First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters
T. M. C. Abbott, S. Allam, P. Andersen, C. Angus, J. Asorey, A. Avelino, S. Avila, B. A. Bassett, K. Bechtol, G. M. Bernstein, E. Bertin, D. Brooks, D. Brout, P. Brown, D. L. Burke, J. Calcino, A. Carnero Rosell, D. Carollo, M. Carrasco Kind, J. Carretero, R. Casas, F. J. Castander, R. Cawthon, P. Challis, M. Childress, A. Clocchiatti, C. E. Cunha, C. B. D'Andrea, L. N. da Costa, C. Davis, T. M. Davis, J. De Vicente, D. L. DePoy, S. Desai, H. T. Diehl, P. Doel, A. Drlica-Wagner, T. F. Eifler, A. E. Evrard, E. Fernandez, A. V. Filippenko, D. A. Finley, B. Flaugher, R. J. Foley, P. Fosalba, J. Frieman, L. Galbany, J. Garcia-Bellido, E. Gaztanaga, T. Giannantonio, K. Glazebrook, D. A. Goldstein, S. Gonzalez-Gaitan, D. Gruen, R. A. Gruendl, J. Gschwend, R. R. Gupta, G. Gutierrez, W. G. Hartley, S. R. Hinton, D. L. Hollowood, K. Honscheid, J. K. Hoormann, B. Hoyle, D. J. James, T. Jeltema, M. W. G. Johnson, M. D. Johnson, E. Kasai, S. Kent, R. Kessler, A. G. Kim, R. P. Kirshner, E. Kovacs, E. Krause, R. Kron, K. Kuehn, S. Kuhlmann, N. Kuropatkin, O. Lahav, J. Lasker, G. F. Lewis, T. S. Li, C. Lidman, M. Lima, H. Lin, E. Macaulay, M. A. G. Maia, K. S. Mandel, M. March, J. Marriner, J. L. Marshall, P. Martini, F. Menanteau, C. J. Miller, R. Miquel, V. Miranda, J. J. Mohr, E. Morganson, D. Muthukrishna, et al. (44 additional authors not shown)
FFERMILAB-PUB-18-590-AEDES-2018-0368
Draft version May 13, 2019
Preprint typeset using L A TEX style emulateapj v. 12/16/11
FIRST COSMOLOGY RESULTS USING TYPE IA SUPERNOVAE FROM THE DARK ENERGY SURVEY:CONSTRAINTS ON COSMOLOGICAL PARAMETERS
T. M. C. Abbott , S. Allam , P. Andersen , C. Angus , J. Asorey , A. Avelino , S. Avila , B. A. Bassett ,K. Bechtol , G. M. Bernstein , E. Bertin , D. Brooks , D. Brout , P. Brown , D. L. Burke ,J. Calcino , A. Carnero Rosell , D. Carollo , M. Carrasco Kind , J. Carretero , R. Casas ,F. J. Castander , R. Cawthon , P. Challis , M. Childress , A. Clocchiatti , C. E. Cunha ,C. B. D’Andrea , L. N. da Costa , C. Davis , T. M. Davis , J. De Vicente , D. L. DePoy , S. Desai ,H. T. Diehl , P. Doel , A. Drlica-Wagner , T. F. Eifler , A. E. Evrard , E. Fernandez ,A. V. Filippenko , D. A. Finley , B. Flaugher , R. J. Foley , P. Fosalba , J. Frieman , L. Galbany ,J. Garc´ıa-Bellido , E. Gaztanaga , T. Giannantonio , K. Glazebrook , D. A. Goldstein , S.Gonz´alez-Gait´an , D. Gruen , R. A. Gruendl , J. Gschwend , R. R. Gupta , G. Gutierrez ,W. G. Hartley , S. R. Hinton , D. L. Hollowood , K. Honscheid , J. K. Hoormann , B. Hoyle ,D. J. James , T. Jeltema , M. W. G. Johnson , M. D. Johnson , E. Kasai , S. Kent , R. Kessler ,A. G. Kim , R. P. Kirshner , E. Kovacs , E. Krause , R. Kron , K. Kuehn , S. Kuhlmann ,N. Kuropatkin , O. Lahav , J. Lasker , G. F. Lewis , T. S. Li , C. Lidman , M. Lima , H. Lin ,E. Macaulay , M. A. G. Maia , K. S. Mandel , M. March , J. Marriner , J. L. Marshall , P. Martini ,F. Menanteau , C. J. Miller , R. Miquel , V. Miranda , J. J. Mohr , E. Morganson ,D. Muthukrishna , A. M¨oller , E. Neilsen , R. C. Nichol , B. Nord , P. Nugent , R. L. C. Ogando ,A. Palmese , Y.-C. Pan , A. A. Plazas , M. Pursiainen , A. K. Romer , A. Roodman , E. Rozo ,E. S. Rykoff , M. Sako , E. Sanchez , V. Scarpine , R. Schindler , M. Schubnell , D. Scolnic ,S. Serrano , I. Sevilla-Noarbe , R. Sharp , M. Smith , M. Soares-Santos , F. Sobreira ,N. E. Sommer , H. Spinka , E. Suchyta , M. Sullivan , E. Swann , G. Tarle , D. Thomas , R. C. Thomas ,M. A. Troxel , B. E. Tucker , S. A. Uddin , A. R. Walker , W. Wester , P. Wiseman , R. C. Wolf ,B. Yanny , B. Zhang , Y. Zhang (DES Collaboration) Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia University of Copenhagen, Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK Korea Astronomy and Space Science Institute, Yuseong-gu, Daejeon, 305-348, Korea Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138, USA Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK African Institute for Mathematical Sciences, 6 Melrose Road, Muizenberg, 7945, South Africa South African Astronomical Observatory, P.O.Box 9, Observatory 7935, South Africa LSST, 933 North Cherry Avenue, Tucson, AZ 85721, USA Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA CNRS, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, and Department of Physics andAstronomy, Texas A&M University, College Station, TX 77843, USA Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450, Stanford University, Stanford, CA 94305, USA SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA Centro de Investigaciones Energ´eticas, Medioambientales y Tecnol´ogicas (CIEMAT), Madrid, Spain Laborat´orio Interinstitucional de e-Astronomia - LIneA, Rua Gal. Jos´e Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil INAF, Astrophysical Observatory of Turin, I-10025 Pino Torinese, Italy Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA Institut de F´ısica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra(Barcelona) Spain Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain Physics Department, 2320 Chamberlin Hall, University of Wisconsin-Madison, 1150 University Avenue Madison, WI 53706-1390 Millennium Institute of Astrophysics and Department of Physics and Astronomy, Universidad Cat´olica de Chile, Santiago, Chile Observat´orio Nacional, Rua Gal. Jos´e Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA Department of Astronomy/Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA Miller Senior Fellow, Miller Institute for Basic Research in Science, University of California, Berkeley, CA 94720, USA Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK a r X i v : . [ a s t r o - ph . C O ] M a y Universit¨ats-Sternwarte, Fakult¨at f¨ur Physik, Ludwig-Maximilians Universit¨at M¨unchen, Scheinerstr. 1, 81679 M¨unchen, Germany Centre for Astrophysics & Supercomputing, Swinburne University of Technology, Victoria 3122, Australia California Institute of Technology, 1200 East California Blvd, MC 249-17, Pasadena, CA 91125, USA CENTRA, Instituto Superior T´ecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA Department of Physics, The Ohio State University, Columbus, OH 43210, USA Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA Department of Physics, University of Namibia, 340 Mandume Ndemufayo Avenue, Pionierspark, Windhoek, Namibia Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138,USA Gordon and Betty Moore Foundation, 1661 Page Mill Road, Palo Alto, CA 94304,USA Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA Australian Astronomical Optics, Macquarie University, North Ryde, NSW 2113, Australia Sydney Institute for Astronomy, School of Physics, A28, The University of Sydney, NSW 2006, Australia The Research School of Astronomy and Astrophysics, Australian National University, ACT 2601, Australia Departamento de F´ısica Matem´atica, Instituto de F´ısica, Universidade de S˜ao Paulo, CP 66318, S˜ao Paulo, SP, 05314-970, Brazil Institute of Astronomy and Kavli Institute for Cosmology, Madingley Road, Cambridge, CB3 0HA, UK Department of Astronomy, The Ohio State University, Columbus, OH 43210, USA Instituci´o Catalana de Recerca i Estudis Avan¸cats, E-08010 Barcelona, Spain Excellence Cluster Universe, Boltzmannstr. 2, 85748 Garching, Germany Faculty of Physics, Ludwig-Maximilians-Universit¨at, Scheinerstr. 1, 81679 Munich, Germany ARC Centre of Excellence for All-sky Astrophysics (CAASTRO) Division of Theoretical Astronomy, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan Institute of Astronomy and Astrophysics, Academia Sinica, Taipei 10617, Taiwan Department of Physics and Astronomy, Pevensey Building, University of Sussex, Brighton, BN1 9QH, UK Department of Physics, University of Arizona, Tucson, AZ 85721, USA Brandeis University, Physics Department, 415 South Street, Waltham MA 02453 Instituto de F´ısica Gleb Wataghin, Universidade Estadual de Campinas, 13083-859, Campinas, SP, Brazil Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101, USA Graduate School of Education, Stanford University, 160, 450 Serra Mall, Stanford, CA 94305, USA
Draft version May 13, 2019
ABSTRACTWe present the first cosmological parameter constraints using measurements of type Ia supernovae(SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsampleof 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with alow-redshift sample of 122 SNe from the literature. Our “DES-SN3YR” result from these 329 SNe Iais based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopicselection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertain-ties. For a flat ΛCDM model we find a matter density Ω m = 0 . ± . w CDM model,and combining our SN Ia constraints with those from the cosmic microwave background (CMB),we find a dark energy equation of state w = − . ± . m = 0 . ± . w w a CDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w = − . ± .
114 and w a = − . ± . Subject headings: cosmology: supernovae INTRODUCTION
Type Ia supernovae (SNe Ia) were used to discoverthe accelerating expansion of the universe (Riess et al.1998, Perlmutter et al. 1999) and remain one of the keyprobes for understanding the nature of the mysterious“dark energy.” Over the last two decades, there havebeen considerable improvements in the calibration andsize of samples at low redshift (Jha et al. 2006; Hickenet al. 2009a, 2012; Contreras et al. 2010), intermediateredshift (Holtzman et al. 2008), and high redshift (Astieret al. 2006; Wood-Vasey et al. 2007; Conley et al. 2011;Rest et al. 2014; Betoule et al. 2014). When combinedwith cosmic microwave background (CMB) data, thesesamples have been used to demonstrate that the darkenergy equation of state, w , is consistent with a cosmo-logical constant ( w = −
1) with a precision of σ w = 0 . > w = − . ± .
041 (Scolnic et al. 2018).The Dark Energy Survey Supernova program (DES-SN) is striving to find even greater numbers of SNe whilereducing systematic uncertainties on the resulting cos-mological parameters. A top priority of this effort hasbeen to accurately model each component of the DES-SN search and analysis, and to accurately simulate biascorrections for the SN Ia distance measurements. DEShas also made improvements in instrumentation and cal-ibration, including: (i) detectors with higher z -band effi-ciency to improve measurements of rest frame supernova(SN) colors at high-redshift, and (ii) extension of the pho-tometric calibration precision over a wide color range bycorrecting each charged-coupled device (CCD) and expo-sure for atmospheric variations and the spectral energydistribution (SED) of the source (see Sect. 3). Theseimprovements enable DES-SN to make a state-of-the-artmeasurement of dark energy properties.ES: Cosmological results with spectroscopically confirmed type Ia supernovae 3This Letter reports “DES-SN3YR” cosmological con-straints from the spectroscopically confirmed SNe Ia inthe first three years of DES-SN in combination with alow-redshift SN Ia sample from the literature. The re-sults presented here are the culmination of a series ofcompanion papers, which contain details of the SN searchand discovery (Kessler et al. 2015; Morganson et al. 2018;Goldstein et al. 2015); spectroscopic follow-up (D’Andreaet al. 2018); photometry (Brout et al. 2019a); calibra-tion (Burke et al. 2018; Lasker et al. 2019); simulations(Kessler et al. 2019); and technique to account for se-lection bias (Kessler & Scolnic 2017). The cosmologi-cal analysis method and validation are detailed in Broutet al. (2019b, B18), which presents the full statistical andsystematic uncertainty budget for these new results. Hin-ton et al. (2019) test a new Bayesian Hierarchical Modelfor supernova cosmology. In this letter, we summarizethese contributions and present our measurements of theequation-of-state ( w ) and matter density (Ω m ). Dataproducts used in this analysis are publicly available on-line. In addition, Macaulay et al. (2019) measure theHubble constant ( H ) by applying these DES-SN3YR re-sults to the inverse-distance-ladder method anchored tothe standard ruler measured by baryon acoustic oscilla-tions (Alam et al. 2017; Carter et al. 2018, BAO), andrelated to the sound horizon measured with CMB data(Planck Collaboration et al. 2016).In § §
3, we summarize the analysis pipeline. In §
4, we presentthe cosmology results. In §
5, we present our discussionand conclusions. DATA SAMPLES
The DES-SN sample for this analysis was collectedover three 5-month-long seasons, from August 2013 toFebruary 2016, using the Dark Energy Camera (DECam,Flaugher et al. 2015) at the Cerro Tololo Inter-AmericanObservatory. Ten 2.7 deg fields were observed approx-imately once per week in the griz filter bands (Abbottet al. 2018). The average depth per visit was 23.5 mag inthe eight “shallow” fields, and 24.5 mag in the two “deep”fields. Within 24 hours of each observation, search im-ages were processed (Morganson et al. 2018), new tran-sients were discovered using a difference-imaging pipeline(Kessler et al. 2015), and most of the subtraction ar-tifacts were rejected with a machine-learning algorithmapplied to image stamps (Goldstein et al. 2015).A subset of lightcurves was selected for spectroscopicfollow-up observations (D’Andrea et al. 2018), result-ing in 251 spectroscopically confirmed SNe Ia with red-shifts 0 . < z < .
85, and 207 SNe Ia that satisfyanalysis requirements (B18) such as signal-to-noise andlight curve sampling; this sample is called the DES-SNsubset. The spectroscopic program required a collab-orative effort coordinated across several observatories.At low to intermediate redshifts, the primary follow-up instrument is the 4-meter Anglo-Australian Telescope(AAT), which confirmed and measured redshifts for 31%of our SN Ia sample (OzDES collaboration; Yuan et al.2015; Childress et al. 2017; Hinton et al. 2016). A va-riety of spectroscopic programs (D’Andrea et al. 2018)were carried out using the European Southern Observa- https://des.ncsa.illinois.edu/releases/sn tory Very Large Telescope, Gemini, Gran Telescopio Ca-narias, Keck, Magellan, MMT, and South African LargeTelescope.We supplement the DES-SN sample with a low-redshift( z < .
1) sample, which we call the low- z subset, com-prising 122 SNe from the Harvard-Smithsonian Centerfor Astrophysics surveys (CfA3, CfA4; Hicken et al.2009b, 2012) and the Carnegie Supernova Project (CSP;Contreras et al. 2010; Stritzinger et al. 2011). We onlyuse samples with measured telescope+filter transmis-sions, and thus CfA1 and CfA2 are not included. ANALYSIS
Supernova cosmology relies on measuring the luminos-ity distance ( d L ) versus redshift for many SNe Ia andcomparing this relation to the prediction of cosmologicalmodels. The distance modulus ( µ ) is defined as µ = 5 log[ d L / . (1)For a flat universe with cold dark matter density Ω m ,dark energy density Ω Λ , and speed of light c , the lumi-nosity distance to a source at redshift z is given by d L = (1 + z ) c (cid:90) z dz (cid:48) H ( z (cid:48) ) , (2)with H ( z ) = H (cid:104) Ω m (1 + z ) + Ω Λ (1 + z ) w ) (cid:105) / . (3)Observationally, the distance modulus of a supernova isgiven by µ = m B + αx − β C + M + γG host + ∆ µ bias . (4)For each SN Ia, the set of griz light curves are fit ( § x , with m B ≡ − . x )),light curve width ( x ), and color ( C ). γ describes thedependence on host-galaxy stellar mass ( M host , § G host = +1 / M host > M (cid:12) , and G host = − / M host < M (cid:12) . A correction for selection bi-ases (∆ µ bias ) is determined from simulations ( § α, β, γ ,and M . The first three parameters describe how theSN Ia luminiosity is correlated with the light curve width( αx ), color ( β C ), and host-galaxy stellar mass ( γG host ). M accounts for both the absolute magnitude of SNe Iaand the Hubble constant. In the rest of this section wedescribe the main components of the analysis pipelinethat are needed to determine the distances (Eq. 4) andcosmological parameters. Calibration
The DES sample is calibrated to the AB magni-tude system (Oke & Gunn 1983) using measurementsof the Hubble Space Telescope (HST) CalSpec standardC26202 (Bohlin et al. 2014). DES internally calibratedroughly 50 standard stars per CCD using a ‘ForwardGlobal Calibration Method’ (Burke et al. 2018; Laskeret al. 2019). Improvements in calibration at the 0.01mag (1%) level are made using SED-dependent ‘chro-matic corrections’ to both the standard stars and tothe DES-SN lightcurve photometry. The low- z sampleis calibrated to the AB system by cross-calibrating tothe Pan-STARRS1 (PS1) photometric catalogs (Scolnicet al. 2015). We also cross-calibrate DES to PS1 and findgood agreement (see § Photometry
To measure the SN Ia flux for each observation, weemploy a scene modeling photometry (SMP) approach(Brout et al. 2019a) based on previous efforts used inSDSS-II (Holtzman et al. 2008) and SNLS (Astier et al.2013). SMP simultaneously forward models a variableSN flux on top of a temporally constant host galaxy.We test the precision by analyzing images that includeartificial SNe Ia, and find that photometric biases arelimited to < . § Spectroscopy: Typing & Redshifts
Spectral classification was performed using both theSuperNova IDentification (Blondin & Tonry 2007, SNID)and Superfit (Howell et al. 2005) software, as described inD’Andrea et al. (2018). All 207 events are spectroscop-ically classified as SNe Ia. Redshifts are obtained fromhost-galaxy spectra, where available, because their sharpspectral lines give more accurate redshifts ( σ z ∼ × − ;Yuan et al. 2015) than the broad SN Ia spectroscopic fea-tures ( σ z ∼ × − ). 158 of the DES-SN events havehost galaxy redshifts, and the rest have redshifts fromthe SN Ia spectra. For the low- z sample, we use the pub-lished redshifts with a 250 km/s uncertainty from Scolnicet al. (2018). Peculiar-velocity corrections are computedfrom Carrick et al. (2015). Light-curve fitting
To measure the SN parameters ( m B , x , C ), the lightcurves were fit with SNANA (Kessler et al. 2009) usingthe SALT2 model (Guy et al. 2010) and the trainingparameters from Betoule et al. (2014). Host Galaxy Stellar Masses
For the γG host term in Eq. 4, we first identify thehost galaxy using catalogs from Science Verification DE-Cam images (Bonnett et al. 2016), and the directionallight radius method (Sullivan et al. 2006; Gupta et al.2016). M host is derived from fitting galaxy model SEDsto griz broadband fluxes with ZPEG (Le Borgne & Rocca-Volmerange 2002). The SEDs are generated with Projetd’Etude des GAlaxies par Synthese Evolutive (
PEGASE ;Fioc & Rocca-Volmerange 1997). In the DES-SN subset,116 out of 207 hosts have M host < M (cid:12) . The low- z host galaxy stellar masses are taken from Scolnic et al.(2018). µ -Bias Corrections We use a simulation-based method (Kessler et al. 2019)to correct for distance biases arising from survey andspectroscopic selection efficiencies, and also from theanalysis and light curve fitting. For each SN Ia we cal-culate the bias correction in Eq. 4, ∆ µ bias ≡ (cid:104) µ − µ true (cid:105) ,where (cid:104)(cid:105) is the average in bins of measured redshift, color, https://snana.uchicago.edu and stretch. The distance µ is determined by analyzingthe simulated data in the same way as the real data (butwith ∆ µ bias = 0), and µ true is the true distance modulusused to generate each simulated event. The correctionincreases with redshift, and for individual SNe Ia can beas large as 0.4 mag ( § § DiffImg and spectroscopic confirmation.The quality of the simulation is illustrated by the goodagreement between the predicted and observed distribu-tion of many observables including redshift, stretch, andcolor (Figs 6 & 7 in Kessler et al. 2019, and Fig. 5 inB18).
Intrinsic scatter model
We simulate bias corrections with two different modelsof intrinsic scatter that span the range of possibilities incurrent data samples. First is the ‘G10’ model, basedon Guy et al. (2010), in which the scatter is primarilyachromatic. Second is the ‘C11’ model, based on Chotardet al. (2011), which has stronger scatter in color. For usein simulations, Kessler et al. (2013) converted each ofthese broadband scatter models into an SED-variationmodel.
Generating the Bias-Corrected Hubble Diagram
We use the “BEAMS with Bias Corrections” (BBC)method (Kessler & Scolnic 2017) to fit for { α, β, γ, M } and to fit for a weighted-average bias-corrected µ in 18redshift bins. In addition to propagating the uncertaintyfrom each term in Eq. 4, the BBC fit adds an empir-ically determined µ -uncertainty ( σ int ) to each event sothat the best fit χ /N dof = 1. This redshift-binned Hub-ble diagram is used for cosmology fitting as describedin § Cosmology Fitting
Cosmological parameters are constrained using the log-likelihood χ = (cid:126)D T [ C stat+syst ] − (cid:126)D (5)and minimizing the posterior with CosmoMC (Lewis &Bridle 2002). D i = µ ( z i ) data − µ ( z i ) model for redshift bin i = 1 , µ ( z i ) data is the BBC-fitted distance modulusin the i ’th redshift bin, and µ ( z i ) model is given by Eq. 1.The covariance matrix ( C stat+syst ) is described in § § (cid:126)D and C stat+syst are computed separately using theG10 and C11 scatter model in the bias-correction simula-tion. Each set of quantities is averaged over the G10 andES: Cosmological results with spectroscopically confirmed type Ia supernovae 5 Fig. 1.—
Hubble diagram for the DES-SN3YR sample. Top: distance modulus ( µ ) from BBC fit (black bars, which are used for cosmologyfits) and for each SN (red, orange circles). The dashed gray line shows our best fit model, while the green and blue dotted lines showmodels with no dark energy and matter densities Ω m = 0 . . σ error barsshow 68% confidence. C11 models, and these averages are used in Eq. 5. Thepurpose of averaging is to mitigate the systematic uncer-tainty related to our understanding of intrinsic scatter( § § Blinding and Validation
The cosmological parameters were blinded until pre-liminary results were presented at the 231st meetingof the American Astronomical Society in January 2018.The criteria for unblinding ( § w -bias below 0.01, and ii) the rms of w -values agreeswith the fitted w -uncertainty, for simulations with andwithout systematic variations. Following this initial un-blinding, several updates were performed ( § w increased by 0.024 andthe total uncertainty increased by 3% (0.057 to 0.059). RESULTS
We present the first cosmological results using SNe Iafrom DES. We begin with the BBC-fitted parameters( α, β, γ, σ int ) in § w in § § Fig. 2.—
Constraints on Ω m -Ω Λ for ΛCDM model (68% and95% confidence intervals). SN contours are shown with statisticaluncertainty only (white-dashed), and with total uncertainty (greenshaded). Constraints from CMB (brown) and DES-SN3YR+CMBcombined (red), are also shown. trum and low- (cid:96) polarization results. We also present re-sults without a CMB prior, and with both CMB andBAO priors. All reported uncertainties correspond to68% confidence. To evaluate consistency between ourprimary result and BAO, we compute the evidence using PolyChord (Handley et al. 2015a,b), and compute theevidence ratio ( R ) defined in Eq. V.3 of Abbott et al.(2019). Consistency is defined by R > . Results for Standardization Parameters
While the cosmology results are based on averagingdistances using the G10 and C11 intrinsic scatter models,here we show best-fit BBC values from B18 using theG10 intrinsic scatter model: α = 0 . ± . β =3 . ± . γ = 0 . ± . σ int = 0 . ± . α , β , and σ int values are consistent with those foundin previous analyses, while γ is smaller compared to thosein Kelly et al. (2010); Sullivan et al. (2010); Lampeitlet al. (2010); Betoule et al. (2014); Scolnic et al. (2018).Results with the C11 model (Table 5 of B18) show similartrends.We also check the consistency among the DES-SN andlow- z subsets. While α and β are consistent, we find σ int = 0 . ± .
006 for DES-SN, the lowest value of anyrolling SN survey. This value differs by 3 . σ from σ int =0 . ± .
015 for the low- z subset, and the systematicuncertainty in adopting a single σ int value is discussedbelow in § § γ values differby 1 . σ : γ DES = 0 . ± .
018 (consistent with zero) and γ low z = 0 . ± . w Uncertainty Budget
Contributions to the systematic uncertainty budget arepresented in B18 and shown here in Table 1 for flat w CDM fits combined with the CMB likelihood. The sta-tistical uncertainty on w ( σ w, stat ) is determined withoutsystematic contributions. Each systematic contributionis defined as σ w, syst = (cid:113) ( σ w, tot ) − ( σ w, stat ) (6)where σ w, tot is the total (stat+syst) uncertainty fromincluding a specific systematic, or a group of systemat-ics. The uncertainty in w has nearly equal contributionsfrom statistical and systematic uncertainties, the latterof which is broken into four groups in Table 1.The first three systematic groups have nearly equalcontributions: 1) photometry and calibration ( σ w =0 . z subsets, data used to train the SALT2lightcurve model, and the HST Calspec standard, 2) µ -bias corrections from the survey ( σ w = 0 . z subset, magnitude versus volume lim-ited selection for low- z , DES-SN spectroscopic selectionefficiency, and determination of DES-SN flux uncertain-ties, and 3) µ -bias corrections from astrophysical effects( σ w = 0 . σ int , parent pop-ulations of stretch and color, choice of w and Ω m in thesimulation, and Galactic extinction. The 4 th systematicsgroup, redshift ( σ w = 0 . † ) have not been included in previous analyses, and thecombined uncertainty is σ w = 0 . z subset, which is almost TABLE 1 w Uncertainty Contributions for w CDM model a Description b σ w σ w /σ w, stat Total Stat ( σ w, stat ) 0.042 1.00Total Syst c [Photometry & Calibration] [0.021] [0.50] Low- z [ µ -Bias Correction: survey] [0.023] [0.55] † Low- z σ Cut 0.016 0.38Low- z Volume Limited 0.010 0.24Spectroscopic Efficiency 0.007 0.17 † Flux Err Modeling 0.001 0.02 [ µ -Bias Correction: astrophysical] [0.026 ] [0.62] Intrinsic Scatter Model (G10 vs. C11) 0.014 0.33 † Two σ int C , x Parent Population 0.014 0.33 † w, Ω m in sim. 0.006 0.14MW Extinction 0.005 0.12 [Redshift] [0.012 ] [0.29] Peculiar Velocity 0.007 0.17 † z + 0 . a The sample is DES-SN3YR (DES-SN + low- z sample)plus CMB prior. b Item in [bold] is a sub-group and its uncertainty. c The quadrature sum of all systematic uncertainties doesnot equal 0 .
042 because of redshift-dependent correlationswhen using the full covariance matrix. † Uncertainty was not included in previous analyses.
40% of the DES-SN3YR sample. For previous analyseswith a smaller fraction of low- z events (e.g., Pantheon,JLA) we do not recommend adding the full 0 . w -uncertainty to their results. Cosmology results
CDM
Using DES-SN3YR and assuming a flat ΛCDM model,we find Ω m = 0 . ± . k ) added as a free parameter in Eq. 3(e.g., see Sect 3.1 of Davis & Parkinson 2017) we findthe constraints shown in Fig. 2 and Table 2 (row 2).Solid contours show our result with both statistical andsystematic uncertainties included, while dashed contoursshow the statistical-only uncertainties for comparison.Fig. 2 also shows that the CMB data provide strong flat-ness constraints, consistent with zero curvature; the im-pact of using this CMB prior is shown in row 3. The im-pact from adding a BAO prior is shown in row 4, wherethe evidence ratio R = 110 shows consistency betweenthe SN+CMB and BAO posteriors. Flat w CDM
For our primary result, we use DES-SN3YR with theCMB prior and a flat w CDM model (Ω k = 0) and findΩ m = 0 . ± .
018 and w = − . ± .
059 (Table 2,row 5). Our constraint on w is consistent with thecosmological-constant model for dark energy. The 68%ES: Cosmological results with spectroscopically confirmed type Ia supernovae 7and 95% confidence intervals are given by the red con-tours in Fig. 3, which also shows the contributions fromDES-SN3YR and CMB. We show two contours for DES-SN3YR, with and without systematic uncertainties inorder to demonstrate their impact. In Table 2, row 6,we show the impact of the low-redshift SN sample by re-moving it; the w -uncertainty increases by 25% and theconstraint lies approximately 1 σ from w = − w -value (Table 2, row 7) is shiftedby only 0 . ∼
20% com-pared to our primary result, and the evidence ratio be-tween SN+CMB and BAO is R = 81 showing consistencyamong the data sets. If we remove the low- z SN subset(row 8), the w -uncertainty increases by only ∼ w -uncertainty increases by nearly 50% (row 9). Flat w w a CDM
Our last test is for w evolution using the w w a CDMmodel, where w = w + w a (1 − a ) and a = (1 + z ) − .Combining probes from SNe, CMB, and BAO, we findresults (Table 2, row 10) consistent with a cosmologicalconstant ( w , w a = − ,
0) and a figure of merit (Albrechtet al. 2006) of 45.5. Removing the SN sample increasesthe w and w a uncertainties by a factor of 2 and 1.5,respectively (row 11). Comparison to other SN Ia Surveys/Analyses
The DES-SN3YR result has competitive constrain-ing power given the sample size ( σ w, tot = 0 .
059 with329 total SNe Ia), even after taking into account addi-tional sources of systematic uncertainty. While our DES-SN3YR sample is < / z +HST, σ w, tot = 0 . z subset is 70% the size of Pantheon’s low- z sub-set, and we included five additional sources of system-atic uncertainty, our improvements ( §
1) result in a w -uncertainty that is only × . DISCUSSION AND CONCLUSION
We have presented the first cosmological resultsfrom the DES-SN program: Ω m = 0 . ± . w = − . ± .
059 for a flat w CDM modelafter combining with CMB constraints. These re-sults are consistent with a cosmological constantmodel and demonstrate the high constraining power(per SN) of the DES-SN sample. DES-SN3YR dataproducts used in this analysis are publicly availableat https://des.ncsa.illinois.edu/releases/sn .These products include filter transmissions, redshifts,light curves, host masses, light-curve fit parameters,Hubble Diagram, bias corrections, covariance matrix,MC chains, and code releases.We have utilized the spectroscopically confirmed SN Iasample from the first three years of DES-SN as well as alow-redshift sample. This 3-year sample contains ∼ Fig. 3.—
Constraints on Ω m - w for the flat w CDM model(68% and 95% confidence intervals). SN contours are shownwith only statistical uncertainty (white-dashed) and with totaluncertainty (green-shaded). Constraints from CMB (brown) andDES-SN3YR+CMB combined (red) are also shown.
To benefit from the increased statistics in the 5-yearsample it will be critical to reduce systematic uncertain-ties. We are working to improve calibration with a largesample of DA White Dwarf observations, including twoHST Calspec standards. Other improvements to system-atics are discussed in § TABLE 2Cosmological results a Row SN Sample + Prior (ΛCDM) Ω m Ω Λ DES-SN3YR b +flatness . ± .
038 0 . ± . . ± .
122 0 . ± . c . ± .
042 0 . ± . d . ± .
007 0 . ± . Row
SN Sample + Prior (Flat w CDM) Ω m w DES-SN3YR+CMB R . ± . − . ± . e +CMB 0 . ± . − . ± . . ± . − . ± . . ± . − . ± . . ± . − . ± . Row