SDSSII Supernova Survey

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SDSS-II Supernova Survey

• Josh Frieman
• Leopoldina Dark Energy Conference
• October 8, 2008

See also poster by Hubert Lampeitl, talk by Bob
Nichol
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SN Models and Observations

• SN cosmology based on a purely empirical approach
  (Phillips)
• SN observations over the last decade have
  strengthened evidence for cosmic
• acceleration, but dark energy constraints
  now dominated by systematic errors
• SNe will be one of 3 dark energy probes pursued
  by JDEM
• Reaching JDEM level of precision for SNe will
  require improved control
• of systematics
• Improved SN modeling, better empirical approaches
  to estimating SN distances,
• and better data are all important weapons
  in the arsenal to reduce systematics
• Current empirical distance estimators are limited
  by the paucity of high-quality
• input/training data. The situation is
  improving (CfA, CSP, KAIT, SNF, SDSS),
• but we need better, homogeneous data at
  low/intermediate redshifts and a
• systematic approach to ingesting them to
  build better empirical estimators.
• Will current ground-based SN surveys
  deliver what we need for JDEM?

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Published Light Curves for Nearby Supernovae
Nearby SNe used to train distance estimators and
anchor Hubble diagram Heterogeneous published
sample, subject to various selection biases
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Cosmic Acceleration Discovery from
High-redshift SNe Ia SNe at z0.5 are 25
fainter than in an open Universe with same value
of ?m
Desert still there 10 years later
?? 0.7 ?? 0. ?m 1.
Technological Redshift Desert Possible
photometric offsets between low- and
high-redshift data
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SDSS II Supernova Survey Goals

• Obtain few hundred high-quality SNe Ia light
  curves in the redshift desert z0.05-0.4 for
  continuous Hubble diagram
• Spectroscopic follow-up for redshifts, SN typing,
  and to study diversity of SN features
• Probe Dark Energy and systematics in redshift
  range complementary to other surveys
• Well-observed, homogeneous sample to anchor
  Hubble diagram train distance estimators
• Large survey volume rare peculiar SNe, probe
  outliers of population to test SN models

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Frieman, et al (2008) Sako, et al (2008)
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Spectroscopic follow-up telescopes
R. Miquel, M. Molla
P. Challis, G. Narayan, R. Kirshner
CfA team
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Searching For Supernovae
Search Template
Difference

• 2005
• 118,693 objects scanned
• 10,937 unique candidates
• 130 confirmed Ia
• 2006
• 14,430 scanned
• 3,694 candidates
• 193 confirmed Ia
• 2007
• 13,613 scanned
• 3,962 candidates
• 175 confirmed Ia

g r i

• Positional match to remove movers
• Insert fake SNe to monitor efficiency

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B. Dilday
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Redshift Distribution for SNe Ia
and counting
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SDSS SN Light- curves Holtzman et al (2008)
Well-sampled, multi-band light curves, including
measurements before peak light
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Spectroscopic Target Selection
2 Epochs SN Ia Fit SN Ibc Fit SN II
Fit
Sako etal 2008
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Spectroscopic Target Selection
2 Epochs SN Ia Fit SN Ibc Fit SN II
Fit
31 Epochs SN Ia Fit SN Ibc Fit SN II
Fit
Fit with template library Classification gt90 a
ccurate after 2-3 epochs Redshifts 5-10
accurate Sako etal 2008
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SN and Host Spectroscopy

• MDM 2.4m
• NOT 2.6m
• APO 3.5m
• NTT 3.6m
• KPNO 4m
• WHT 4.2m
• Subaru 8.2m
• HET 9.2m
• Keck 10m
• Magellan 6m
• TNG 3.5m
• SALT 10m

20052006
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SDSS SN Ia Spectra
1000 spectra taken over 3 seasons
Zheng et al (2008)
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Fitting SN light curves I MLCS2k2

• Multicolor Light Curve Shape (Riess et al '98
  Jha et al '07)
• Model SN light curves as a single parameter
  family, trained on low-z UBVRI data from the
  literature
• Assumes SN color variations are due to dust
  extinction, subject to prior

P(Av)
time-dependent model vectors
fit parameters
Time of maximum distance modulus dust
law extinction stretch/decline rate
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MLCS2k2 model templates
Jha et al, 2007

• ? -0.3 bright, broad
• ? 1.2 faint, narrow

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Fitting SN Light curves II SALT2
Guy et al

• Fit each light curve using rest-frame spectral
  surfaces
• Transform to observer frame
• Light curves fit individually, but distances
  only estimated globally
• Not trained just on low-redshift data distances
  are cosmology-dependent, flat priors on model
  parameters

light-curve shape
color term
Global fit parameters, determined along with
cosmological parameters
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Light Curve Fitting with MLCS2k2 and SALT2
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Monte Carlo Simulations match data distributions
Use actual observing conditions (local sky,
zero-points, PSF, etc)
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Model Spectroscopic Photometric Efficiency
Redshift distribution for all SNe passing
photometric selection cuts (spectroscopically
complete sample) Data Need to model biases due
to whats missing Difficult to model
spectroscopic selection
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Extract AV Distribution from SDSS
(no prior)
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Extract RV distribution from SDSS SN data

• MLCS previously used Milky Way avg RV3.1
• Lower RV more consistent with SALT2 color law
• Not conventional dust

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Preliminary Cosmology Results
w open
Kessler, Becker, et al. 2008
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Issues with rest-frame U band
epoch

• Data vs. SALT2 Model Residuals
• Similar Low-z vs. High-z discrepancy seen in MLCS
• MLCS trained only on Low-z, SALT2 model dominated
  by SNLS
• Similar differences seen in rest-frame UV spectra
  (Foley et al)

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SN Ia vs. Host Galaxy Properties I
Smith et al
Bright SN
Luminosity/Decline Rate
Faint
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SN Ia vs. Host Galaxy Properties II
Is reddening local to the SN environment?
Smith et al
Color/reddening
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SN Ia vs. Host Galaxy Properties III

• Preliminary

Two SN Ia Populations? Implications for
SN cosmology host-galaxy population evolution
Smith et al
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Future Improved SN Ia Distances
Fit Cosmology
Train Fitters