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Parameterizing Dark Energy Z. Huang, R. J. Bond, L. Kofman Canadian Institute of Theoretical Astrophysics.

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Presentation on theme: "Parameterizing Dark Energy Z. Huang, R. J. Bond, L. Kofman Canadian Institute of Theoretical Astrophysics."— Presentation transcript:

1 Parameterizing Dark Energy Z. Huang, R. J. Bond, L. Kofman Canadian Institute of Theoretical Astrophysics

2 w - Dark Energy Equation of State Constant w=w 0 w(a)=w 0 +w a (1-a)Principal components Data: CMB + SN + LSS + WL + Lya Code: modified cosmomc w 0 = -0.98 ± 0.05  1 =0.12  2 =0.32  3 =0.63

3 What we know about w Phenomenologically w is close to –1 at low redshift Rich information at z 4 Results depend on parametrization. Need theoretical priors. What if we start from physics?

4 Use physics to solve problems When Canadian plug does not fit UK socket…

5 Dynamics of Quintessence/Phantom we do need assumptions simplicity of w(a). simplicity of V(φ).

6 Dynamics of Quintessence/Phantom define Field equation+ Friedmann Equations Popular story of quintessence: Fast rolling (large  V ) in early universe (scaling regime); Slow rolling (small  V ) in late universe.

7 Simplicity assumptions 1.V(  ) is monotonic. 2.Quintessence rolls down (dV/dt 0). 3.w is close to -1 at low redshift. Quantitatively, |1+w|<0.4 at 0<z<1. 4.V(  ) is a “simple” function. Quantitatively, |  V | is less than or of the same order of either Planck scale or  V. examples, V(  ) = V 0 exp(-  ) V(  ) = V 0 + V 1  V(  ) = V 0  n (n=0,±1, ±2, ±3,…) In the relevant redshift range (e.g. 0<z<4),

8 1-parameter parametrization Additional assumption: slow-roll at 0<z<10 (initial velocity Hubble-damped at low redshift). where  s >0, quintessence  s =0, cosmological constant  s <0, phantom CMB + SN + LSS + WL + Lya “average slope”  s =

9 Initial velocity is damped by Hubble friction

10 Time variation of  V is not important Given solution w(a) and  V (a), define trajectory variables:  s =  V  uniformly averaged at 1/3<a<1.  w = (1+w)/f(a/a eq ). (remind: 1-param formula is w fit =-1+  s f(a/a eq ))

11 Constraint Equation Define w 0 =w| a=1, w a =-dw/da| a=1 w 0 and w a are functions of (  s, Ω m ). Numerical fitting yields

12 binned SNe samples (192 samples) Some w 0 -w a mimic cosmological constant

13 2-parameter parametrization Assumption: slow-roll at 0<z<2 (with possible non-damped velocity). Hubble damping term CMB + SN + LSS + WL + Lya

14 2-parameter parametrization - residual velocity at low redshift.

15 3-parameter parametrization In general  V varies. Assuming no oscillation, we model Other corrections can only be numerically fitted: redefine a eq. O(θ 3 ) term numerical fitting. a s -  s power suppression (if  s and a s are both large, the power of Hubble damping term would be suppressed). When all smoke clears 

16 3-parameter parametrization

17 3-parameter fitting Perfectly fits slow-to-moderate roll.

18 Fit wild rising trajectories

19 Measuring 3 parameters Use 3-parameter for 0 4)=w h (free parameter).

20 Comparing 1-2-3-parameter 1-parameter: use 1-param formula for all redshift. 2-parameter: use 2-param formula for 0 2)=w h (free constant). 3-parameter: use 3-param formula for 0 4)=w h (free constant). Conclusion: all the complications are irrelevant, now only can measure  s CMB + SN + WL + LSS +Lya

21 Forecast: Planck + JDEM SN + DUNE WL

22 Thawing, freezing or non-monotonic? Thawing: w monotonically deviate from -1. Freezing: w monotonically approaches -1. Our parameterization with flat priors. Roughly 15 percent thawing, 8 percent freezing, most are non-monotonic. With freezing prior: With thawing prior:

23 Conclusions  For a wide class of quintessence/phantom models, the functional form V(φ) in the near future is observationally immeasurable. Only a key trajectory parameter  s = (1/16πG) can be well measured.  The second parameter a s can only be constrained to be less than ~0.3.  For current observational data, even with (physically motivated) dynamic w(a) parametrization, cosmological constant remains to be the best and simplest model.


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