Testing Dark Energy with Supernova (and other cosmological probes)

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16.9.2009 Marek Kowalski Grasscosmofun

Testing Dark Energy with Supernova(and other cosmological probes)

Marek Kowalski

Physikalisches Institut Universität Bonn16.9.2009, Szczecin

Content

Introduction to supernova cosmology

SNe observations & cosmological parameters

Constraints on selected models

1998: Discovery of dark energy

16.9.2009 Marek Kowalski Grasscosmofun M

Weak lensing mass census Large scale structure Baryon Accoustic OscillationsM= 0.3

Flat universe

+ M = 1.01+/-0.02

1998: Discovery of dark energy

WMAP 2006

SDSS, Eisenstein et al. (2005)

Baryon Acoustic Oscillation (BAO)

16.9.2009 Marek Kowalski Grasscosmofun 1

Supernova Type Ia

•Type Ia supernovae (SNe Ia) provide bright “standard candle” that can be used to construct a Hubble diagram.

• Accretion sends mass of white dwarf star to Chandrasekhar limit leading to gravitational collapse and a thermo-nuclear explosion of its outer layers.

• Each one is a strikingly similar explosion event with nearly the same peak intensity.

Astronomers think in…

Magnitudes: m = -2.5 log(Flux) + constant

Filter: B,V,R,I,Z (400-900 nm)

“Standard” Candles

• Nearby supernovae used to study SNe light curve (z<0.1)• Brightness not quite standard

Stretching the timescale:

t'= s× t

M '= M+α (s−1)

Correcting the Brightness:

Intrinsically brighter SNe have wider

lightcurves.

Spectra used for Identification & Redshift determination

Searching for Supernovae

Reference new picture difference

SN-Candidate

One example - the SNLS: • Canadian-French-Hawaii Telescope: 3.6 m

• MegaCam Camera: 3.6 108 pixels

• 5-year program finding about 500 SNe Ia

SNLS-Lightcurves

Sloan Digitial Sky Survey (SDSS)

First year papers: arXiv:0908.4274, arXiv:0908.4276

16.9.2009 Marek Kowalski Grasscosmofun Sloan Digital Sky Survey

SNe at large Redshifts (z>1)Observations from Space with the Hubble Space Telescopes:

SNe Type Ia & Acceleration of the Universe

Normalization

fainter then expected

1 00.73 0.270 1

slightly brighter M

Supernova Cosmology Project Kowalski et al., Ap.J. (2008)

Analysis aspects

Consistent Lightcurve FitsSALT fitter used for all SNe using mostly original band-passes (Guy et al 2005/2007).

Blind analysisThe analysis (i.g. cuts) were developed on a blinded data set, all luminosities were offsetby a hidden redshift-dependent amount.

Robust analysisInitial cosmological fit using median statistics.3-sigma outliers removed. Assignment of sample dependent dispersion.

Stretch and color corrected luminosity:

Large sample of SNe allows new studies of systematic errors

A heterogenous data sample

Study of - mean deviation- residual slope

A heterogenous data sample

Test for Tension

high-zlow

mean deviation: OK

Test for Tension

high-zlow

residual slope: (OK)

Testing SN evolution by subdividing the sample

Evolution test:Redshift

No significant evidence for evolution!

Evolution test:Population

Systematic errors

Nuisance parameters for systematic errors: μ =mmax −M +α (s −1) − βc + ΔM + ΔM i

common for all z>0.2 SNe sample dependent

Perlmutter et al., 1999

Results: Cosmological fit parameters

m = 0.274 ± 0.016(stat) ± 0.012(sys)

Ωm = 0.285 ± 0.020(stat) ± 0.010(sys)

Ωk = −0.010 ± 0.010(stat) ± 0.005(sys)

Combination of SNe with: BAO (Eisenstein et. al., 2005)CMB (WMAP-5 year data, 2008)

For a flat Universe:

… and with curvature:

Equation of state: w =p/

w =−0.969 ±0.061(stat) ±0.065(sys)SNe + BAO + CMB

... and allowing for curvature: w =−1.001±0.071(stat) ±0.081(sys)

With systematics

Redshift dependent w

w=w0 + (1-a) wa

Redshift dependent w

Assuming step-wise constant w:

A floating non-SNe bin to decouple low from high-redshift constraints

Constraining Dark Energy models

- ln(1+z)

Constraining Models (I):Growing Neutrinos

w0 =−1+m,0

12 eV C. Wetterich (2007), L. Amendola et al. (2007),

Scalar-field couples to massive neutrinos.

Once neutrinos become sub-relativistic, one obtains -like behavior.

Today: massive neutrinos and small offset of w from -1:

Early dark energy (e) is second parameter and LSS and WMAP constrain e to be less then a few % (Doran et al. 2007). We assume a 10% linear growth prior.

D.Rubin, E. Linder, MK et al., ApJ (2009)

Constraining Models (I):Growing Neutrinos

m<1.2 (h/0.7)2 eV @ 95 CL stat error onlym<2.1 (h/0.7)2 eV @ 95 CL with sys error

with sys error

Lab constraints:m2 eVKatrin sensitivity:m 0.2 eVoszillations:m0.05 eV

Constraining Models (II):Braneworld Gravity

Gravity leaking into extra dimensions on the scales of the Hubble radius can mimic cosmic acceleration.Dvali, Gabadadze, Porrati (2000) - DGP

We chose dark matter and curvature as DGP parameters to obtain an effective Dark Energy equation of state:

w(z) =−1−k(z)

1+m(z)−k(z)

DGP-Model versus CDM: 2

stat = 15.0 (stat error only) 2

sys = 2.7 (with sys. error)

Constraining Models (II):Braneworld Gravity

D.Rubin, E. Linder, MK et al., ApJ (2009)

Constraining Models (III):Backreaction

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H(z) = H0 ×

Ωm (1+ z)3 + (1− Ωm )(1+ z)−n

From a solution to the Buchert Eq:

Perturbation expansion

(if appliciable): n= -1

n =1.6±0.9 @ 68% stat error onlyΩm=0.42±0.04 @ 68% stat error only

Boljakov et al. 2008Larena et al. 2008

Future projectsfor Dark Energy

Project z-range # SNe

Pan-STARRS 0.1-0.5 ~104

LSST (2015) 0.1-0.9 ~106

SNAP (2018) 0.2-3.0 >3000

(JDEM/Euclid)SNAP

Other important future methods:Weak lensing Cluster rates Baryon acoustic osciallation

Summary

SNe as standard candles provide a direct measurement of the Acceleration history

Combining SNe, BAO, and CMB we (slowly) become able to test individual models for dark energy

Today, systematic uncertainties are of similar size as statistical uncertainties (some even say they are larger). Next generation surveys offer the chance to improve on both.

Riess GoldDavis 2007

Union w/oNew SCP SNe

without the 8new nearby SNe

184 SNe 192 SNe

Comparison with previous compilations

Testing Dark Energy with Supernova (and other cosmological probes)

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