Supernova Simulations and Strategies For the Dark Energy Survey J. P. Bernstein1, R. Kessler2,3, S. Kuhlmann1, R. Biswas1, E. Kovacs1, G. Aldering4, I. Crane1,5, C. B. D’Andrea6, D. A. Finley7, J. A. Frieman2,3,7, T. Hufford1, M. J. Jarvis8,9, A. G. Kim4, J. Marriner7, P. Mukherjee10, R. C. Nichol6, P. Nugent4, D. Parkinson10, R. R. R. Reis7,11, M. Sako12, H. Spinka1, M. Sullivan13 ABSTRACT We present an analysis of supernova light curves simulated for the upcoming Dark Energy Survey (DES) supernova search. The simulations employ a code suite that generates and fits realistic light curves in order to obtain distance modulus/redshift pairs that are passed to a cosmology fitter. We investigated several different survey strategies including field selection, supernova selection biases, and photometric redshift measurements. Using the results of this study, we chose a 30 square degree search area in the griz filter set. We forecast 1) that this survey will provide a homogeneous sample of up to 4000 Type Ia supernovae in the redshift range 0.05<z<1.2, and 2) that the increased red efficiency of the DES camera will significantly improve high-redshift color measurements. The redshift of each supernova with an identified host galaxy will be obtained from spectroscopic observations of the host. A supernova spectrum will be obtained for a subset of the sample, which will be utilized for control studies. In addition, we have investigated the use of combined photometric redshifts taking into account data from both the host and supernova. We have investigated and estimated the likely contamination from core-collapse supernovae based on photometric identification, and have found that a Type Ia supernova sample purity of up to 98% is obtainable given specific assumptions. Furthermore, we present systematic uncertainties due to sample purity, photometric calibration, dust extinction priors, filter-centroid shifts, and inter-calibration. We conclude by estimating the uncertainty on the cosmological parameters that will be measured from the DES supernova data. Subject headings: supernovae – cosmology: simulations 1Argonne National Laboratory, 9700 South Cass Av- Portsmouth PO1 3FX, UK enue, Lemont, IL 60439, USA 7Center for Particle Astrophysics, Fermi National Accel- 2Kavli Institute for Cosmological Physics, The Univer- erator Laboratory, P.O. Box 500, Batavia, IL 60510, USA sity of Chicago, 5640 South Ellis Avenue Chicago, IL 60637, 8Centre for Astrophysics, Science & Technology Re- USA search Institute, University of Hertfordshire, Hatfield, 3Department of Astronomy and Astrophysics, The Uni- Herts, AL10 9AB, UK versity of Chicago, 5640 South Ellis Avenue Chicago, IL 9Physics Department, University of the Western Cape, 60637, USA Cape Town, 7535, South Africa 4 E. O. Lawrence Berkeley National Laboratory, 1 Cy- 10Department of Physics and Astronomy, Pevensey 2 clotron Rd., Berkeley, CA 94720, USA Building University of Sussex, Falmer Brighton BN1 9QH, 5Department of Physics, University of Illinois at UK Urbana-Champaign, 1110 West Green Street, Urbana, IL 11Now at: Instituto de F´ısica, Universidade Federal do 61801-3080 USA Rio de Janeiro C. P. 68528, CEP 21941-972, Rio de Janeiro, 6Institute of Cosmology and Gravitation, University RJ, Brazil of Portsmouth, Dennis Sciama Building, Burnaby Road, 12Department of Physics and Astronomy, University of 1 Contents 8 Dark energy constraints from differ- ent survey Strategies 30 1 Introduction 2 8.1 FigureofMerit . 30 8.2 Systematic uncertainties . 34 2 Supernova light curve simulation 4 2.1 SNANA ................ 5 9 Discussion and summary 36 2.2 Simulation inputs . 5 A Fractions of SNIa Host Galaxies Sat- 3 Survey strategy options and example isfying Apparent-Magnitude Limits 39 simulations 7 3.1 Fields, filters, and selection cuts . 8 B SNe systematic uncertainties in the 3.2 Light curves and SN statistics . 10 FoM calculation 42 B.1 NuisanceParameters . 43 4 Redshift Determination 15 4.1 Role of Each Method of Redshift 1. Introduction Determination . 15 In the late 1990’s, observations of distant Type 4.2 Accuracy of photometric redshifts . 16 Ia supernovae (SNIa) provided the convincing ev- 4.3 Photometric redshifts for hosts idence for the acceleration of cosmic expansion withoutspectra . 17 (Riess et al. 1998; Perlmutter et al. 1999). Dedi- cated supernova (SN) surveys covering cosmolog- 5 Supernova analysis with spectro- ically relevant redshifts, such as the ESSENCE scopic redshifts 17 Supernova Survey (Miknaitis et al. 2007; Foley 5.1 MLCS2k2 light curve fitting with full et al. 2009), Supernova Legacy Survey (SNLS, priors ................ 17 Astier et al. 2006; Conley et al. 2011), Sloan Dig- 5.2 MLCS2k2 light curve fitting with flat ital Sky Survey-II Supernova Survey (SDSS, Frie- priors & SALT2 fitting . 19 man et al. 2008b; Sako et al. 2011), Carnegie Su- pernova Project (Hamuy et al. 2006; Stritzinger 6 Type Ia supernova sample purity 21 et al. 2011), Stockholm VIMOS Supernova Survey II (Melinder et al. 2011), and Hubble Space Tele- 6.1 Corecollapseinputrate . 21 scope searches (e.g., Strolger et al. 2004; Dawson 6.2 Relative fractions of core collapse et al. 2009; Amanullah et al. 2010), have substan- types................. 22 tially improved the quantity and quality of SNIa 6.3 Corecollapsebrightness . 22 data in the last decade. A previously unknown 6.4 Corecollapsetemplates . 22 energy-density component known as dark energy is 6.5 Sample purity results . 23 the most common explanation for cosmic accelera- tion (for a review, see Frieman et al. 2008a; Wein- 7 Supernova colors, dust extinction, berg et al. 2012). The recent SN data, in combina- and infrared data 26 tion with measurements of the cosmic microwave background (CMB) anisotropy and baryon acous- 7.1 Sensitivity to A and R ..... 26 V V tic oscillations (BAO), have confirmed and con- 7.2 VIDEO survey and additional in- strained accelerated expansion in terms of the the fraredoverlap . 27 relative dark energy density (ΩDE) and equation 7.2.1 The DES+VIDEO overlap . 27 of state parameter (w pDE/ρDE, where pDE ≡ 7.2.2 The DES+VIDEO super- and ρDE are the pressure and density of dark en- novasample. 27 ergy, respectively). The next generation of cosmo- logical surveys is designed to improve the measure- Pennsylvania, 203 South 33rd Street, Philadelphia, PA ment of w, and constrain its variation with time, 19104, USA from observations of the most powerful probes of 13 Department of Physics, Denys Wilkinson Building, Ox- dark energy as suggested by the Dark Energy Task ford University, Keble Road, Oxford, OX1 3RH, UK 2 Force (Albrecht et al. 2006): SNe, BAO, weak lensing, and galaxy clusters. Future SN surveys face common issues, includ- ing the number and position of fields, filters, expo- sure times, cadence, and spectroscopic and photo- metric redshifts. Each study must optimize tele- scope allocations to return the best cosmological constraints. The simulation analysis presented in the paper is for the Dark Energy Survey14 (DES), which expects to see first-light in 2012. The DES will carry out a deep optical and near-infrared sur- Fig. 1.—: The DES footprint. The white squares vey of 5000 square degrees of the South Galac- indicate the locations of our current choice of five tic Cap (see Fig. 1) using a new 3 deg2 Charge SN fields (see 3.1). For the survey strategies Coupled Device (CCD) camera (the Dark Energy considered in this§ analysis with additional shal- Camera, or “DECam,” Flaugher et al. 2010) to be low fields, those fields are placed next to these five mounted on the Blanco 4-meter telescope at the fields. The size of the squares as shown is much Cerro Tololo Inter-American Observatory (CTIO). larger than the 3 deg2 field of view of DECam in The DES SN component will use approximately order to make them easier to see in this Figure. 10% of the total survey time during photomet- The scale shows the log of r-band (as defined in ric conditions and make maximal use of the non- 3.1) Galactic extinction in magnitudes. photometric time, for a total SN survey of 1300 § hours. The DECam focal plane detectors (Estrada∼ et al. 2010) are thick CCDs from Lawrence Berke- tage of multi-object spectrograph capabilities to ley National Laboratory (LBNL), which are char- obtain many spectra at once. Photometric red- acterized by much improved red sensitivity rel- shifts can also be obtained using deep co-added ative to conventional CCDs (see Fig. 2, as well photometry of the host galaxy, but the redshift as Holland 2002; Groom et al. 2006; Diehl et al. accuracy is degraded, reducing the usefulness of 2008). This will allow for deeper measurements the SN for cosmological measurements. The exist- in the redder bands, which is of particular impor- ing SNIa samples from previous surveys include a tance for high-redshift SNe. This effect is shown in subset of SNe with measured spectra consisting of Fig. 3, which plots simulated scatter in the SALT2 1000 SNIa spread out over many surveys (Sulli- ∼ (Guy et al. 2007) SN color parameter (see 5.2) van et al. 2011; Amanullah et al. 2010, and refer- for the SDSS, SNLS, and DES. Note, in particu-§ ences therein), and the remainder includes many lar, the superior high-redshift color measurements more SNe with host spectra or host and/or SN in the DES deep fields (see 3.1). Details of the photometric redshifts. The usefulness of the cur- simulation method can be found§ in 2. The im- rent photometric samples depends on the fraction plementation for the DES, e.g., an exposure§ time of host galaxies that will be followed-up, a num- of approximately an hour in the SDSS-like z pass- ber which is uncertain. The DES will identify up band per field per observation, is discussed in 3.