Institute for Computational Astrophysics

Image of the Month


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Many stars are (literally) vibrating. Because the vibrations are sound waves trapped inside the star they yield a rich and complex harmonic structure (the oscillation spectra) that when interpreted through computer modeling gives us a picture of the interior of the star. The oscillations are visible as minute (1 part in 1 million) variations in the star’s luminosity and can be seen from satellites (such as Kepler) that are sensitive to small variations in the light output from stars.

 To interpret the oscillation spectra astronomers compare numerically computed oscillation spectra of stellar models to the observed star’s oscillation spectrum. Unfortunately, the complicated physics of the surface layers, which are highly turbulent, dominates the oscillation spectra and hinders efforts to explore the physics of the deeper interior.

 In Fig. A we show an echelle diagram for a model star. The diagram plots the frequency of each oscillation mode in a manner that reveals its harmonic structure. We have used a simple heuristic model of the surface physics to show its potential impact on the frequencies. This can be seen as an increasing size of the deviation of the red and blue curves from the black, reference curve).

 Unfortunately, the observed oscillations rarely extend to the lower frequencies where the surface effect is minimal. Consequently, any model fit to the observations could be significantly skewed by the unknown magnitude of the surface effect. We get around this issue by using Bayesian statistics and marginalizing out all surface effects. Effectively, when determining the probability of how well a given set of models fit the observed oscillation spectrum, we cancel out all possible surface effects. By doing so in all our models, we can then evaluate the other model physics.

 In Fig. B we show an example of how well this works. A test model was constructed with some interesting interior physics; specifically, it was given a 0.2 pressure scale height overshoot beyond the outer convective core boundary. In addition we contaminated the oscillation spectra of the models with a Gaussian uncertainty of 0.5 µHz and added a surface effect perturbation. We then used our Bayesian codes to marginalize the (unknown) surface effect (effectively cancel it out of the comparisons) and compare our standard models with varying amounts of overshoot to the three test models. The Bayesian analysis determines which models have the highest probability (in figure B this is represented by the Evidence) of being a fit to the test model. Models with the highest evidence are the most probable. In all three test models, the code correctly identified the 0.2 core overshoot models as the most likely best model fits; even when there was significant surface effect contamination.

Dr. David Guenther - ICA & SMU Astronomy & Physics Professor

Introduction to ICA

The Institute for Computational Astrophysics (ICA) was formed to expand an area of expertise within the Department of Astronomy and Physics at Saint Mary's University. Since its creation in late 2001, the ICA has grown to six full time faculty members, and at any time there are on the order of eight graduate students and two post doctoral fellows amongst the ICA membership. This does not include the other faculty, graduate students, and post doctoral fellows in the Department of Astronomy and Physics. The ICA promotes research in computational astrophysics through the research and publication activities of the individual members, by hosting visitors and colloquium speakers, and by having all its members participate in national and international conferences.

The ICA is also responsible for providing its members with access to high performance computing resources. The ICA has its own small cluster for code development, but most computing is done through ACENET, the advanced research computing provider for Atlantic Canada. ACENET also provides computational research consulting expertise, training, and collaboration and visualization tools on the Saint Mary's campus. The ICA has played a significant role in the formation and direction of ACENET since its inception in 2003, with several members involved in areas such as Principal Investigator, Research Directorate member, and the national Resource Allocation Committee for Compute Canada, ACENET’s national partner. ACENET employs three full time technical support personnel at Saint Mary's, and ICA members have been major beneficiaries of their expertise.

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