SN Ia Rates in the SNLS: SN Ia Rates in the SNLS: Progress Report Progress Report

Mark Sullivan Mark Sullivan University of OxfordUniversity of Oxford

http://legacy.astro.utoronto.ca/http://legacy.astro.utoronto.ca/http://cfht.hawaii.edu/SNLS/http://cfht.hawaii.edu/SNLS/

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: http://cfht.hawaii.edu/SNLS/

Victoria

Chris Pritchet, Dave Balam

Toronto

Ray Carlberg, Alex Conley, Andy Howell, Kathy

Perrett

The SNLS The SNLS collaborationcollaboration

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: http://cfht.hawaii.edu/SNLS/

Victoria

Chris Pritchet, Dave Balam

Toronto

Ray Carlberg, Alex Conley, Andy Howell, Kathy

Perrett

The SNLS The SNLS collaborationcollaboration

Marseille

Stephane Basa, Dominique Fouchez

LBL

Saul Perlmutter, + …

SNLS: Vital StatisticsSNLS: Vital Statistics

5 year “rolling” SN survey5 year “rolling” SN survey

Goal: >400 high-z SNe to measure “w”Goal: >400 high-z SNe to measure “w”

Uses “Megacam” imager on the CFHT; griz Uses “Megacam” imager on the CFHT; griz every 4 nights in queue scheduled modeevery 4 nights in queue scheduled mode

Survey nearly completeSurvey nearly complete

>>350 confirmed 350 confirmed zz>0.1 SNe Ia>0.1 SNe Ia

~~2000 SN detections in total2000 SN detections in total

Previous results: volumetric ratesPrevious results: volumetric rates

Neill et al. (2006)

Extend to test SN Ia rate evolution

Passive Passive hostshosts

Star-forming Star-forming hostshosts

Previous results: Connection to host galaxiesPrevious results: Connection to host galaxies

170 SNLS SNe Ia170 SNLS SNe Ia

SN rate versus host SFR

SN stretch distributions split by galaxy star-

formation rate

SN Ia

rate

per

uni

t mas

s

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?What’s new?

Improved efficienciesImproved efficiencies Detailed simulations of entire surveyDetailed simulations of entire survey

Improved photometric typingImproved photometric typing Better templates, understanding of SNeBetter templates, understanding of SNe

More spectroscopic redshifts (VVDS, DEEP)More spectroscopic redshifts (VVDS, DEEP)

Improved host galaxy analysisImproved host galaxy analysis Deeper data, better calibrationDeeper data, better calibration Star-formation “bursts” now includedStar-formation “bursts” now included

More SNe!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-Ias

All unmasked SNLS imaging data

Detection efficiencies (z,s,c) Visibility (field,season)

Add random fake SNe Ia

Recover using RTA search software

Apply same data quality culls

€

rV =1V

1ε i zi,si,c i( )ΔTi

resti

N

∑

Constructing the rate

Efficiencies from Efficiencies from Monte Carlo simsMonte Carlo sims

Result is a grid of Result is a grid of efficiencies in efficiencies in

redshift,stretch,colourredshift,stretch,colour

Perrett et al. (2008)

Mag

z

s

c

Drifts in colour and stretch in SNLSDrifts in colour and stretch in SNLS

Example: Spectrscopic Example: Spectrscopic samplesample

Brighter/broader/bluer SNe Brighter/broader/bluer SNe easier to find and observe easier to find and observe spectroscopicallyspectroscopically

Observed stretch and colour Observed stretch and colour should change with zshould change with zStretch

Colour

Detection bias onlyDetection and spectroscopy

Perrett et al. (2008)

Malmquist effects: Compare to dataMalmquist effects: Compare to data

SN redshift estimationSN redshift estimationImproved version of Improved version of Sullivan et al. 2006Sullivan et al. 2006

LM method followed LM method followed by grid searchby grid search

z,s,c,dm,Tmaxz,s,c,dm,Tmax

Optional priorsOptional priors

Full PDF output for Full PDF output for each parametereach parameter

SN Ia

SN redshift estimationSN redshift estimation

SN IaCC SNe

Improved version of Improved version of Sullivan et al. 2006Sullivan et al. 2006

LM method followed LM method followed by grid searchby grid search

z,s,c,dm,Tmaxz,s,c,dm,Tmax

Optional priorsOptional priors

Full PDF output for Full PDF output for each parametereach parameter

SN redshift estimationSN redshift estimation

SN IaCC SNeUnknown

Improved version of Improved version of Sullivan et al. 2006Sullivan et al. 2006

LM method followed LM method followed by grid searchby grid search

z,s,c,dm,Tmaxz,s,c,dm,Tmax

Optional priorsOptional priors

Full PDF output for Full PDF output for each parametereach parameter

Volumetric rate evolutionVolumetric rate evolution

Perrett et al. (2008)

Preliminary

Pass

ive

Pass

ive

Star

-form

ing

Star

-form

ing

Star

burs

ting

Star

burs

ting

Little morphological information Little morphological information availableavailable

CFHT u*g’r’i’z’ imaging via the CFHT u*g’r’i’z’ imaging via the Legacy program.Legacy program.

PEGASE2 used to fit SED PEGASE2 used to fit SED templates to optical data templates to optical data measured from custom stacksmeasured from custom stacks

Star-formation rate, total stellar Star-formation rate, total stellar mass, mean age are estimated. mass, mean age are estimated.

Hosts classified by physical Hosts classified by physical parametersparameters

Physical Parameters of SNLS SN Ia hostsPhysical Parameters of SNLS SN Ia hosts

Sullivan et al. (2006)Sullivan et al. (2006)

u g

r iz

““Age” versus stretchAge” versus stretch0.2<z<0.8

Indicative of Delay-time Distribution (e.g. Totani et al.)?

DTDs from SN Ia host agesDTDs from SN Ia host ages

Caveats:Caveats:These are based on average galaxy agesThese are based on average galaxy ages ““mass-weighted”, “luminosity-weighted”, ... ?mass-weighted”, “luminosity-weighted”, ... ?

Sensitive to IMF/SFH choices, age/metallicity isSensitive to IMF/SFH choices, age/metallicity isssues ues Corrections: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>~10GyrNo resolution below ~0.5Gyr, no information at t>~10GyrSNe with very faint/no hosts not included (<10)SNe with very faint/no hosts not included (<10)

Nonetheless, SNLS is:Nonetheless, SNLS is: A well understood survey, large number of SNeA well understood survey, large number of SNe Has a high spectroscopic completeness, external redshiftsHas a high spectroscopic completeness, external redshifts

DTDDTD0.2<z<0.8

Preliminary

Monte Carlo error analysis yet to be performed

DTDDTD0.2<z<0.8

Preliminary“A+B”

DTDDTD0.2<z<0.8

PreliminaryGaussian

DTDDTD0.2<z<0.8

PreliminaryPower law

DTDDTD0.2<z<0.8

PreliminaryExponential

SummarySummarySNLS is a large homogeneous SN Ia sample, ideal for SNLS is a large homogeneous SN Ia sample, ideal for rates studiesrates studiesLarge amount of host galaxy dataLarge amount of host galaxy data

SN Ia rates:SN Ia rates: Measurement of volumetric rate extended to look for Measurement of volumetric rate extended to look for

evolutionevolution Measurement of galaxy rate extended to “DTD”Measurement of galaxy rate extended to “DTD”

Galaxy age distribution will place constraints on DTDGalaxy age distribution will place constraints on DTDLarge number of other transients not yet exploitedLarge number of other transients not yet exploited

Papers coming soon...Papers coming soon...