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.

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First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy

Cited by 46 publications

References 85 publications

DES Y1 results: Splitting growth and geometry to test ΛCDM

DES Y1 results: Splitting growth and geometry to test ΛCDM

The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae

The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification

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