Transcript SNe Ia and the effect of environment

SN Ia Rates in the SNLS:
Progress Report
Mark Sullivan
University of Oxford
http://legacy.astro.utoronto.ca/
http://cfht.hawaii.edu/SNLS/
Paris
Toronto
Victoria
Chris Pritchet,
Dave Balam
Ray Carlberg, Alex
Conley, Andy
Howell, Kathy
Perrett
Reynald Pain, Pierre
Astier, Julien Guy, Nicolas
Regnault, Christophe
Balland, Delphine Hardin,
Jim Rich, + …
Marseille
Stephane Basa,
Dominique Fouchez
Oxford
LBL
Saul Perlmutter, + …
Mark Sullivan, Isobel
Hook, + …
The SNLS
collaboration
Full list of collaborators at: http://cfht.hawaii.edu/SNLS/
Paris
Toronto
Victoria
Chris Pritchet,
Dave Balam
Ray Carlberg, Alex
Conley, Andy
Howell, Kathy
Perrett
Reynald Pain, Pierre
Astier, Julien Guy, Nicolas
Regnault, Christophe
Balland, Delphine Hardin,
Jim Rich, + …
Marseille
Stephane Basa,
Dominique Fouchez
Oxford
LBL
Saul Perlmutter, + …
Mark Sullivan, Isobel
Hook, + …
The SNLS
collaboration
Full list of collaborators at: http://cfht.hawaii.edu/SNLS/
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
Extend to test SN Ia rate evolution
Neill et al. (2006)
SN Ia rate per unit mass
Previous results: Connection to host galaxies
SN rate versus
host SFR
Extend to measure
SNIa DTD
SN stretch distributions
split by galaxy starformation rate
Star-forming
hosts
SFR per unit mass
Evidence for two/multiple SN Ia
channels, or just a wide-range of delaytimes with one channel?
Extend to measure
stretch-age
relations
Passive
hosts
170 SNLS SNe Ia
Sullivan et al. (2006)
SN stretch (s)
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, ...
Constructing the rate
“Real” SN Ia Sample
“Fake” Sample
All SNLS SN Candidates
All unmasked SNLS imaging data
Masking (star halos, etc.)
Add random fake SNe Ia
Observational culls (data quality)
Recover using RTA search software
PhotoID: LC Fitting, Cull non-Ias
Apply same data quality culls
Final SN Ia Sample
Detection efficiencies (z,s,c)
Visibility (field,season)
1 N
1
rV  
V i i zi,si ,c i Tirest
Mag
Efficiencies from
Monte Carlo sims
z
Result is a grid of
efficiencies in
redshift,stretch,colour
s
c
Perrett et al. (2008)
Drifts in colour and stretch in SNLS
Colour
Example: Spectrscopic
sample
Brighter/broader/bluer SNe
easier to find and observe
spectroscopically
Observed stretch and
colour should change with z
Stretch
Detection bias only
Detection and spectroscopy
Perrett et al. (2008)
Malmquist effects: Compare to data
SN redshift estimation
SN Ia
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 redshift estimation
SN Ia
CC SNe
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 redshift estimation
SN Ia
CC SNe
Unknown
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
Volumetric rate evolution
Preliminary
Perrett et al. (2008)
Passive
Physical Parameters of SNLS SN Ia hosts
available
 CFHT u*g’r’i’z’ imaging via the
Star-forming
r Legacy
i program.
z
Starbursting
u
g
 Little morphological information
 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
Sullivan et al. (2006)
0.2<z<0.8
“Age” versus stretch
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”, ... ?
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
0.2<z<0.8
DTD
Preliminary
Monte Carlo error analysis yet to
be performed
0.2<z<0.8
DTD
“A+B”
Preliminary
0.2<z<0.8
DTD
Gaussian
Preliminary
0.2<z<0.8
DTD
Power law
Preliminary
0.2<z<0.8
DTD
Exponential
Preliminary
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...