Transcript SNe Ia and the effect of environment

Dark energy in the Supernova
Legacy Survey
Mark Sullivan (University of Toronto)
http://legacy.astro.utoronto.ca/
Toronto Group
Victoria Group
Chris Pritchet, Don
Neill, Dave Balam
USA
LBL: Saul Perlmutter
CIT: Richard Ellis
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
Plus: Many students and associate members throughout the world
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
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Largest single telescope
sample
“On track” for 500 by survey
end
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Supernova Legacy Survey
Imaging
CFHT Legacy Survey
Deep program
Spectroscopy
Types, redshifts
from 8m-class
telescopes
Discoveries Lightcurves
Gemini N & S (120 hr/yr)
g’r’i’z’ every 4 days
during dark time
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Keck (8 nights/yr)
VLT (120 hr/yr)
Magellan (15
nights/yr)
Dark Energy in the SNLS
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First Year Results (Astier et al. 2006)
Assuming flatness, w=-1: ΩM = 0.263 ± 0.042
First-Year SNLS Hubble Diagram
15% of final
sample
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Dark energy: SNLS + WMAP
w  0.984
0.066
0.085
 M  0.719
HST/GOODS+WMAP
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0.021
0.029
Spergel et al. (2006)
SNLS+WMAP
The third year sample
Third Year cosmological analysis:
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Data collection complete yesterday (end 06A)!
SN sample ~4 times larger
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
Durham, July 2006
Preview of 3rd 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.
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UV and U-band k-corrections
At z<0.8, rest-frame B-V is used to colour-correct SNe
At z>0.8:
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i’ and z’ probe rest-frame U and B – no V data
Understanding of UV/U required for colour correction to be
performed
Almost no data – error in existing templates essentially
unknown
Rest-frame UV study at Keck (PI: Richard Ellis)
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SNe Ia show much
diversity in the UV
Improving the kcorrection spectral
template will decrease
systematics from this
region at z>0.8
Ellis, Sullivan et al. in
prep.
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Constraining population evolution
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Potential Systematics in measuring w
Photometric zeropoints
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More “mundane”
Mismatches to local SNe observations
Contamination by non-SNe Ia
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Spectroscopy is critical
K-corrections
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U and near-UV uncertain; evolution in UV?
Extinction
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Grey dust; Effective RB; Dust evolution
Redshift evolution in the mix of SNe
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“Population drift” – environment?
Evolution in SN properties
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Light-curves/Colors/Luminosities
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More “scientifically
interesting”
Potential Systematics in measuring w
Photometric zeropoints
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Mismatches to local SNe observations
Contamination by non-SNe Ia
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Spectroscopy is critical
K-corrections
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U and near-UV uncertain; evolution in UV?
Extinction
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Grey dust; Effective RB; Dust evolution
Redshift evolution in the mix of SNe
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“Population drift” – environment?
Evolution in SN properties
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Light-curves/Colors/Luminosities
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“Population
Evolution”
White Dwarf
?
Many competing models for:
• Nature of progenitor system – the
“second star”
• Single versus double degenerate
• Young versus old progenitor
• Explosion mechanism?
• Mass transfer mechanism?
SNLS: SN rate as a function of sSFR
Per unit stellar mass,
SNe are at least an
order of magnitude
more common in starforming galaxies
SN rate in SNLS
“passive” galaxies
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Sullivan et al. (2006)
125 Host Galaxies
at z<0.75
“A+B” Model for SN Ia rate
Scannapieco & Bildsten (2005) and Mannucci et al.
(2005) proposed a two-component model:
SNRIa t   A M stellar B  SFR
Confirmed by SNLS results:
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SNR is linearly proportional to galaxy mass and SFR
SNe Ia will originate from a wide range in progenitor age
Two components? Or one with a wide range in delay-time?
Either way – the mix of the two components will evolve
with redshift…
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Mix will evolve with redshift…
SNRIa t   A M stellar B  SFR
“A+B” total
Relative mix
evolves strongly
with redshift
“B” component
“A” component
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Population evolution: stretch and colour
Distance estimator used:
B  mB   ( s  1)    c
s – “stretch” corrects
for light-curve shape
via α
“c” – B-V colour corrects
for extinction (and
intrinsic variation) via β
(how) Do these vary across environment?
By understanding and calibrating any relationships, we can
improve the quality of our standard candle
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“Stretch” and Environment
Sullivan et al.
(2006)
Similar trend observed at
low-redshift
Star-forming
galaxies
Simplest inference:
Older progenitors
produce smaller stretch,
fainter SNe
Passive
galaxies
Stretch
Fainter/faster SNe
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Brighter/slower SNe 
Younger progenitors
produce larger stretch,
brighter SNe
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 lightcurves in passive
and active
galaxies
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Colour relationships
Fainter
Combination of:
Intrinsic “brighter-bluer”
relationship
Extinction
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
Brighter
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SN Colour
Colour relationships – low stretch
Preferentially located
in passive galaxies
Less dust
Intrinsic SN
relationship only?
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Colour relationships – high stretch
Preferentially located in
star-forming galaxies
Extinction much greater
Intrinsic SN relationship
PLUS dust?
Or just different intrinsic
SN relationship?
Effective β differs according to
environment
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Low-stretch
rms: 0.14
High-stretch
rms: 0.20
Low-stretch SNe show a far smaller scatter on the Hubble
Diagram – but, they are rarer (A+B!)
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Summary
3rd year analysis: challenge is controlling
systematics such as population drift:
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SNe Ia know and “care” about their environment
Stretch depends on age of the progenitor population
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
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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.
3rd 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.
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