1. Dark energy in the Supernova Legacy Survey Mark Sullivan (University of Toronto) http://legacy.astro.utoronto.ca/

2. Toronto Group Ray Carlberg, Mark Sullivan, Andy Howell, Kathy Perrett, Alex Conley French Group Reynald Pain (PI), Pierre Astier, Julien Guy, Nicolas Regnault, Jim Rich, Stephane Basa, Dominique Fouchez UK Gemini PI: Isobel Hook + Justin Bronder, Richard McMahon, Nic Walton Victoria Group Chris Pritchet, Don Neill, Dave Balam USA LBL: Saul Perlmutter CIT: Richard Ellis Plus: Many students and associate members throughout the world

3. Durham, July 2006 SNLS: Vital Statistics 5 year (202n) rolling SN survey Goal: 500 high-z SNe to measure “w” Uses “Megacam” imager on the CFHT; griz every 4 nights in queue scheduled mode Survey running for 3 years ~300 confirmed z>0.1 SNe Ia Largest single telescope sample Largest single telescope sample “On track” for 500 by survey end “On track” for 500 by survey end

4. Durham, July 2006 Supernova Legacy Survey Keck (8 nights/yr) Gemini N & S (120 hr/yr) VLT (120 hr/yr) Magellan (15 nights/yr) Imaging CFHT Legacy Survey Deep program Spectroscopy Types, redshifts from 8m-class telescopes DiscoveriesLightcurves g’r’i’z’ every 4 days during dark time

5. Durham, July 2006 Dark Energy in the SNLS

6. Durham, July 2006 First-Year SNLS Hubble Diagram First Year Results (Astier et al. 2006) Assuming flatness, w=-1: Ω M = 0.263 ± 0.042 15% of final sample

7. Durham, July 2006 Dark energy: SNLS + WMAP Spergel et al. (2006) HST/GOODS+WMAPSNLS+WMAP

8. Durham, July 2006 The third year sample Third Year cosmological analysis: Data collection complete yesterday (end 06A)! Data collection complete yesterday (end 06A)! SN sample ~4 times larger SN sample ~4 times larger Improved “z” data will make the z>0.8 SNe more cosmologically powerful than in Year 1 Improved “z” data will make the z>0.8 SNe more cosmologically powerful than in Year 1 Final results should be ready in the Autumn Final results should be ready in the Autumn

9. Durham, July 2006 Preview of 3 rd year Hubble Diagram (preliminary) 160 SNe Ia to z=0.8 ~50 are still having data acquired or are still being reduced ~70 at z>0.8 await an improved k-correction template Sullivan et al. in prep.

10. Durham, July 2006 UV and U-band k-corrections At z<0.8, rest-frame B-V is used to colour-correct SNe At z>0.8: i’ and z’ probe rest-frame U and B – no V data i’ and z’ probe rest-frame U and B – no V data Understanding of UV/U required for colour correction to be performed Understanding of UV/U required for colour correction to be performed Almost no data – error in existing templates essentially unknown Almost no data – error in existing templates essentially unknown Rest-frame UV study at Keck (PI: Richard Ellis)

11. Durham, July 2006 SNe Ia show much diversity in the UV Improving the k- correction spectral template will decrease systematics from this region at z>0.8 Ellis, Sullivan et al. in prep.

12. Durham, July 2006 Constraining population evolution

13. Durham, July 2006 Potential Systematics in measuring w Photometric zeropoints Mismatches to local SNe observations Mismatches to local SNe observations Contamination by non-SNe Ia Spectroscopy is critical Spectroscopy is criticalK-corrections U and near-UV uncertain; evolution in UV? U and near-UV uncertain; evolution in UV?Extinction Grey dust; Effective R B ; Dust evolution Grey dust; 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 More “mundane” More “scientifically interesting”

14. Durham, July 2006 Potential Systematics in measuring w Photometric zeropoints Mismatches to local SNe observations Mismatches to local SNe observations Contamination by non-SNe Ia Spectroscopy is critical Spectroscopy is criticalK-corrections U and near-UV uncertain; evolution in UV? U and near-UV uncertain; evolution in UV?Extinction Grey dust; Effective R B ; Dust evolution Grey dust; 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 “Population Evolution”

15. ? White Dwarf Many competing models for: 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? Mass transfer mechanism? Mass transfer mechanism?

16. Durham, July 2006 SNLS: SN rate as a function of sSFR Per unit stellar mass, SNe are at least an order of magnitude more common in star- forming galaxies SN rate in SNLS “passive” galaxies 125 Host Galaxies at z<0.75 Sullivan et al. (2006)

17. Durham, July 2006 “A+B” Model for SN Ia rate Scannapieco & Bildsten (2005) and Mannucci et al. (2005) proposed a two-component model: Confirmed by SNLS results: SNR is linearly proportional to galaxy mass and SFR SNR is linearly proportional to galaxy mass and SFR SNe Ia will originate from a wide range in progenitor age SNe Ia will originate from a wide range in progenitor age Two components? Or one with a wide range in delay-time? Two components? Or one with a wide range in delay-time? Either way – the mix of the two components will evolve with redshift… Either way – the mix of the two components will evolve with redshift…

18. Durham, July 2006 Mix will evolve with redshift… Relative mix evolves strongly with redshift “B” component “A” component “A+B” total

19. Durham, July 2006 Population evolution: stretch and colour Distance estimator used: (how) Do these vary across environment? By understanding and calibrating any relationships, we can improve the quality of our standard candle s – “stretch” corrects for light-curve shape via α “c” – B-V colour corrects for extinction (and intrinsic variation) via β

20. Durham, July 2006 “Stretch” and Environment Stretch  Fainter/faster SNe Brighter/slower SNe  Sullivan et al. (2006) Star-forming galaxies Passive galaxies Similar trend observed at low-redshift Simplest inference: Older progenitors produce smaller stretch, fainter SNe Younger progenitors produce larger stretch, brighter SNe

21. Durham, July 2006 Yet – so far – the stretch correction seems to work equally well in all environments (Conley et al. 2006, AJ in press) No evidence for gross differences between light- curves in passive and active galaxies

22. Durham, July 2006 Colour relationships First year sample: β=1.6 (Milky Way dust predicts β=4.1) But – stretch correlates with environment; so perhaps the colour correction (β) should correlate with stretch Fainter Brighter SN Colour Combination of: Intrinsic “brighter-bluer” relationship Extinction

23. Durham, July 2006 Colour relationships – low stretch Preferentially located in passive galaxies Less dust Intrinsic SN relationship only?

24. Durham, July 2006 Colour relationships – high stretch Effective β differs according to environment Preferentially located in star-forming galaxies Extinction much greater Intrinsic SN relationship PLUS dust? Or just different intrinsic SN relationship?

25. Durham, July 2006 Low-stretch SNe show a far smaller scatter on the Hubble Diagram – but, they are rarer (A+B!) Low-stretch rms: 0.14 High-stretch rms: 0.20

26. Durham, July 2006 Summary 3 rd year analysis: challenge is controlling systematics such as population drift: SNe Ia know and “care” about their environment SNe Ia know and “care” about their environment Stretch depends on age of the progenitor population Stretch depends on age of the progenitor population SNe with narrow light-curves – preferentially hosted in passive galaxies – show less scatter SNe with narrow light-curves – preferentially hosted in passive galaxies – show less scatter Cosmology with sub-samples of SNe improves the power of the standard candle Cosmology with sub-samples of SNe improves the power of the standard candle

27. Durham, July 2006 Summary The SNLS dataset is the most uniform, well understood, and statistically powerful SN Ia data set – currently the best SN dataset to combine with BAO or WMAP data to measure w. 3 rd year analysis will be completed in the Autumn – watch this space The final SNLS data set will be essential for constraining systematics and when planning next generation projects like the LSST or NASA’s JDEM.

28. Durham, July 2006 PassivePassive Star-formingStar-forming StarburstingStarbursting  PEGASE2 is used to fit SED templates to the optical ugriz data.  Recent star-formation rate and total stellar mass are estimated.  Host galaxies classified by their specific star-formation rate.  PEGASE2 is used to fit SED templates to the optical ugriz data.  Recent star-formation rate and total stellar mass are estimated.  Host galaxies classified by their specific star-formation rate. Host Galaxies of SNLS SNe Sullivan et al. (2006)