Constraining Dark Energy: First Results from the SDSSII Supernova Survey

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Constraining Dark Energy First Results from the
SDSS-II Supernova Survey

• Josh Frieman
• IFAE, Barcelona
• March 30, 2009

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Components of the Universe
25 Dark Matter Dominant in Galaxies
Clusters 70 Dark Energy Dominates the
Universe, causing Expansion to speed up 4
baryons

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1980s Will the Universe expand forever or
recollapse in a Big Crunch? How much Dark
Matter is there?
Cosmic Scale Factor
Empty
Just open
In all these cases, Universe decelerates due to
gravity
Closed
Today
Cosmic Time
4
p ?? (w ?1)
Cosmic Scale Factor
Accelerating
Empty
1998 discovered that the Universe started
speeding up about 5 billion years ago
Just open
Closed
Today
Cosmic Time
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Cosmic Acceleration

• What can cause this?
• The Universe is filled with a component that
  gives
• rise to gravitational repulsion. We now
  call this
• Dark Energy
• Einsteins theory of General Relativity is wrong
  on cosmic distance scales.
• 3. We must drop the assumption of
  homogeneity/isotropy Universe is only apparently
  accelerating, due to large-scale structure.

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Acceleration and Dark Energy



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Cosmological Constant ? as Dark Energy
Quantum zero-point fluctuations virtual
particles continuously fluctuate into and out of
the vacuum (via the Uncertainty principle).
Vacuum energy density in Quantum Field
Theory Insert cutoff at kmax M ? Theory
Data
Pauli
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Scalar Field as Dark Energy(inspired by
inflation)

• If Dark Energy is due to a scalar field, j,
  slowly evolving in a potential, V(j) (ignoring
  matter density)
• Density pressure
• Slow roll

V(j)
j
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Loga0/a(t)
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Tragic History of ?a cautionary tale
periodically invoked to solve cosmological
crises, then dropped when they
passed 1916 Einstein static Universe
(greatest blunder of my life?) 1929 1st age
crisis Universe younger than Earth 1967
apparent clustering of quasars at fixed
redshift 1974 inferred distances using galaxy
brightness 1995 2nd age crisis Universe
younger than stars 1998
Supernovae 2000 Cosmic Microwave Background and
Galaxy Surveys Why do we think its different
now?
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Discovery Evidence for Acceleration

• 1998 Type Ia Supernovae
• Supernova Cosmology Project
• High-z Supernova Team
• 2000-1 First CMB Acoustic Peak
• DASI, Boomerang, Maxima
• 2004-5 Galaxy Clustering
• SDSS, 2dF

Independent, robust lines of evidence for the
first time
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Nearby SN 1994D (Ia)
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SN Ia Theory

• Standard model
• Thermonuclear explosions of CO white dwarf
  stars.
• Evolution to criticality
• Accretion from a binary companion leads to
  growth of the WD to the critical Chandrasekhar
  mass, 1.4Msun
• Details of explosion not well understood. About
  half of the mass is burned to Nickel. Light-curve
  fueled by radioactive decay, peak luminosity
  determined by MNickel

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SN Ia Spectral Homogeneity
from SDSS Supernova Survey
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Luminosity
?m15
15 days
Time
Empirical Correlation Brighter SNe Ia decline
more slowly Phillips 1993
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• Type Ia SN
• Peak Brightness
• as calibrated
• Standard Candle
• Peak brightness
• correlates with
• decline rate
• Variety of algorithms for modeling these
  correlations
• After correction,
• ? 0.15 mag
• (7 distance error)

Luminosity
Time
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Correction for Brightness-Decline relation
reduces scatter in nearby SN Ia Hubble
Diagram Distance modulus (log measure of
distance) Riess etal 96
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Acceleration Discovery from High-redshift SNe
Ia Apply same Brightness-Decline relation at
High-z SNe at z0.5 are 25 fainter than in an
open Universe with same value of ?m Gap at
intermediate z
?? 0.7 ?? 0. ?m 1.
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assuming w?1
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CMB Sound Waves in the Early Universe

• Before H recombination
• Universe is ionized.
• Photons provide enormous pressure and restoring
  force.
• Photon-baryon perturbations oscillate as acoustic
  waves.

• After H recombination
• Universe is neutral.
• Photons can travel freely past the baryons.
• Phase of oscillation at trec affects late-time
  amplitude.

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Acoustic Oscillations in the CMB
Temperature map of the Cosmic Microwave Backgroun
d radiation

• There is a characteristic angular scale, 1
  degree on the sky, set by the distance sound
  waves in the photon-baryon fluid can travel just
  before H recombination sound horizon s cstls

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Geometry of three-dimensional space
Kgt0
Klt0
K0
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Kgt0
K0
Klt0
?
s
CMB Maps
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Angular positions of acoustic peaks probe
spatial curvature of the Universe
Hu
1/?
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Microwave Background AnisotropyProbes Spatial
Curvature
DASI (2001) Pryke et al

• Boomerang (2001) Netterfield et al

Data indicates nearly flat geometry
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Current CMB Results
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SDSS 2.5 meter telescope
Apache Point Observatory
New Mexico

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SDSS Galaxy Distribution
Luminous Red Galaxies
SDSS Galaxy Distribution
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Large-scale Correlations of SDSS Luminous Red
Galaxies
Baryon Acoustic Oscillations seen in Large-scale
Structure mean distance to galaxies at z0.35
Redshift-space Correlation Function
Eisenstein, etal 2005
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Results based on Recent Data Compilation
Only statistical errors shown
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Supernova Legacy Survey (2003-2008)

• 5 year survey, goal 500 distant SNe Ia to
  measure w
• Uses CFHT/Megacam
• 36 CCDs, good blue response
• 4 filters griz for good k-corrections and color
  measurement
• Spectroscopic follow-up on 8-10m

Megaprime
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SNLS Rolling Search
Early light curves
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Ten Years Later Larger, better data sets at high
redshift ESSENCE Wood-Vasey, etal
Miknaitis, etal SNLS Astier, etal But
redshift desert remains
45 SNe Ia
120 SNe
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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
• Probe Dark Energy in z regime complementary to
  other surveys
• Well-observed sample to anchor Hubble diagram,
  train light-curve fitters, and explore
  systematics of SN Ia distances
• Rolling search determine SN/SF rates/properties
  vs. z, environment
• Rest-frame u-band templates for z gt1 surveys
• Large survey volume rare peculiar SNe, probe
  outliers of population

high-cadence, multi-band, well-calibrated
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Spectroscopic follow-up telescopes
R. Miquel, M. Molla, L. Galbany
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• Frieman, et al (2008) Sako, et al (2008)

Results today from 2005 season
Kessler, et al 09 Lampeitl et al 09 Sollerman
et al 09
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Searching For Supernovae
Search Template
Difference

• 2005
• 190,020 objects scanned
• 11,385 unique candidates
• 130 confirmed Ia
• 2006
• 14,441 scanned
• 3,694 candidates
• 193 confirmed Ia
• 2007
• 175 confirmed Ia

g r i

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

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B. Dilday
507 spectroscopically confirmed SNe Ia
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SDSS SN Photometry
Holtzman etal (2008)
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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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Spectroscopic Deconstruction
SN model Host galaxy model Combined model

• Zheng, et al (2008)

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Correct Distance Estimates for Brightness-decline
relation and dust extinction
MLCS2k2 Light-curve templates Jha, etal (2007)
? lt0 bright, broad ? gt0 faint, narrow

• time-dependent model vectors
• trained on Low-z SNe

(plus K-corrections)
fit parameters
Time of maximum distance modulus dust
law extinction stretch/decline rate
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Dust Extinction Law RV
Milky Way
Jha
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Extract RV by matching colors of SDSS SNe to MLCS
simulations

• MLCS previously used Milky Way avg RV3.1
• Lower RV more consistent with SALT color law

D. Cinabro
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Priors Efficiencies
Determine priors and efficiencies from data and
Monte Carlo simulations
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Priors Efficiencies
Inferred P(AV)
Inferred P(?)
Determine priors and efficiencies from data and
Monte Carlo simulations
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Determine Survey Efficiencies
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Monte Carlo Simulations match data distributions
Use recorded observing conditions (local sky,
zero-points, PSF, etc)
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Hubble Diagram
45 SNe Ia
120 SNe
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Hubble Diagram with SDSS SNe 103 SNe Ia from
first season Kessler etal (2009) Lampeitl
etal (2009) Sollerman etal (2009)
45 SNe Ia
120 SNe
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Preliminary Results
SDSSNearby SNe Only
MLCS2k2
SALT-II

• BAO
• SDSS

BAO SDSS
CMB WMAP5
CMB WMAP5
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SALT-II Light-curve Fits
Guy et al

• Fit each light curve using rest-frame spectral
  surfaces
• Light curves fit individually, but distances
  only estimated globally
• Differences from MLCS not trained just on
  low-redshift data flat priors on model
  parameters, espec. color color variations not
  assumed to come only from dust

light-curve shape
color term
Global fit parameters, determined along with
cosmological parameters
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SDSSNearbySNLSESSENCEHST
MLCS2k2
SALT-II
BAO SDSS

• BAO
• SDSS

CMB WMAP5
CMB WMAP5
SALT2 distance moduli for SNLS SNe systematically
higher than MLCS
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Systematic Errors are Dominant
SNBAOCMB constraints
MLCS vs. SALT discrepancy is NOT associated with
SDSS SNe
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SALT vs MLCS template light curves
MLCS SALT
Diagnosis Large difference in Light-curve model
in U-band Use of prior on extinction in MLCS
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SALT vs MLCS
Diagnosis Large difference in Light-curve model
in U-band Use of prior on extinction in MLCS
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The Dark Energy Survey

• Study Dark Energy using
• 4 complementary techniques
• I. Cluster Counts
• II. Weak Lensing
• III. Baryon Acoustic Oscillations
• IV. Supernovae
• Two multiband surveys
• 5000 deg2 g, r, i, z,Y
• smaller area repeat (SNe)
• Build new 3 deg2 camera
• and Data management sytem
• Survey 2011-2016 (525 nights)
• Response to NOAO AO

Blanco 4-meter at CTIO
in systematics in cosmological parameter
degeneracies geometricstructure growth test
Dark Energy vs. Gravity
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The DES Instrument DECam
F8 Mirror
Filters Shutter
3556 mm
CCD Read out
Hexapod
Optical Lenses
1575 mm
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Large Synoptic Survey Telescope

• 8.4m ground based telescope with 10 sq. degree
  field
• 5000 Gbytes/night of data
• Real-time analysis
• Celestial Cinematography

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Conclusions

• Robust evidence for cosmic acceleration from
  Supernovae and other probes
• Systematic errors pose challenges to reaching
  greater precision in dark energy properties
• We have data in hand to help resolve these issues
  for SNe retraining light-curve models using
  SDSS, CSP, CfA, SNF,
• Future experiments will/must be designed to
  control systematic errors and exploit
  complementarity of multiple probes DES,
  PanSTARRS, PAU, LSST, JDEM,