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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? Sir Arthur Eddington (1882 – 1944) What appliance can pierce through the outer layers of a star and test the conditions within?” (written in 1926) 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 Observations Pulsations cause variations in spectral lines and brightness Space missions Space missions will soon revolutionize the observations Space missions Space missions will soon revolutionize the observations Space missions Space missions will soon revolutionize the observations 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 1. Generate N random trial sets of parameter values. 2. Evaluate the model for each trial and calculate the variance. 3. Assign a “fitness” to each trial, inversely proportional to the variance. 4. Select a new population from the old one, weighted by the fitness. 5. Encode-Breed-Mutate-Decode 6. 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