1. Asteroseismology of Sun-like Stars
Travis Metcalfe (HAO)

2. The Internal Constitution of the Stars
“At first sight it would seem that the deep interior of the sun and stars is less accessible to scientific investigation than any other region of the universe.
Our telescopes may probe farther and farther into the depths of space; but how can we ever obtain certain knowledge of that which is hidden behind substantial barriers?
What appliance can pierce through the outer layers of a star and test the conditions within?”
(written in 1926)
Sir Arthur Eddington
(1882 – 1944)

3. Seismology: seeing with sound
Convection creates acoustic noise – some of it resonates

4. Motivation
New opportunities to probe the fundamental physics of solar and stellar models.
Understanding the solar structure and evolution in a broader physical context.

5. 1D oscillations: violin strings
Fundamental
Third overtone
First overtone
Second overtone

6. 2D oscillations: drums
Radial modes
Non-radial modes

7. 2D oscillations: drums
Radial modes
Non-radial modes

8. 3D oscillations: stars
Radial modes
Non-radial modes

9. 3D oscillations: stars
Radial modes
Non-radial modes

10. Pulsations cause variations in spectral lines and brightness
Observations
Pulsations cause variations in spectral lines and brightness

11. Space missions will soon revolutionize the observations

12. Space missions will soon revolutionize the observations

13. Space missions will soon revolutionize the observations

14. Matching models to observations is an optimization problem
Epistemology
Matching models to observations is an optimization problem

15. Optimization
Easy

16. Optimization
Hard

17. Evolution as optimization
“Evolution is cleverer than you are.” – Francis Crick

18. Evolution as optimization
“Evolution is cleverer than you are.” – Francis Crick

19. Genetic algorithms Generate N random trial sets of parameter values.
Evaluate the model for each trial and calculate the variance.
Assign a “fitness” to each trial, inversely proportional to the variance.
Select a new population from the old one, weighted by the fitness.
Encode-Breed-Mutate-Decode
Loop to step 2 until the solution converges.

20. Evolutionary operators

21. Parallel computing Genetic algorithms are intrinsically parallelizable
Each iteration typically has 128 model evaluations
Number of processors sets the number of models that can be evaluated in parallel
Also need multiple runs with different random initialization

22. Stellar parameters Total Mass Surface Temperature Chemical composition
Convective efficiency
Internal chemical gradients
Rotation rate
Age / Evolutionary status

23. The Future: Eddington et al.
2007 and beyond: a flood of unprecedented observations