Cosmology Results from the SDSS Supernova Survey

1
Cosmology Results from the SDSS Supernova Survey

• David Cinabro
• SMU
• 15 September 2008

2
Contents

• ? Cosmology and Dark Energy Intro
• ? SDSS Supernova Survey (2005-07)?
• ? Hubble Diagram Analysis Results
  (1st-year SDSS data external)?

3
Primary Motivation for Supernova
Surveysmeasure expansionhistory of the
Universein particular, the role of dark energy
4
Expansion Basics
H(z)2 H02 ?i ?i (1z)3(1w) where w
(equation of state parameter) is pressure/density
5
Methods to Measure H(z)?
H(z)2 ?i ?i (1z)3(1w)
6
(No Transcript)
7
Hubble Diagram Basics

• Expansion history
• depends on
• ?? and ?M

8
Hubble Diagram Basics

• Expansion history
• depends on
• ?? and ?M

What we measure with SNe
relative to empty universe
9
Hubble Diagram Basics

• Expansion history
• depends on
• ?? and ?M

What we measure with SNe
relative to empty universe
10
w-sensitivity with Supernova
11
w-Quest with Supernova

w 0.9 gives 4 variation from w 1
SNLS, ESSENCE
SDSS
redshift
12
Surveys

• 1990s
• Development discovery
• phase (Hi-z, SCP).
• Lightcurve quality limited by
• telescope time.

compilation from Riess et. al., AJ 607(2004)
Calan Tololo, HZT, SCP, CfA, Higher-Z, ACS.
?M1 ??0
?mag
?M1 ??0
0 0.5 1.0
1.5 2.0
redshift
13
Surveys
2000s Much more
telescope time ? rolling searches more
passbands. (SNLS, ESSENCE, SDSS)?

• 1990s
• Development discovery
• phase (Hi-z, SCP).
• Lightcurve quality limited by
• telescope time.

SNLS 1st year sample (Astier 2005) plus 40
low-z SNe from literature
compilation from Riess et. al., AJ 607(2004)
Calan Tololo, HZT, SCP, CfA, Higher-Z, ACS.
?M1 ??0
?mag
?M1 ??0
0 0.5 1.0
1.5 2.0
redshift
14
Surveys
2000s Much more
telescope time ? rolling searches more
passbands. (SNLS, ESSENCE, SDSS)?

• 1990s
• Development discovery
• phase (Hi-z, SCP).
• Lightcurve quality limited by
• telescope time.

SNLS 1st year sample (Astier 2005) plus 40
low-z SNe from literature
compilation from Riess et. al., AJ 607(2004)
Calan Tololo, HZT, SCP, CfA, Higher-Z, ACS.
?M1 ??0
?mag
?M1 ??0
0 0.5 1.0
1.5 2.0
redshift
15
Surveys
2000s Much more
telescope time ? rolling searches more
passbands. (SNLS, ESSENCE, SDSS)?

• 1990s
• Development discovery
• phase (Hi-z, SCP).
• Lightcurve quality limited by
• telescope time.

SNLS 1st year sample (Astier 2005) plus 40
low-z SNe from literature
compilation from Riess et. al., AJ 607(2004)
Calan Tololo, HZT, SCP, CfA, Higher-Z, ACS.
?M1 ??0
?mag
SDSS survey fills gap adds low-z SNe
?M1 ??0
0 0.5 1.0
1.5 2.0
redshift
16
SN papers becoming Methodology papersas
surveys contribute smaller fraction of total SNe
Ia

• Astier06 SNLS contributes 70 of 110
• Kowalski 2008
• contributes 8 of 307 SNe Ia
• SDSS 2008 contributes 100 of 240

17
Meet the SDSS-II Supernova Team
AJ 135, 338 (2008)?
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SDSS-II Supernova Survey Sep 1 - Nov 30,
2005-2007 (1 of 3 SDSS projects for 2005-2008)?
GOAL Few hundred high-quality type Ia SNe
lightcurves in redshift range 0.05-0.35
SAMPLING 300 sq deg in ugriz (3
million galaxies every two nights)?
SPECTROSCOPIC FOLLOW-UP HET, ARC 3.5m, MDM,
Subaru, WHT, Keck, NTT, KPNO, NOT, SALT,
Magellan, TNG
19
SDSS Data Flow
One full night collects 800 fields (ugriz per
field) ? 200 GB
one raw g-field (0.150)?
Each search field is compared to a 2-year old
template field things that go boom are
extracted for human scanning. Ten dual-CPU
servers at APO process g,r,i data (2400
fields) in 20 hrs.
(can you find a confirmed SN Ia ?)?
20
SDSS Data Flow
One full night collects 800 fields (ugriz per
field) ? 200 GB
one raw g-field (0.150)?
21
SDSS Manual Scanning
search template subtr
g
r
i
The 'Good'
z0.05 also followed by SNF and CSP
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SDSS Manual Scanning
search template subtr
g
search template subtr
r
i
The 'Good'
z0.05 also followed by SNF and CSP
The 'Bad'
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SDSS Manual Scanning
search template subtr
g
search template subtr
search template subtr
r
i
The 'Good'
z0.05 also followed by SNF and CSP
The 'Bad'
The 'Ugly'
24
z 0.09
z 0.20
g
r
i
search template subtr
search template subtr
z 0.36
z 0.29
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Lightcurve Fits Update in Real Time
2 epochs 30 epochs
mag
mag
mag
day
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Lightcurve Fits Update in Real Time
2 epochs 30 epochs
gt 90 of photometric Ia candidates were
spectroscopically confirmed to be SN Ia
mag
mag
mag
day
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Follow-up Spectral id
II
H?
Ia
Flux
H?
Observer wavelength (Å)?
Observer wavelength (Å)?
AGN (rare)
Ia
Flux
Observer wavelength (Å)?
Observer wavelength (Å)?
28
Survey Scan Stats Sako et al., AJ 135, 348
(2008)
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Survey Scan Stats Sako et al., AJ 135, 348
(2008)
Plus 1000 photometric SN Ia we have 200
host-galaxy redshifts and still observing
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SN Fakes

• Fake SN Ia were
• inserted into the
• images in real
• time to measure
• software
• scanning
• efficiencies.

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SDSS-SN Redshift Cadence
200520062007
20052006
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SDSS-SN Redshift Cadence
200520062007
20052006
Temporal edge effects SNe peak too early or too
late. May relax cuts later.
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SDSS Rate for SN Ia with z lt 0.122005 sample ?
Dilday et al., arXiv0801.3297Motivation
understand nature of SN progenitors
Contributions 16 spectroscopically
confirmed Ia (26 before cuts)? 1
photometric-id with host spec-Z
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Unbinned Likelihood Fit

• SDSS result Dilday 2008
• ? previous results with spectroscopic
  confirmation
• ? idem, but unclear efficiency (exclude from our
  fit)?

Rate (1z)1.5 0.6
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SDSS SN Ia Rate in progress
Spectro- Confirmed Photometric id host
z Photometric id only
statistics vs. systematics
350 and larger syst- error
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SDSS Hubble Diagram Analysis Samples Include

• SDSS 2005 ( 100)?
• Low redshift from literature (26 or 44)?
• SNLS published ( 70)?
• ESSENCE published ( 60)?

37
Supernova Photometry from Fit
(Holtzman et. al., 2008, submitted)?
FIT-DATA all images (few dozen ?
ugriz)? FIT-MODEL galaxy stars SN sky
FIT PROPERTIES gal stars same in every
image SN variable in every image gal
stars SN PSF-smeared
calib stars
SN?

• NO PIXEL RE-SAMPLING !
• ? no pixel correlations
• proper stat. errors

SDSS image
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Extensive Photometry Tests Include

• Recover zero flux pre-explosion
• Recover star mags
• Recover flux from fake SN

39
Analysis Overview

• Use both MLCS2k2 SALT2 methods (competing SNIa
  models)?
• Evaluate systematic uncertainties

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Analysis Overview

• Use both MLCS2k2 SALT2 methods
• Evaluate systematic uncertainties
• Five sample-combinations
• a) SDSS-only
• b) SDSS ESSENCE SNLS
• c) Nearby SDSS
• d) Nearby SDSS ESSENCE SNLS
• e) Nearby ESSENCE SNLS

no nearby sample SDSS is lowz anchor
SDSS is high-z sample
(nominal)?
(compare to WV07)?
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Lightcurve Fit Brief Introduction

• Fit data to parametric model (or template) to get
  shape and color.
• Use shape and color to standardize intrinsic
  luminosity.

? SDSS data
Fit model
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Comparison of Lightcurve Fit Methods
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Comparison of Lightcurve Fit Methods
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Comparison of Lightcurve Fit Methods
vectors
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SDSS SN Ia Lightcurves _at_ z 0.09 z
0.19 z 0.36
? data
-- fit model
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Hubble Diagram
46 99 56 63
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Cosmology Fit for w and ?M(illustration with all
4 Ia samples)?
CMB (WMAP 5year)?
BAO
Preliminary
SN Ia
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Fit Residuals
Preliminary
.17 mag error added in fit, but not in plot)?
redshift
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Fit Residuals
Preliminary
smaller ?2 is partly due to inefficiency from
spectroscopic targeting.
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Systematic uncertainties for MLCS
method. Uncertainties for SALT2 nearly
finished ...
Preliminary
Total systematic uncertainty 0.22
0.11 0.15 0.09 0.09 Statistical uncertainty
0.25 0.12 0.18 0.10
0.12
a) SDSS-only b) SDSS ESSENCE SNLS c) Nearby
SDSS d) Nearby SDSS ESSENCE SNLS e) Nearby
ESSENCE SNLS
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Total Error Contours(stretch stat-contour along
BAOCMB axis
SDSS-only
Preliminary
w
systematic tests 68 stat-error 68 total-error
?M
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Results
MLCS Total-error contours
SALT2 stat-error contours (expect ?stat ?syst
)?
w
Preliminary
Preliminary
?M
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Results
ESSENCE Wood-Vasey, AJ 666, 694 (2007)? SNLS
Astier, AJ 447, 31 (2006)?
MLCS Total-error contours
w
Preliminary
?
?M
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Questions to Ponder

• Q1 Why does our MLCS-based w-result
• differ by 0.3 compared to WV07
  
• (same method same data) ?
• Q2 Why do MLCS and SALT2 results differ
• when high-redshift samples
• (ESSENCE SNLS) are included ?

55
Questions to Ponder

• Q1 Why does our MLCS-based w-result
• differ by 0.3 compared to WV07
  
• (same method same data) ?
• ? ?w .1 different RV to describe
  host-galaxy extinction
• ? ?w .1 account for spectroscopic
  inefficiency
• ? ?w .1 require z gt .025 instead of z gt .015
  to avoid
• Hubble anomaly
• Misc different Bessell filter shifts, fit in
  flux, Vega ?BD17
• Note changes motivated by SDSS-SN
  observations !
• Note changes do NOT commute depends on sequence.

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Dust Law RV AV/E(B-V)and A(?) from
Cardelli, Clayton, Mathis ApJ, 345, 245 (1989)?

• ? Previous MLCS-based analyses assumed RV 3.1
  (global parameter)?
• ? Growing evidence points to RV 2
• ? SALT2 ? (RV1) 2 - 2.5
• ? LOWZ studies (Nobili 08 RV 1.8)?
• ? individual SN with NIR (Krisciunas)?
• ? We have evaluated RV with our own SDSS data

57
Dust Law RV AV/E(B-V)?
To measure a global property of SN Ia, need
sample with well-understood efficiency
Spec-confirmed SN Ia sample has large (spec)
inefficiency that is not modeled by the sim.
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Dust Law RV AV/E(B-V)?
To measure a global property of SN Ia, need
sample with well-understood efficiency
Solution include photometric SNe Ia with
host-galaxy redshift !
z lt .3 Dust sample
Spec-confirmed SN Ia sample has large (spec)
inefficiency that is not modeled by the sim.
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Dust Law RV AV/E(B-V)?
Method minimize data-MC chi2 for color vs. epoch
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Dust Law RV AV/E(B-V)?
Method minimize data-MC chi2 for color vs. epoch
SDSS Result RV 1.9 0.2stat
0.6syst Consistent with SALT2 ? and other
SN-based studies.
Preliminary
61
Spectroscopic Inefficiency

• Simulate all known
• effects using REAL
• observing conditions
• Compare data/sim
• redshift distributions
• Difference attributed
• to spectroscopic ineff.

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? Spectroscopic efficiency modeled as
exp(-mV/?)? ? Eff(spec) is included in fitting
prior ? Assign w-syst error 1/2 change from
this effect
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? Spectroscopic efficiency modeled as
exp(-mV/?)? ? Eff(spec) is included in fitting
prior ? Assign w-syst error 1/2 change from
this effect
a) SDSS-only b) SDSS ESSENCE SNLS c) Nearby
SDSS d) Nearby SDSS ESSENCE SNLS e) Nearby
ESSENCE SNLS
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Hubble Anomalya.k.a Hubble Bubble Conley et.
al. astro-ph/0705.0367

• Hubble anomaly in LOWZ sample cz7500 km/s
• About x2 smaller with RV1.9 (compared to
  RV3.1), but still there
• Error bars reflect RMS spread

?
?fit from data ?calc calculated from
concordance model
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Hubble Anomaly

• SDSS data suggests z gt.025 (instead of
  .015) to avoid Hubble anomaly.
• Reduces LOWZ sample from 44 to 26 SNe Ia.
• Increases w by .1
• Add .05 to w-syst

(2005 only)?
Preliminary
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MLCS vs. SALT2

• ? Now that we use MLCS-RV value consistent with
  SALT2-?, cosmology results become more discrepant
  ! Puzzling ??
• ? Ignore fitting prior allow AV lt 0
  ? ?w .06 ltlt MLCS-SALT2
  discrepancy
• ? Discrepancy is from model, NOT from SDSS data
• ? Guesses difference is in the training or
  problem in treating efficiency in one of the
  methods
• ? Comparisons still in progress

67
What Next?
Weak Lensing
CMB Polarization
IR Observation
LSST
JDEM
Clusters
Primordial Neutrinos?
SN Expansion History
68
Conclusions

• ? Paper in preparation (with 99 SDSS SNe Ia, z
  0.05-0.40)?
• ? side-by-side comparisons of MLCS vs. SALT2
• ? SDSS photometric SNe Ia zhost are used to
  measure dust properties (RV) important step
  toward using photo-SN in Hubble diagram, and
  quantifying survey efficiency.
• ? SDSS SN with z lt .15 may help understand low-z
  Hubble anomaly.
• ? Need publicly available training codes to
  optimize training and evaluate systematic errors.
• ? all SDSS-based analysis (fitter sim) is
  publicly available now data available with
  paper.
• ? Three-season SDSS SN survey is done. Still
  acquiring host-galaxy redshifts to improve
  measurement of dust properties and for more SN
  Ia on the Hubble diagram.