SN Ia Rates in the SNLS: Progress Report

1. SN Ia Rates in the SNLS: Progress Report Mark Sullivan University of Oxford http://legacy.astro.utoronto.ca/ http://cfht.hawaii.edu/SNLS/

2. LBL Saul Perlmutter, + … Paris Reynald Pain, Pierre Astier, Julien Guy, Nicolas Regnault, Christophe Balland, Delphine Hardin, Jim Rich, + … Toronto Ray Carlberg, Alex Conley, Andy Howell, Kathy Perrett Victoria Chris Pritchet, Dave Balam Marseille Stephane Basa, Dominique Fouchez Oxford Mark Sullivan, Isobel Hook, + … The SNLS collaboration Full list of collaborators at: http://cfht.hawaii.edu/SNLS/

3. LBL Saul Perlmutter, + … Paris Reynald Pain, Pierre Astier, Julien Guy, Nicolas Regnault, Christophe Balland, Delphine Hardin, Jim Rich, + … Toronto Ray Carlberg, Alex Conley, Andy Howell, Kathy Perrett Victoria Chris Pritchet, Dave Balam Marseille Stephane Basa, Dominique Fouchez Oxford Mark Sullivan, Isobel Hook, + … The SNLS collaboration Full list of collaborators at: http://cfht.hawaii.edu/SNLS/

4. SNLS: Vital Statistics • 5 year “rolling” SN survey • Goal: >400 high-z SNe to measure “w” • Uses “Megacam” imager on the CFHT; griz every 4 nights in queue scheduled mode • Survey nearly complete • >350 confirmed z>0.1 SNe Ia • ~2000 SN detections in total

5. Previous results: volumetric rates Extend to test SN Ia rate evolution Neill et al. (2006)

6. Previous results: Connection to host galaxies SN rate versus host SFR SN stretch distributions split by galaxy star-formation rate Extend to measure SNIa DTD SN Ia rate per unit mass Star-forming hosts SFR per unit mass Extend to measure stretch-age relations Evidence for two/multiple SN Ia channels, or just a wide-range of delay-times with one channel? Passive hosts 170 SNLS SNe Ia Sullivan et al. (2006) SN stretch (s)

7. What’s new? • Improved efficiencies • Detailed simulations of entire survey • Improved photometric typing • Better templates, understanding of SNe • More spectroscopic redshifts (VVDS, DEEP) • Improved host galaxy analysis • Deeper data, better calibration • Star-formation “bursts” now included • More SNe! • Evolution in rates, DTDs, ...

8. All SNLS SN Candidates Masking (star halos, etc.) Observational culls (data quality) PhotoID: LC Fitting, Cull non-Ias All unmasked SNLS imaging data Add random fake SNe Ia Recover using RTA search software Apply same data quality culls Final SN Ia Sample Detection efficiencies (z,s,c) Visibility (field,season) Constructing the rate “Real” SN Ia Sample “Fake” Sample

9. Efficiencies from Monte Carlo sims Mag Result is a grid of efficiencies in redshift,stretch,colour z s c Perrett et al. (2008)

10. Drifts in colour and stretch in SNLS • Example: Spectrscopic sample • Brighter/broader/bluer SNe easier to find and observe spectroscopically • Observed stretch and colour should change with z Colour Stretch Detection bias only Detection and spectroscopy Perrett et al. (2008)

11. Malmquist effects: Compare to data

12. SN redshift estimation Improved version of Sullivan et al. 2006 LM method followed by grid search z,s,c,dm,Tmax Optional priors Full PDF output for each parameter SN Ia

13. SN redshift estimation Improved version of Sullivan et al. 2006 LM method followed by grid search z,s,c,dm,Tmax Optional priors Full PDF output for each parameter SN Ia CC SNe

14. SN redshift estimation Improved version of Sullivan et al. 2006 LM method followed by grid search z,s,c,dm,Tmax Optional priors Full PDF output for each parameter SN Ia CC SNe Unknown

15. Volumetric rate evolution Preliminary Perrett et al. (2008)

16. r i z g u Physical Parameters of SNLS SN Ia hosts • Little morphological information available • CFHT u*g’r’i’z’ imaging via the Legacy program. • PEGASE2 used to fit SED templates to optical data measured from custom stacks • Star-formation rate, total stellar mass, mean age are estimated. • Hosts classified by physical parameters Passive Star-forming Starbursting Sullivan et al. (2006)

17. “Age” versus stretch 0.2<z<0.8 Indicative of Delay-time Distribution (e.g. Totani et al.)?

18. DTDs from SN Ia host ages Caveats: • These are based on average galaxy ages • “mass-weighted”, “luminosity-weighted”, ... ? • Sensitive to IMF/SFH choices, age/metallicity issues • Corrections: • Efficiencies, volume, visibility,“age of Universe”, SFR(z) • No resolution below ~0.5Gyr, no information at t>~10Gyr • SNe with very faint/no hosts not included (<10) Nonetheless, SNLS is: • A well understood survey, large number of SNe • Has a high spectroscopic completeness, external redshifts

19. DTD 0.2<z<0.8 Preliminary Monte Carlo error analysis yet to be performed

20. DTD 0.2<z<0.8 “A+B” Preliminary

21. DTD 0.2<z<0.8 Gaussian Preliminary

22. DTD 0.2<z<0.8 Power law Preliminary

23. DTD 0.2<z<0.8 Exponential Preliminary

24. Summary • SNLS is a large homogeneous SN Ia sample, ideal for rates studies • Large amount of host galaxy data • SN Ia rates: • Measurement of volumetric rate extended to look for evolution • Measurement of galaxy rate extended to “DTD” • Galaxy age distribution will place constraints on DTD • Large number of other transients not yet exploited • Papers coming soon...