Supernova Surveys. Julien GUY PPC2010, Torino, July LPNHE - IN2P3 - CNRS - Universités Paris 6 et Paris 7

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1 Supernova Surveys Julien GUY LPNHE - IN2P3 - CNRS - Universités Paris 6 et Paris 7 PPC2010, Torino, July 2010

2 Outline 1 Cosmology wit Type Ia Supernovae 2 SN surveys SDSS Union2 SNLS 3 Tests of SNe evolution

3 Usage of standard candles for cosmology 1/2 The metric for a homogeneous and isotropic universe + general relativity Friedman-Lemaitre-Robertson-Walker equations ( ) 2 dr/dt H = 8πG R 3 ρ M + Λ/3 k R 2 which links the expansion rate of the universe to its content and spatial curvature. With light as a messenger, we have four observables connected to the expansion history. the redshift z = R 0 /R emission 1 fluxes of objects of known luminosity luminosity distance d L (z) = (1 + z)r(z) angular size of objects of known physical size angular distance d a (z) = r(z)/(1 + z) counts of objects of known number density volume element ( where r(z) = c Ωk S dz ) k Ωk H, Ωk = k (R 0H 0) 2

4 Usage of standard candles for cosmology 2/2 flux = L/4πd 2 L Both low z and distant candles needed to distinguish models Parameters degeneracy: standard candles at 0 < z < 1 measure with precision a singe parameter.

5 Type Ia Supernova (SN Ia) very bright : sun luminosity rare : about one per galaxy per millennium light curve duration : 1 month peak brightness dispersion 40% precision on distance modulus 0.15 identified by spectroscopy (broad absorption features give ejecta velocity and chemical composition)

6 SN Ia simulations Thermonuclear explosion of C+O white dwarf fed by companion star State of the art: Kasen (2009) reproduces brighter-slower and brighter-bluer relations good qualitative description of the observations but not precise enough for usage as fitter to estimate distances

7 Measuring Luminosity Distances with SNe Ia Distance modulus µ = 5 log 10 D L estimated using apparent magnitude in a rest-frame (or redshifted) filter + correction factors based on the shape of the SN light curve and its color µ B = m B M B + α shape β color M B, α and β fitted at the same time as cosmology. c -1 H 0 ) - β c - 5 log 10 (d l µ B stretch brighter-slower relation c -1 H 0 ) + α (s-1) - 5 log 10 (d l µ B color brighter-bluer relation

8 Measuring Luminosity Distances with SNe Ia SNLS-04D3fk g M mb, shape and color determined from observed SNe light curves in a limited set of filters Requires a model of the SN spectral evolution to correct for redshift effect (k-corrections) Flux 0 flux JD g r i z z = 0.25 i M r M z M f (z 1, T rest ) f (z 2, T rest ) = ( ) dl (z 2 ) 2 d L (z 1 ) flux z = λ (Å)

9 Hubble diagram and cosmological constraints

10 Dark Energy? SNe+BAO(+CMB) w 1 ± 0.1(sys.) p = wρ w < 1/3 ( ρ(z) exp 3 w(z) + 1 ) dz 1 + z w = 1 cosmological constant w > 1 scalar fields w < 1 exotic fields

11 Outline 1 Cosmology wit Type Ia Supernovae 2 SN surveys SDSS Union2 SNLS 3 Tests of SNe evolution

12 Increasing SN samples (1/2) Recent SN photometric samples: Carnegie sample (I-band diagram) ( 50, Freedman 2009, Folatelli 2010) SDSS-II ( 100, Kessler 2009) ESSENCE ( 60,Wood-Vasey 2007) HST ( 30,Riess 2007) SNLS, SNLS-1: ( 70,Astier 2006), SNLS-3 ( 230, in prep) Recent compilations: Union2 sample (inc. 6 new SNe) (Amanullah, 2010) Constitution sample (CfA3) (Hicken, 2009) Union sample (Kowalski, 2008)

13 Increasing SN samples (1/2) Spectroscopic samples: SNLS-3 (Balland 2009; Bronder 2008; Howell 2005) ESSENCE-4 years (Foley 2009) CfA low-z sample (Matheson 2009) Future high-z samples: Pan-Starrs DES (Dark Energy Survey) LSST Future low-z samples: Palomar Transient Factory SNFactory SkyMapper...

14 Cosmology analysis: 3 examples SDSS (Kessler 2009) Union-2 compilation SNLS-3

15 SDSS The survey Intermediate SN search 0.05 < z < 0.4 On the SDSS 2.5-m telescope Rolling search: repeated scans of a 2.5 x 120 deg2 equatorial stripe. (u)gri(z) lightcurves, 500 spectroscopically confirmed SNe Ia ( 100 during the first year).

16 SDSS Data (Kessler et al, 2009) Large combined data sample Analysis performed with two LC fitters: MLCS2k2 (Jha et al, 07) SALT2 (Guy et al, 07) thorough comparison of the two lightcurve fitters / distance estimators.

17 SDSS Results Different results when using two different light curve fitters / distance estimators. Using SALT2 Using MLCS2k2 w = 0.96 ± 0.06(stat) ± 0.12(sys) w = 0.76 ± 0.07(stat) ± 0.11(sys)

18 SALT2 vs MLCS2k2 Distance estimate SALT2 Fitted along with the cosmology (no distance information in the model) MLCS2k2 Directly from the model (trained on SNe with known distances) Training sample Nearby & distant SNe (helps to model rest-frame UV) Well measured nearby SNe, with known distances. SNe colors No assumption on the nature of the color-luminosity relation. Additional color variation reddening by dust. Prior on color. k-corrections Built in the model. External k-corrections applied to the data.

19 MLCS2k2 vs SALT2: rest-frame U 1/2 Rest-frame U-band information has a strong impact on distance estimates. Can be tested by comparing distances including or not restframe UV information. Calibration offsets SNLS/SDSS/Nearby SNe (problem with the observer-frame U-band?).

20 MLCS2k2 vs SALT2: rest-frame U 2/2 SNLS-3 example : Color(g M, r M ) Color(r m i M ) no visible effect as a function of z.

21 MLCS2k2 vs SALT2: SNe colors SALT2: Either unusual dust or intrinsic SN property In SALT2, both the color law and the color luminosity relation are fit on the data. Either unusual dust (e.g. Goobar 2009) or intrinsic SN property The effective reddening law for SNe does not follow the standard dust extinction law (Cardelli law) c -1 H 0 ) + α (s-1) - 5 log 10 (d l µ B color For SNe Ia the total to selective extinction ratio R B 3 < 4.1.

22 MLCS2k2 vs SALT2: SNe colors MLCS2k2: Prior on SNe colors MLCS2K2 assumes the color variability (beyond correlation with shape) is due to extinction by dust prior to ensure A V = R V E(B V ) > 0. can lead to a redshift dependent bias. strong effect on the cosmological parameter determination. Explains about half of the discrepancies between 2 analysis using the same MLCS technique (Kessler 2009 vs Wood-Vasey 2007).

23 MLCS2k2 vs SALT2: conclusion Kessler (2009) finds that the discrepancy MLCS2k2/SALT2 is due to two effects: - difference of models in rest-frame U - difference of color treatment (prior or no prior) SALT2, trained on numerous well calibrated high-z SNe is more reliable in U. The prior on color imposed in MLCS2k2 is not well justified.

24 Outline 1 Cosmology wit Type Ia Supernovae 2 SN surveys SDSS Union2 SNLS 3 Tests of SNe evolution

25 Union-2 Amanullah new HST SNe at 0.5 < z < 1.1 discovered in 2001 compilation of a large SNe sample (557, however incl. SNe of poor quality) consistent fit of all SNe with SALT2 (discard MLCS2k2 for the reasons discussed above) approximate treatment of systematics (ignore z-dependence inside surveys). inc. systematics in χ 2 (and contours).

26 Union-2: results inc. systematics Amanullah 2010 for a flat universe: w = with curvature: w = (stat) (stat) (stat + sys) (stat + sys)

27 Union-2: w 0 /w a, w(z) Amanullah 2010 w(z) = w 0 + w a z 1+z

28 Outline 1 Cosmology wit Type Ia Supernovae 2 SN surveys SDSS Union2 SNLS 3 Tests of SNe evolution

29 SNLS: A Large Photometric Survey h / year on a 3.6-m Hawaii MegaCam: Wide Field Camera 1 deg 2, 36 2k 4k CCDs Good PSF sampling 1 pix = 0.2 Excellent image quality: 0.7 (FWHM) Rolling search mode Part of CFHTLS, 40 nights/year for 5 years Four 1-deg 2 fields repeated obs. (3-4 nights) in griz bands

30 SNLS:... and a Large Spectroscopic Survey Goals spectral identification of SNe Ia (z < 1) redshift determination (host galaxy lines) complementary programs detailed studies of SNe Ia Telescopes VLT large program (120h / year) Gemini (120h / year) Keck (30h / year) (Howell et al, 2005 ApJ 634, 1190)

31 SNLS: Statistics Public list of candidates: May 2008 Telescope SNIa (/?) SNII (/?) Total SN (/?) Other Total Gemini Keck VLT Total Identified Type Ia Supernovae now on disk similar number with photometric identification Survey ended in June 2008

32 SNLS: Statistics Public list of candidates: May 2008 Telescope SNIa (/?) SNII (/?) Total SN (/?) Other Total Gemini Keck VLT Total Identified Type Ia Supernovae now on disk similar number with photometric identification Survey ended in June 2008

33 SNLS 1st 3 Year Analysis 1st Year analysis (Astier 2006): w = ± (stat) ± (sys) for a flat cosmology. Syst. uncertainty on w dominated by calibration. 3 Year analysis : w ,7 % 95 % 68 % BAO (SDSS) Ω M +Ω X =1 st SNLS 1 Year Ω M Statistics: Independant analyses (Fr, Ca), being carefully cross-checked Improved photometric calibration Improved SN fitters trained on the SNLS data (two fitters SALT2 & SiFTO) Detailed studies of the SN properties w.r.t. host galaxy type Systematics included in the cosmological fits

34 SNLS3: Photometric Calibration Uncertainty Budget, Regnault et al. (2009) g r i z Zero Points (stat) ±0.002 ±0.002 ±0.002 ±0.005 Aperture corr. < < < < Background sub < < ±0.005 < Shutter ±0.002 ±0.002 ±0.002 ±0.002 Linearity < < < < nd order airmass corr. < < < < Grid reference colors < < < < Grid color corrs < < ±0.002 < Landolt catalogs ±0.001 ±0.001 ±0.001 ±0.002 Magnitudes of BD +17 ±0.002 ±0.004 ±0.003 ±0.018 Transfer to SNe ±0.002 ±0.002 ±0.002 ±0.002 Total ±0.005 ±0.006 ±0.007 ±0.019

35 SNLS3: SALT2 & SiFTO (Guy et al, 2007), (Conley et al, 2008) Two methods are used. Differences provide an estimate of systematics. SALT2 Empirical model of the Spectral Sequence PCA F = x 0 [M 0 (p, λ) + x 1 M 1 (p, λ)] exp(c CL(λ)) SiFTO SN Ia spectral sequence from (Hsiao, 2007) Pure stretching with time : M(p, λ, s) = M(p/(s 1), λ) s s(λ) Color relations Both fitters trained using nearby and SNLS lightcurves (lightcurve fit separate from distance estimate).

36 SNLS3: SALT2 vs. SiFTO (Guy et al, accepted) 0.1 SALT2 B SIFTO B SN Redshift Systematics on rest-frame magnitudes 0.02

37 SNLS3: SALT2 vs. SiFTO (Conley et al, in prep.)

38 SNLS3: Uncertainties on SNe distances Uncertainties on µ z=0.2 statistical uncertainty calibration finite training sample residual scatter model Light curve fitter 0.02

39 SNLS3: External data sample (Conley et al, in prep) Sample Redshift range N SNe Ref. Low-z Hamuy (1996), Riess (1999), Jha (2006), Hicken (2009)... SDSS Holzman (2009) SNLS HST Riess 2007 More systematic uncertainties for each survey: calibration survey incompleteness (Malmquist bias)

40 SNLS3: Hubble diagram

41 SNLS3: Other systematics (other than light curve fitter, calibration, selection) Peculiar velocities for low-z SNe Contamination by Core collapse SNe for high-z SNe Evolution of color-luminosity relation with redshift Evolution of SNe with z : age of stellar population or metallicity Gravitational magnification - about 200 different systematics (S k ) identified. - Conversion of those systematics into a covariance matrix of SNe distance moduli (µ i ) C sys,ij = µ i µ j k S k S k ( S k ) 2

42 SNLS3 Results: Constraints in Ω M w plane, for Ω k = 0 stat. only

43 SNLS3 Results: Constraints in Ω M w plane, for Ω k = 0 stat. + syst.

44 SNLS3 Results: Constraints on (constant) w Contribution of individual systematics For Ω M = 0.27, w = ± (stat only) (typo in table above) and w = 1.08 ± 0.10 (stat+syst) Dominant systematic uncertainties: calibration, SN vs host galaxy properties

45 Outline 1 Cosmology wit Type Ia Supernovae 2 SN surveys SDSS Union2 SNLS 3 Tests of SNe evolution

46 Evolution Tests We know what SNe Ia are: we are able to simulate explosions in qualitative agreement with observations. However, we don t know exactly what are the companions that feed the white dwarf. Potential sources of evolution: Different progenitors types with different lifetimes One single progenitor type with correlation between lifetime and luminosity Luminosity vs Metallicity Change of dust amount and properties Two kinds of evolution tests: Compare low- and high-redshift events (Bronder et al. 2007) More sensitive: compare events at similar redshifts as a function of their host galaxy properties (star formation rate, metallicity).

47 Evolution test: Comparison of low- and high-redshift events µ c -1 B - 5 log 10 (d l H 0 ) - β c stretch µ c -1 B - 5 log 10 (d l H 0 ) + α (s-1) color brighter-slower relation brighter-bluer relation

48 Evolution of the color-luminosity relation µ B = m B M B + α shape β color Kessler (2009) finds an evolution of β with redshift. With an improved modeling and a better treatment of uncertainties: no clear evidence. SDSS (Kessler 2009) SNLS3

49 Evolution test: Spectra vs z Balland (2009) SNLS VLT spectra compared in two redshift bins.

50 Evolution test: Standardization Parameters vs. Host Galaxy Type Sullivan (2010) SNe in massive hosts (mostly passive ellipticals) have smaller stretch factors. They appear brighter (after correction for stretch and color relations) at 4 σ. similar result found on low-z sample (Kelly 2010) and by SDSS (Lampeitl 2010) we account for this in SNLS3 cosmology fits future surveys will need to do the same (if signal is confirmed)

51 Evolution test: Standardization Parameters vs. Host Galaxy Type Comparison with models Galaxy mass correlates with metallicity (bigger=older). observations: brighter SNe Ia in metal-rich galaxies after lightcurve shape correction theory: brigher SNe Ia in metal-poor galaxies Kasen 2009: both brightness and lightcurve width decrease with metallicity Effect modifies zeropoint of width luminosity relation.

52 Conclusion 1/2 growing low and high z SNe samples more are coming (Pan-Starrs, DES, LSST...) uncertainty on w ( ±0.1) dominated by systematics, mostly calibration SNe simulations become more and more precise Evidence for some evolution via correlations with host properties We can correct for this with host galaxy photometry

53 Conclusion 2/2 For the future, we need : Very precise calibration near IR observations of SNe at high-z Photometric identification for most high-z surveys. Informations on hosts (+ other science : SNe as a probe of IGM, gravitational magnification)

54 Impact of calibration