Cosmological constraints from the 3rd year SNLS dataset

Transcription

1 IAS Workshop on Dark Energy November 2008 Cosmological constraints from the 3rd year SNLS dataset preliminary results with focus on systematics Julien Guy LPNHE IN2P3/CNRS Univ. Paris VI & VII

2 Systematic Uncertainties : Photometry/Calibration low S/N photometry, sky background/galaxy subtraction uniformity of the detector response across the focal plane flux calibration... Distance Estimate Understanding of the detector response Modeling of the supernovae Evolution with redshift Selection Bias Contamination Evolution of SNe population (age of stellar population, metallicity) Evolution of the properties of the line of sight (dust, lensing)

3 Where is the systematics floor of cosmology with SNe Ia? 20??: SNe (LSST) 2006: 71 SNe (SNLS)?

4 Outline 1) Overview of the SuperNova Legacy Survey 2) What is new with respect to first year analysis : larger statistics 2 photometric pipelines new calibration 2 light curve fitters improved propagation of systematic uncertainties 3) Preliminary results 4) What is coming next

5 the SuperNova Legacy Survey Collaboration : France, Canada, UK, US,... Imaging on Canada France Hawaii Telescope (4 m class), part of CFHTLS Spectroscopy on VLT, Gemini, Keck telescopes (8/10 m class)

6 Paris/Saclay Toronto Victoria R. Carlberg, A. Conley, A. Howell, K. Perrett C. Pritchet, M. Graham, E. Hsiao R. Pain, P. Astier, C. Balland, N.Fourmanoit, J. Guy, D. Hardin, T.Kronborg, M.Mouchet, N. Regnault J. Rich, G. Bazin, N. Palanque, V.Ruhlmann Marseille S. Basa, D. Fouchez, J. Ledu Lisbon A. Mourao, V. Arsenijevic, S. Fabbro LBNL S. Perlmuter, N. Suzuki ESO C. Lidman, S. Baumont Oxford I. Hook, M. Sullivan, E. Walker Full list of collaborators at: The SNLS collaboration

7 SNLS imaging survey 1 SN per galaxy per millennium > need to monitor galaxies simultaneously to get 10 events per month 5 year "rolling search" : Part of CFHTLS Imaging of the same fields 5 times per month in 4 filters 4 fields of 1 square deg each total exposure time ~200 h per field Megacam : 1 square deg. 36 CCDs 2k x 4k = 300 million pixels 1pixel = 0.185'' CFHT: Diameter 3.6 m Mauna Kea, Hawaii (4200 m)

8 SNLS Spectroscopic Survey Identification Redshift determination Detailed studies (UV) Telescopes : VLT Large program (service) 240h in , idem Gemini : 60h/semestre (service) Keck : 30h/an (in one semestre) Total spectro ~ 130 hours/semestre (imaging : ~ 110)

9 Statistics 276 SNe Ia (Ia?) identified with spectroscopy before aug with good light curve sampling 234 selected for cosmology (10 discarded = 4%) First year paper = 71 About 20 months of observations 1/3 of all data

10 Photometry 2 different pipelines with different techniques 1) As in Astier (2006) : simultaneous fit of SN position, fluxes and galaxy geometric alignment with resampling of all images on a reference exposure PSF of reference exposure convolution kernel derivation fit of model on all images simultaneously 2) PSF photometry on subtractions convolution and alignment of high quality (best seeing) deep reference image to all images independently subtraction Difference of magnitudes : PSF photometry on subtractions > 1st technique selected after comparison (more precise, no galactic contamination bias)

11 Systematic Uncertainties : Photometry/Calibration low S/N photometry, sky background/galaxy subtraction uniformity of the detector response across the focal plane flux calibration... Distance Estimate Understanding of the detector response Modeling of the supernovae Evolution with redshift Selection Bias Contamination Evolution of SNe population (age of stellar population, metallicity) Evolution of the properties of the line of sight (dust, lensing)

12 Focal plane non uniformities Dithered observations of dense stellar fields permit to solve for non uniformities of instrumental response Variation of inst. response up to 8% difference Variation of effective wavelength of filter : up to 4 nm difference Agreement with transmission measurements

13 SNLS flux calibration The main difficulty / largest systematic uncertainty Low z SNe calibrated against Landolt UBVRI system > We do the same thing, so we observe Landolt stars... But we need fluxes (not magnitudes) and at <1% accuracy

14 SNLS flux calibration (cont'd) Ratio of distances from ratio of fluxes Need the ratio of fluxes at different wavelength of a reference spectrum For instance : B R colour of reference spectrum Today : based on stellar models of white dwarfs > compatible within 1% with black body calibration (Hayes 1985) Systematics limited today : Uncertainty in (B R) of 0.01 <=> 225 SNe at <z>~0.5

15 SNLS flux calibration (cont'd) 1) Landolt UBVRI > Mecagam griz transformation 2) Use BD+17o4708 for flux calibration instead of Vega secondary HST standard (<0.5% of primary white dwarfs) observed by Landolt same colour range as our observations of Landolt stars SDSS standard Needs small corrections : Milky Way extinction (BD+17 vs other Landolt stars) BD+17 is a binary system Low metallicity star...

16 Systematic Uncertainties : Photometry/Calibration low S/N photometry, sky background/galaxy subtraction uniformity of the detector response across the focal plane flux calibration... Distance Estimate Understanding of the detector response Modeling of the supernovae Evolution with redshift Selection Bias Contamination Evolution of SNe population (age of stellar population, metallicity) Evolution of the properties of the line of sight (dust, lensing)

17 Light curve fitters 2 different implementations with the same physical assumptions Guy 2007 : SALT 2 Conley 2008 : SIFTO The goal is to compare fluxes of SNe at different z, but in the same wavelength range, and the same phase of the supernova light curve Since we observe the SNe every 4 days, and only in a limited number of pass bands, we need a model of the spectral evolution of the SN to 'interpolate' among measurements. z=0 z=0.5

18 Empirical models SN Ia : White dwarf star in a close binary system fed by a companion star exploding when reaching Chandrasekar mass limit. Simulations of SNe Ia explosions are not accurate enough very high resolution 3D simulations needed for flame propagation complicated radiative transfer (geometry, dynamics, composition) One must rely on empirical modeling of the spectral sequence based on the largest possible dataset of spectra and light curves. Complication : Diversity of SNe Ia (light curve duration, composition, kinematics)

19 Light curve fitting / distance estimate In all published high z cosmology analyses : The flux at maximum light integrated in a redshifted B band is the luminosity indicator (this is partly arbitrary) Two other photometric parameters that correlate with luminosity are used to improve distance estimate: a light curve shape :, stretch, X1 a color (B V) > some spectroscopic observables correlate with luminosity but can not be used at high z because of a limited S/N and wavelength range > the CMAGIC technique uses an original color magnitude diagram to estimate distances but it requires high S/N measurements in the 'tail' of the light curve. Different models are used to derive those parameters Their usage to determine distances also differ

20 Light curve fitters MLCS SALT SALT 2 SIFTO GOODS (Riess et al.), ESSENCE (Wood Vasey et al.), SDSS SNLS 1 (Astier et al.), ESSENCE, Union compilation (Kowalski et al.) SNLS 3, SDSS, less parametric, kind of PCA with missing data SNLS 3 MLCS (as used by GOODS and ESSENCE) SALT and SIFTO (as used by SNLS) Directly extract a distance estimate from light curves 2 steps : light curve fitting without distance => need a training set of SNe for which we know a priori the distance information (can use high z SNe for training), distance estimate with a combination of the parameters 1) Apply k corrections : transform photometric measurements to standard rest frame bands 2) Fit corrected light curves to a set of templates Full model of the spectral sequence Consider the (B V) color excess as a measurement of host galaxy extinction The relation between the measured color and the luminosity is fitted at the same time as cosmology. integrated spectrum compared to data > better error propagation

21 Improving light curve fitters using distant SNe (cannot be done easily with a direct distance estimator like MCLS) Using high z SNe permits to model rest frame UV emission without the difficulty of UV calibration from the ground (or need to use space telescopes) Using SNe at various redshifts permits to scan photometrically the wavelength range between instrumental filters (otherwise one relies on interpolation of order of 100nm) Today, the statistics are better at high z Most systematics due to the SN modeling are turned into statistical uncertainties because the accuracy of the model scales with the number of SNe Example : SALT(trained on nearby SNe) vs SALT2(trained on SNLS high z data)

22 Light curve fitters : SALT 2 / SIFTO same physical interpretation almost same training sample different parametrization => both used in SNLS 3 => they give almost same results The choice of parametrization of the empirical model is not a dominant systematic uncertainty

23 Systematic Uncertainties : Photometry/Calibration low S/N photometry, sky background/galaxy subtraction uniformity of the detector response across the focal plane flux calibration... Distance Estimate Understanding of the detector response Modeling of the supernovae Evolution with redshift Selection Bias Contamination Evolution of SNe population (age of stellar population, metallicity) Evolution of the properties of the line of sight (dust, lensing)

24 Spectroscopic identification & redshift determination 2 teams with different techniques cross check their classification Howell et al 2005 Baumont et al arxiv: New methods: > "PHASE" PHotometry Assisted Spectrum Extraction

25 Spectroscopic identification (cont'd) Baumont et al arxiv: New methods: > "PHASE" > Identification based on simultaneous fit of : SN light curves SN spectrum galactic contribution (using templates) 04D1dc z= D4ib z=0.704 > helps discriminate SNe Ia vs Ib/c for late epoch spectra

26 Systematic Uncertainties : Photometry/Calibration low S/N photometry, sky background/galaxy subtraction uniformity of the detector response across the focal plane flux calibration... Distance Estimate Understanding of the detector response Modeling of the supernovae Evolution with redshift Selection Bias Contamination Evolution of SNe population (age of stellar population, metallicity) Evolution of the properties of the line of sight (dust, lensing)

27 SNe evolution with redshift Influence on luminosity of environment : metallicity progenitors with different life time composition of host galaxy dust Ideally : A predictive theoretical model tells what to look for. In practice : Compare photometric and spectroscopic properties at low and high redshifts (limited by low S/N at high z) Compare SNe as a function of the host galaxy properties

28 SNe evolution with redshift No evidence in the photometric properties vs redshift Brighter slower Blue: low z SNe Black: high z SNe Brighter bluer

29 SNe evolution with redshift Same rise time Motivation: change of rise time = change of amount of 56Ni = change of luminosity Riess 1999 found a difference of days between low and high z! low z : / 0.2 high z (SNLS) : / 0.2 (stat) +/ 0.2 (syst)

30 SNe evolution with redshift No evidence in the spectroscopic properties vs redshift CaII (H&K) ejection velocity Equivalent width of SiII (EW) Equivalent width of MgII (EW)

31 SNe evolution with redshift SN properties vs host galaxy properties ~ spirals Star Formation Rate (SFR) (from the colors of galaxies) Stretch factor > SNe in environment where stars are formed have larger stretch factor (But no significant change of distance modulus) > Are there different sub classes of SNe Ia? or a relation between age and stretch? (we don't know)

32 SNe evolution with redshift Statistical tests : => Split SNe Ia sample in sub sample and do several fit of Hubble diagrams => Add nuisance parameters and marginalize over them. For instance, luminosity offsets for SNe in high SFR galaxies redshift dependent stretch, colour correction... => No systematic floor

33 Systematic Uncertainties : Photometry/Calibration low S/N photometry, sky background/galaxy subtraction uniformity of the detector response across the focal plane flux calibration... Distance Estimate Understanding of the detector response Modeling of the supernovae Evolution with redshift Selection Bias Contamination Evolution of SNe population (age of stellar population, metallicity) Evolution of the properties of the line of sight (dust, lensing)

34 Calibration is the largest challenge for the cosmology with SNe Ia for projects like LSST SNDICE : R&D at CFHT also R&D for LSST in France

35 SNLS 3 Propagation of systematic uncertainties Derivative of luminosity, stretch, color w.r.t. all systematics Covariant matrix of systematic uncertainties (not necessarily diagonal) Consider systematics as additional parameters to marginalize => Provide a likelihood to the community including stat and syst.