Dark energy in the Supernova Legacy Survey Mark Sullivan (University of Toronto)

Dark energy in the Supernova Dark energy in the Supernova Legacy SurveyLegacy Survey

Mark Sullivan (University of Toronto)Mark Sullivan (University of Toronto)

http://legacy.astro.utoronto.ca/

Toronto Group

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

Victoria Group

Chris Pritchet, Don Neill, Dave Balam

USA

LBL: Saul Perlmutter

CIT: Richard Ellis

Plus: Many students and associate members throughout the world

Durham, July 2006Durham, July 2006

SNLS: Vital StatisticsSNLS: Vital Statistics5 year (202n) rolling SN survey5 year (202n) rolling SN survey

Goal: 500 high-z SNe to measure “w”Goal: 500 high-z SNe to measure “w”

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

Survey running for 3 yearsSurvey running for 3 years

~~300 confirmed 300 confirmed zz>0.1 SNe Ia>0.1 SNe Ia

Largest single telescope sampleLargest single telescope sample

““On track” for 500 by survey endOn track” for 500 by survey end


Supernova Legacy SurveySupernova Legacy Survey

Keck (8 nights/yr)

Gemini N & S (120 hr/yr)

VLT (120 hr/yr)

Magellan (15 nights/yr)

ImagingCFHT Legacy Survey

Deep program

Spectroscopy Types, redshifts from 8m-class

telescopes

DiscoveriesLightcurves

g’r’i’z’ every 4 days during dark

time


Dark Energy in the SNLSDark Energy in the SNLS


First-Year SNLS Hubble Diagram

First Year Results (Astier et al. 2006)First Year Results (Astier et al. 2006)Assuming flatness, w=-1: ΩM = 0.263 ± 0.042

15% of final sample


Dark energy: SNLS + WMAPDark energy: SNLS + WMAP

066.0085.0984.0w

021.0029.0M 719.0

Spergel et al. (2006)

HST/GOODS+WMAP SNLS+WMAP


The third year sampleThe third year sample

Third Year cosmological analysis:Third Year cosmological analysis: Data collection complete yesterday (end 06A)!Data collection complete yesterday (end 06A)! SN sample SN sample ~~4 times larger4 times larger Improved “z” data will make the z>0.8 SNe more Improved “z” data will make the z>0.8 SNe more

cosmologically powerful than in Year 1cosmologically powerful than in Year 1 Final results should be ready in the AutumnFinal results should be ready in the Autumn


Preview of 3Preview of 3rdrd year Hubble Diagram ( year Hubble Diagram (preliminarypreliminary))

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.


UV and U-band k-correctionsUV and U-band k-corrections

At z<0.8, rest-frame B-V is used to colour-correct SNeAt z<0.8, rest-frame B-V is used to colour-correct SNe

At z>0.8:At z>0.8: i’ and z’ probe rest-frame U and B – i’ and z’ probe rest-frame U and B – no V datano V data Understanding of UV/U required for colour correction to be Understanding of UV/U required for colour correction to be

performedperformed Almost no data – error in existing templates essentially Almost no data – error in existing templates essentially

unknownunknown

Rest-frame UV study at Keck (PI: Richard Ellis)Rest-frame UV study at Keck (PI: Richard Ellis)


SNe Ia show much diversity in the UV

Improving the k-correction spectral

template will decrease systematics from this

region at z>0.8

Ellis, Sullivan et al. in prep.


Constraining population evolutionConstraining population evolution


Potential Systematics in measuring wPotential Systematics in measuring wPhotometric zeropointsPhotometric zeropoints

Mismatches to local SNe observationsMismatches to local SNe observations

Contamination by non-SNe IaContamination by non-SNe Ia Spectroscopy is criticalSpectroscopy is critical

K-correctionsK-corrections U and near-UV uncertain; evolution in UV?U and near-UV uncertain; evolution in UV?

ExtinctionExtinction Grey dust; Effective RGrey dust; Effective RBB;; Dust evolutionDust evolution

Redshift evolution in the mix of SNeRedshift evolution in the mix of SNe ““Population drift” – environment?Population drift” – environment?

Evolution in SN propertiesEvolution in SN properties Light-curves/Colors/LuminositiesLight-curves/Colors/Luminosities

More “mundane”

More “scientifically interesting”


Potential Systematics in measuring wPotential Systematics in measuring wPhotometric zeropointsPhotometric zeropoints

Mismatches to local SNe observationsMismatches to local SNe observations

Contamination by non-SNe IaContamination by non-SNe Ia Spectroscopy is criticalSpectroscopy is critical

K-correctionsK-corrections U and near-UV uncertain; evolution in UV?U and near-UV uncertain; evolution in UV?

ExtinctionExtinction Grey dust; Effective RGrey dust; Effective RBB;; Dust evolutionDust evolution

Redshift evolution in the mix of SNeRedshift evolution in the mix of SNe ““Population drift” – environment?Population drift” – environment?

Evolution in SN propertiesEvolution in SN properties Light-curves/Colors/LuminositiesLight-curves/Colors/Luminosities

“Population Evolution”

?White Dwarf

Many competing models for:Many competing models for:• Nature of progenitor system – the Nature of progenitor system – the

“second star”“second star”• Single versus double degenerateSingle versus double degenerate• Young versus old progenitorYoung versus old progenitor• Explosion mechanism?Explosion mechanism?• Mass transfer mechanism?Mass transfer mechanism?


SNLS: SN rate as a function of sSFRSNLS: SN rate as a function of sSFR

Per unit stellar mass, SNe are at least an order of magnitude more common in star-forming galaxies

SN rate in SNLS “passive” galaxies 125 Host

Galaxies at z<0.75

Sullivan et al. (2006)


““A+B” Model for SN Ia rateA+B” Model for SN Ia rate

SFRBMAt stellarIaSNR

Scannapieco & Bildsten (2005) and Mannucci et al. Scannapieco & Bildsten (2005) and Mannucci et al. (2005) proposed a two-component model:(2005) proposed a two-component model:

Confirmed by SNLS results:Confirmed by SNLS results: SNR is linearly proportional to galaxy mass and SFRSNR is linearly proportional to galaxy mass and SFR SNe Ia will originate from a wide range in progenitor ageSNe Ia will originate from a wide range in progenitor age Two components? Or one with a wide range in delay-time?Two components? Or one with a wide range in delay-time? Either way – the mix of the two components will evolve Either way – the mix of the two components will evolve

with redshift…with redshift…


Mix will evolve with redshift…Mix will evolve with redshift…

Relative mix Relative mix evolves evolves stronglystrongly

with redshiftwith redshift

“B” component

“A” component

“A+B” total

SFRBMAt stellarIaSNR


Population evolution: stretch and colourPopulation evolution: stretch and colour

Distance estimator used:Distance estimator used:

(how) Do these vary across environment?(how) Do these vary across environment?

By understanding and calibrating any relationships, we can By understanding and calibrating any relationships, we can improve the quality of our standard candleimprove the quality of our standard candle

csmBB )1(

s – “stretch” corrects s – “stretch” corrects for light-curve shape for light-curve shape

via via αα““c” – B-V colour corrects c” – B-V colour corrects

for extinction (and for extinction (and intrinsic variation) via intrinsic variation) via ββ


““Stretch” and EnvironmentStretch” and Environment

Stretch

Fainter/faster SNe Brighter/slower SNe

Sullivan et al. (2006)

Star-forming galaxies

Passive galaxies

Similar trend observed at low-redshift

Simplest inference:

Older progenitors produce smaller stretch, fainter SNe

Younger progenitors produce larger stretch, brighter SNe


Yet – so far – the stretch correction seems to work Yet – so far – the stretch correction seems to work equally well in all environmentsequally well in all environments

(Conley et al. 2006, AJ in press)(Conley et al. 2006, AJ in press)

No evidence for gross differences

between light-curves in passive

and active galaxies


Colour relationshipsColour relationships

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

Fainter

Brighter SN Colour

Combination of:

Intrinsic “brighter-bluer” relationship

Extinction


Colour relationships – low stretchColour relationships – low stretch

Preferentially located in passive galaxies

Less dust

Intrinsic SN relationship only?


Colour relationships – high stretchColour relationships – high stretch

Effective β differs according to environment

Preferentially located in star-forming galaxies

Extinction much greater

Intrinsic SN relationship PLUS dust?

Or just different intrinsic SN relationship?


Low-stretch SNe show a far smaller scatter on the Hubble Diagram – but, they are rarer (A+B!)

Low-stretch

rms: 0.14

High-stretch

rms: 0.20


SummarySummary

33rdrd year analysis: challenge is controlling year analysis: challenge is controlling systematics such as population drift:systematics such as population drift: SNe Ia know and “care” about their environmentSNe Ia know and “care” about their environment Stretch depends on age of the progenitor populationStretch depends on age of the progenitor population SNe with narrow light-curves – preferentially hosted in SNe with narrow light-curves – preferentially hosted in

passive galaxies – show less scatter passive galaxies – show less scatter Cosmology with sub-samples of SNe improves the Cosmology with sub-samples of SNe improves the

power of the standard candlepower of the standard candle


SummarySummary

The SNLS dataset is the most uniform, well understood, The SNLS dataset is the most uniform, well understood, and statistically powerful SN Ia data set – currently the and statistically powerful SN Ia data set – currently the best SN dataset to combine with BAO or WMAP data to best SN dataset to combine with BAO or WMAP data to measure w.measure w.

33rdrd year analysis will be completed in the Autumn – year analysis will be completed in the Autumn – watch this spacewatch this space

The final SNLS data set will be essential for constraining The final SNLS data set will be essential for constraining systematics and when planning next generation projects systematics and when planning next generation projects like the LSST or NASA’s JDEM.like the LSST or NASA’s JDEM.

Documents

Dark energy in the Supernova Legacy Survey Mark Sullivan (University of Toronto)