SN Ia Rates in the SNLS: Progress Report Mark Sullivan University of Oxford

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

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

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

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

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

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

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

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

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 Stretch Colour Detection bias only Detection and spectroscopy Perrett et al. (2008)

Malmquist effects: Compare to data

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

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

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

Volumetric rate evolution Perrett et al. (2008) Preliminary

PassivePassive Star-formingStar-forming StarburstingStarbursting  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  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 Physical Parameters of SNLS SN Ia hosts Sullivan et al. (2006) u g r i z

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

DTDs from SN Ia host ages Caveats: These are based on average galaxy ages “mass-weighted”, “luminosity-weighted”,... ? “mass-weighted”, “luminosity-weighted”,... ? Sensitive to IMF/SFH choices, age/metallicity issues Corrections: Efficiencies, volume, visibility,“age of Universe”, SFR(z) 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 A well understood survey, large number of SNe Has a high spectroscopic completeness, external redshifts Has a high spectroscopic completeness, external redshifts

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

DTD 0.2<z<0.8 Preliminary “A+B”

DTD 0.2<z<0.8 Preliminary Gaussian

DTD 0.2<z<0.8 Preliminary Power law

DTD 0.2<z<0.8 Preliminary Exponential

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 volumetric rate extended to look for evolution Measurement of galaxy rate extended to “DTD” 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...