1 / 146

Understanding Dark Energy Parameters in Accelerating the Universe

Explore the concept of dark energy parameters and their role in accelerating the universe. Discuss the cosmological constant, cosmic inflation, and modifications to Einstein gravity. Learn about the Dark Energy Task Force's simulated data sets and their impact on our understanding of dark energy. Follow-up questions address the choice of parameters, the impact on specific dark energy models, and the discriminating power of these data sets.

ha
Download Presentation

Understanding Dark Energy Parameters in Accelerating the Universe

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Dark energy paramters Andreas Albrecht (UC Davis) U Chicago Physics 411 guest lecture October 15 2010

  2. How can one accelerate the universe?

  3. How can one accelerate the universe? • A cosmological constant

  4. How can one accelerate the universe? • A cosmological constant AKA

  5. How can one accelerate the universe? • A cosmological constant AKA

  6. How can one accelerate the universe? • A cosmological constant AKA

  7. How can one accelerate the universe? • 2) Cosmic inflation

  8. How can one accelerate the universe? • 2) Cosmic inflation V

  9. How can one accelerate the universe? • 2) Cosmic inflation Dynamical V

  10. How can one accelerate the universe? • A cosmological constant 2) Cosmic inflation

  11. How can one accelerate the universe? • 3) Modify Einstein Gravity

  12. Focus on

  13. Focus on Cosmic scale factor = “time”

  14. Focus on Cosmic scale factor = “time” DETF:

  15. (Free parameters = ∞) Focus on Cosmic scale factor = “time” DETF:

  16. (Free parameters = ∞) Focus on Cosmic scale factor = “time” DETF: (Free parameters = 2)

  17. The Dark Energy Task Force (DETF) • Created specific simulated data sets (Stage 2, Stage 3, Stage 4) • Assessed their impact on our knowledge of dark energy as modeled with the w0-wa parameters

  18. The DETF stages (data models constructed for each one) Stage 2: Underway Stage 3: Medium size/term projects Stage 4: Large longer term projects (ie JDEM, LST) • DETF modeled • SN • Weak Lensing • Baryon Oscillation • Cluster data

  19. DETF Projections Stage 3 Figure of merit Improvement over Stage 2 

  20. DETF Projections Ground Figure of merit Improvement over Stage 2 

  21. DETF Projections Space Figure of merit Improvement over Stage 2 

  22. DETF Projections Figure of merit Improvement over Stage 2  Ground + Space

  23. Followup questions: • In what ways might the choice of DE parameters have skewed the DETF results? • What impact can these data sets have on specific DE models (vs abstract parameters)? • To what extent can these data sets deliver discriminating power between specific DE models? • How is the DoE/ESA/NASA Science Working Group looking at these questions?

  24. Followup questions: • In what ways might the choice of DE parameters have skewed the DETF results? • What impact can these data sets have on specific DE models (vs abstract parameters)? • To what extent can these data sets deliver discriminating power between specific DE models? • How is the DoE/ESA/NASA Science Working Group looking at these questions?

  25. How good is the w(a) ansatz? w0-wa can only do these w DE models can do this (and much more) z

  26. How good is the w(a) ansatz? NB: Better than w0-wa can only do these w & flat DE models can do this (and much more) z

  27. Illustration of stepwise parameterization of w(a) Z=0 Z=4

  28. Illustration of stepwise parameterization of w(a) Measure Z=0 Z=4

  29. Illustration of stepwise parameterization of w(a) Each bin height is a free parameter

  30. Illustration of stepwise parameterization of w(a) Refine bins as much as needed Each bin height is a free parameter

  31. Illustration of stepwise parameterization of w(a) Refine bins as much as needed Z=0 Z=4 Each bin height is a free parameter

  32. Illustration of stepwise parameterization of w(a) Refine bins as much as needed Each bin height is a free parameter

  33. Try N-D stepwise constant w(a) N parameters are coefficients of the “top hat functions” AA & G Bernstein 2006 (astro-ph/0608269 ). More detailed info can be found at http://www.physics.ucdavis.edu/Cosmology/albrecht/MoreInfo0608269/

  34. Try N-D stepwise constant w(a) Used by Huterer & Turner; Huterer & Starkman; Knox et al; Crittenden & Pogosian Linder; Reiss et al; Krauss et al de Putter & Linder; Sullivan et al N parameters are coefficients of the “top hat functions” AA & G Bernstein 2006 (astro-ph/0608269 ). More detailed info can be found at http://www.physics.ucdavis.edu/Cosmology/albrecht/MoreInfo0608269/

  35. Try N-D stepwise constant w(a) • Allows greater variety of w(a) behavior • Allows each experiment to “put its best foot forward” • Any signal rejects Λ N parameters are coefficients of the “top hat functions” AA & G Bernstein 2006

  36. Try N-D stepwise constant w(a) • Allows greater variety of w(a) behavior • Allows each experiment to “put its best foot forward” • Any signal rejects Λ N parameters are coefficients of the “top hat functions” “Convergence” AA & G Bernstein 2006

  37. Q: How do you describe error ellipsis in ND space? A: In terms of N principle axes and corresponding N errors : 2D illustration: Axis 1 Axis 2

  38. Axis 1 Axis 2 Q: How do you describe error ellipsis in ND space? A: In terms of N principle axes and corresponding N errors : Principle component analysis 2D illustration:

  39. Q: How do you describe error ellipsis in ND space? A: In terms of N principle axes and corresponding N errors : NB: in general the s form a complete basis: 2D illustration: The are independently measured qualities with errors Axis 1 Axis 2

  40. Q: How do you describe error ellipsis in ND space? A: In terms of N principle axes and corresponding N errors : NB: in general the s form a complete basis: 2D illustration: The are independently measured qualities with errors Axis 1 Axis 2

  41. z-=4 z =1.5 z =0.25 z =0 Characterizing ND ellipses by principle axes and corresponding errors DETF stage 2 Principle Axes

  42. z-=4 z =1.5 z =0.25 z =0 Characterizing ND ellipses by principle axes and corresponding errors WL Stage 4 Opt Principle Axes

  43. z-=4 z =1.5 z =0.25 z =0 Characterizing ND ellipses by principle axes and corresponding errors WL Stage 4 Opt Principle Axes “Convergence”

  44. DETF(-CL) 9D (-CL)

  45. DETF(-CL) 9D (-CL) Stage 2  Stage 3 = 1 order of magnitude (vs 0.5 for DETF) Stage 2  Stage 4 = 3 orders of magnitude (vs 1 for DETF)

  46. Upshot of ND FoM: • DETF underestimates impact of expts • DETF underestimates relative value of Stage 4 vs Stage 3 • The above can be understood approximately in terms of a simple rescaling (related to higher dimensional parameter space). • DETF FoM is fine for most purposes (ranking, value of combinations etc).

  47. Upshot of NDFoM: • DETF underestimates impact of expts • DETF underestimates relative value of Stage 4 vs Stage 3 • The above can be understood approximately in terms of a simple rescaling (related to higher dimensional parameter space). • DETF FoM is fine for most purposes (ranking, value of combinations etc).

  48. Upshot of NDFoM: • DETF underestimates impact of expts • DETF underestimates relative value of Stage 4 vs Stage 3 • The above can be understood approximately in terms of a simple rescaling (related to higher dimensional parameter space). • DETF FoM is fine for most purposes (ranking, value of combinations etc).

  49. Upshot of NDFoM: • DETF underestimates impact of expts • DETF underestimates relative value of Stage 4 vs Stage 3 • The above can be understood approximately in terms of a simple rescaling (related to higher dimensional parameter space). • DETF FoM is fine for most purposes (ranking, value of combinations etc).

  50. An example of the power of the principle component analysis: Q: I’ve heard the claim that the DETF FoM is unfair to BAO, because w0-wa does not describe the high-z behavior to which BAO is particularly sensitive. Why does this not show up in the 9D analysis?

More Related