Constraining Dark Energy with the Supernova Legacy Survey Mark Sullivan University of Toronto

Paris Group Reynald Pain, Pierre Astier, Julien Guy, Nicolas Regnault, Christophe Balland, Delphine Hardin, Jim Rich, + … UK Gemini PI: Isobel Hook, Richard McMahon, + … USA LBL: Saul Perlmutter, + … CIT: Don Neill Full list of collaborators at: Victoria Group Chris Pritchet, Dave Balam, + … Toronto Group Ray Carlberg, Alex Conley, Andy Howell, Kathy Perrett, Mark Sullivan The SNLS collaboration Marseille Group Stephane Basa, Dominique Fouchez, + …

White Dwarf SNe Ia are thermonuclear explosions of C-O white dwarf stars “Standard” nuclear physics Bright: 10 billion suns Standardizable: 7% calibration Brightness and homogeneity make them the best measure of distance, and hence dark energy, in the Universe

Supernova Legacy Survey ( ) Megaprime 5 year survey, goal: 500 distant SNe Ia to measure “w” Uses CFHT/“Megacam” 36 CCDs, good blue response 4 filters for good k-corrections and color measurement

CFHT-LS Organisation CFHT-LS (imaging) – DEEPWIDE Galaxy studies Time sequenced dataset (202n over 5 years) Cosmic shear Clusters SNLS collaboration Data-processing Major Spectroscopic Program Gemini (Canada/UK/USA) 120 hrs/yr (60:40:20) VLT (France/Other Euros) 120 hrs/yr Keck (through LBL) 40 hrs/yr Cosmological analyses Magellan near-IR study (Freedman et al.) Rest-frame I-band Hubble diagram Keck SN Ia UV study (Ellis/Sullivan et al.) LRIS high-S/N - metallicity through UV lines Testing accuracy of k-corrections in the UV SN IIP study (Nugent/Sullivan/Ellis et al.) Using SNe IIP as standard candles Independent Hubble diagram to z=0.5

Supernova Legacy Survey Keck (8 nights/yr) Gemini N & S (120 hr/yr) VLT (120 hr/yr) Magellan (15 nights/yr) Imaging Distances from light-curvesSpectroscopy Redshifts  Distances from cosmological model DiscoveriesLightcurves g’r’i’z’ every 4 days during dark time

k-corrections in SNLS

Making a standard candle Phillipsrelation 1. “Phillips relation”: A correction to SN Ia light-curves based on light- curve shape drastically improves the quality of the standard candle. Time  Brightness  Time  56 Ni  56 Co  56 Fe powers the SN Ia light-curve Conventional Wisdom: SNe are a one-parameter family defined by amount of 56 Ni synthesized in the explosion. More 56 Ni  greater luminosity  higher Temperatures  higher opacity  broader LC

Colour at peak Making a standard candle Phillipsrelation 1. “Phillips relation”: A correction to SN Ia light-curves based on light- curve shape drastically improves the quality of the standard candle. 2. SN colour: A correction to the SN luminosity based on the SN colour Blue Red Fainter 

Making a standard candle Phillipsrelation 1. “Phillips relation”: A correction to SN Ia light-curves based on light- curve shape drastically improves the quality of the standard candle. Brightness  Time  20% 7%! Many methods: Stretch – Perlmutter 97, 99 (M)LCS(2k2) – Riess, 95,96, Jha 07 SALT(2) – Guy 05, 07 SiFTO – Sullivan 07 CMAGIC – Wang et al.; Conley 06 Δ m 15 – Phillips 93; Hamuy 95; Prieto SN colour: A correction to the SN luminosity based on the SN colour

“Local” SN Ia Hubble Diagrams Jha et al Prieto et al Most light-curve fitting techniques fare equally well

Light-curve fit parameters from different fitters are tightly correlated

A Typical SN What we need to measure Peak brightness Lightcurve width (stretch) Colour (c)

SNLS: Current status Survey running for 3.5 years ~310 confirmed distant SNe Ia ( not yet processed) ~ Largest single telescope sample of SNe ~ Largest single telescope sample of SNe “On track” for 500 spectroscopically confirmed SNe Ia by survey end (>1000/>2000 total SNIa/All SN light-curves) “On track” for 500 spectroscopically confirmed SNe Ia by survey end (>1000/>2000 total SNIa/All SN light-curves)

“Rolling” light-curves

First-Year SNLS Hubble Diagram SNLS 1 st year Ω M = ± (stat) ± (sys) =-1.02 ± 0.09 (stat) ± (sys) Astier et al citations (187 in refereed journals)

“Third year” SNLS Hubble Diagram (preliminary) Preliminary 3/5 years of SNLS ~240 distant SNe Ia Independent analysis to 1 st year: Different calibration route Different photometric methods Different SN light-curve analysis tools Sullivan et al. 2007

“Third year” SNLS Hubble Diagram (preliminary) Ω M =0.3, Ω λ =0 Ω M =1.0, Ω λ =0 Best-fit for SNLS+flatness Preliminary (error was in A06) Sullivan et al. 2007

Cosmological Constraints (Preliminary) SNLS+BAO (No flatness)SNLS + BAO + simple WMAP + Flat BAO SNe WMAP-3 6-7% measure of Sullivan et al. 2007

Future Prospects with SNLS Current constraints on : =-1 to ~6-7% (stat) >-0.8 excluded at 3-sigma level >-0.8 excluded at 3-sigma level At survey end a 4-5% statistical measure will be achieved: 500 SNLS + 200(?) SDSS + new local samples 500 SNLS + 200(?) SDSS + new local samples Improved external constraints (BAO, WMAP, WL) Improved external constraints (BAO, WMAP, WL) Systematic errors becoming ever more important

Potential SN Systematics in measuring w(a) More “mundane” More “scientifically interesting” “Experimental Systematics” Calibration, photometry, Malmquist-type effects Calibration, photometry, Malmquist-type effects Contamination by non-SNe Ia Minimized by spectroscopic confirmation Minimized by spectroscopic confirmationK-corrections UV uncertain; “golden” redshifts; spectral evolution? UV uncertain; “golden” redshifts; spectral evolution? Non-SNe systematics Peculiar velocities; Hubble Bubble; Weak lensing Peculiar velocities; Hubble Bubble; Weak lensingExtinction Effective R B ; Dust evolution Effective R B ; Dust evolution Redshift evolution in the mix of SNe “Population drift” – environment? “Population drift” – environment? Evolution in SN properties Light-curves/Colors/Luminosities Light-curves/Colors/Luminosities

Hubble Bubble Latest MLCS2k2 paper (Jha 2007) MLCS2k2 attempts to separate intrinsic colour-luminosity and reddening MLCS2k2 attempts to separate intrinsic colour-luminosity and reddening 3σ decrease in Hubble constant at ≈7400 km/sec – local value of H 0 high; distant SNe too faint Local void in mass density? Could have significant effects on w measurement MLCS2k2 SALT No Bubble with other light-curve fitters! Conley et al. (2007)

Light-curve fit parameters from different fitters are tightly correlated

Handling colour in SN Ia Colour is the most important correction to SN Ia luminosities Underlying physics: Redder SNe Ia are fainter due to Extinction along the line of sight Extinction along the line of sight Intrinsic luminosity/colour relationship of the SN population Intrinsic luminosity/colour relationship of the SN population Two basic approaches: Attempt to identify intrinsic relationship and assume standard dust dominates the rest (Jha et al.) Attempt to identify intrinsic relationship and assume standard dust dominates the rest (Jha et al.) Fit a luminosity/colour relationship empirically on the SN data Fit a luminosity/colour relationship empirically on the SN data β=4.1

“Bubble” significance versus “β” Standard Dust: β ~ 4.1 Observed: β ~ 2 Conley et al. (2007)

All fitters agree: β<4.1 Conley et al. (2007)

What does β=2 (R V =1) mean? Very strange dust? But R V =1 is not seen anywhere in the Milky Way Dust around SNe is changed by the explosion? Most likely: SNe have an (as yet) uncorrected-for intrinsic colour-luminosity relationship While fitting empirically for β may be empirical, currently it’s the best way

Potential SN Systematics in measuring w(a) More “mundane” More “scientifically interesting” “Experimental Systematics” Calibration, photometry, Malmquist-type effects Calibration, photometry, Malmquist-type effects Contamination by non-SNe Ia Minimized by spectroscopic confirmation Minimized by spectroscopic confirmationK-corrections UV uncertain; “golden” redshifts; spectral evolution? UV uncertain; “golden” redshifts; spectral evolution? Non-SNe systematics Peculiar velocities; Hubble Bubble; Weak lensing Peculiar velocities; Hubble Bubble; Weak lensingExtinction Effective R B ; Dust evolution Effective R B ; Dust evolution Redshift evolution in the mix of SNe “Population drift” – environment? “Population drift” – environment? Evolution in SN properties Light-curves/Colors/Luminosities Light-curves/Colors/Luminosities

“Experimental Systematics” Calibration, photometry, Malmquist-type effects Calibration, photometry, Malmquist-type effects Contamination by non-SNe Ia Minimized by spectroscopic confirmation Minimized by spectroscopic confirmationK-corrections UV uncertain; “golden” redshifts; spectral evolution? UV uncertain; “golden” redshifts; spectral evolution? Non-SNe systematics Peculiar velocities; Hubble Bubble; Weak lensing Peculiar velocities; Hubble Bubble; Weak lensingExtinction Effective R B ; Dust evolution Effective R B ; Dust evolution Redshift evolution in the mix of SNe “Population drift” – environment? “Population drift” – environment? Evolution in SN properties Light-curves/Colors/Luminosities Light-curves/Colors/Luminosities Potential SN Systematics in measuring w(a) More “mundane” More “scientifically interesting” “Population Evolution”

? White Dwarf Many uncertainties: Nature of progenitor system – the “second star” Nature of progenitor system – the “second star” Single versus double degenerate? Single versus double degenerate? Young versus old progenitor? Young versus old progenitor? Explosion mechanism? Explosion mechanism? Effect of progenitor metallicity on luminosity? Effect of progenitor metallicity on luminosity?

Host galaxies impact SN properties Some evidence that SNe Ia in ellipticals show smaller scatter Sullivan et al. (2003) e.g. Hamuy et al. (2000) SN Ia Light-curve shape depends on morphology Low stretchHigh stretch

PassivePassive Star-formingStar-forming StarburstingStarbursting  Little morphological information available  CFHT u*g’r’i’z’ imaging via the Legacy program.  PEGASE2 is used to fit SED templates to the optical data.  Recent star-formation rate, total stellar mass, mean age are estimated.  Hosts classified according to physical parameters instead of “what they look like”.  Little morphological information available  CFHT u*g’r’i’z’ imaging via the Legacy program.  PEGASE2 is used to fit SED templates to the optical data.  Recent star-formation rate, total stellar mass, mean age are estimated.  Hosts classified according to physical parameters instead of “what they look like”. Typing of SNLS SN Ia hosts Sullivan et al. (2006) u g r i z

SNLS: SN rate as a function of sSFR SN Ia hosts classified by star- formation activity Per unit stellar mass, SNe are at least an order of magnitude more common in more vigorously star- forming galaxies SNLS “passive” galaxies

SNLS selection of hosts D2 ACS imaging Plenty of irregular/late- type systems Few genuine ellipiticals

SN Ia Stretch dependencies 170 SNe Ia (Update from Sullivan et al. 2006; better zeropoints, host photometry, more SNe) Passive Star-forming Stretch versus mean age Stretch by galaxy star- formation activity The majority of SN Ia come from young stellar populations

Recent SNLS evidence for two components for SNe Ia Older progenitor SNe Empirically, SN Rate is proportional to galaxy mass Preferentially found in old stellar environments Typically fainter with faster light-curves (low stretch) Younger progenitor SNe Empirically, SN rate is proportional to galaxy star formation rate Exclusively found in later type star- forming galaxies Typically brighter with slower light- curves (high stretch) (Extreme example: SNLS-03D3bb?)

Recent SNLS evidence for two components for SNe Ia Older progenitor SNe Empirically, SN Rate is proportional to galaxy mass Preferentially found in old stellar environments Typically fainter with faster light-curves (low stretch) Younger progenitor SNe Empirically, SN rate is proportional to galaxy star formation rate Exclusively found in later type star- forming galaxies Typically brighter with slower light- curves (high stretch) (Extreme example: SNLS-03D3bb?) Could evolution between the two components with redshift distort the dark energy signal?

SN population drift? Relative mix of evolves with redshift A+B predictions, but similar for any two component model Sullivan et al. 2006

Stretch versus redshift (3 rd year) ?

Evolution in Stretch? Gaussians – predicted evolution from A+B model Average stretch, and thus average intrinsic brightness of SNe Ia evolves with redshift but if stretch correction works perfectly, this should not affect cosmology Howell et al Nearby z<0.75 z>1

Effect on cosmology Extreme case using SNLS 1 st year Use only s 1 SNe at z>0.4 Effects of evolution smaller than error budget for determination of, must be studied closely to determine the effect on measuring w(a) SNLS yr 1 N: 115  = 1.6  = 1.8 w =  0.09  M = 0.27  0.02 Yr 1 s split N: 56  = 1.4  = 1.8 w =  0.15  M = 0.28  0.03

Split by s (preliminary) S ΩMΩMΩMΩM<w>αβ <0.95 >0.95

Stretch correction across environments Conley et al No evidence for gross differences between light- curves in passive and active galaxies Rest-frame B composite light- curve

“Morphological” Hubble diagram

SN Subsets Problems: Low-redshift sample very small, Malmquist correction likely to be different Passive Star-forming α=1.34 ± 0.25 β=2.52 ± 0.2 σ~0.10 mag =-0.88±0.11 α=1.19 ± 0.15 β=2.71 ± 0.2 σ~0.140 mag =-1.04±0.08 Split by host galaxy Preliminary

Do SNe Ia Evolve? UV Spectrum Probes Metallicity Lentz et al. (2000) Varying metallicity changes line blanketing in the UV

High S/N spectra of SNLS SNe Ellis, Sullivan et al. 2007

Light-curve width dependence Implications for JDEM: k-corrections must improve in accuracy

Summary SNLS is a well-controlled, calibrated and understood experiment Current SN Ia measurement determine to ~ 6-7% SNLS is the best cosmological SN Ia dataset available SNLS is the best cosmological SN Ia dataset available In 2-3 years, 4-5% will be achieved Colour is the critical correction to SN Ia distances; currently can only be handled empirically SNe Ia have a range of progenitor ages Impacts on light-curve shape: faster/older & slower/younger Impacts on light-curve shape: faster/older & slower/younger SNe in passive galaxies are better standard candles? More homogeneous stellar population? Less dust? More homogeneous stellar population? Less dust?