Asteroseismology of Sun-like Stars Travis Metcalfe (HAO)

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)

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

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

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

2D oscillations: drums Radial modes Non-radial modes

2D oscillations: drums Radial modes Non-radial modes

3D oscillations: stars Radial modes Non-radial modes

3D oscillations: stars Radial modes Non-radial modes

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

Space missions will soon revolutionize the observations

Space missions will soon revolutionize the observations

Space missions will soon revolutionize the observations

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

Optimization Easy

Optimization Hard

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

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

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.

Evolutionary operators

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

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

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