Challenge Facilitate the detection of multiple planets around neighboring stars using advanced Bayesian statistical algorithms. Solution An easy route to parallel computing with gridMathematica provides the necessary speed-up to achieve this goal on inexpensive mutiplecore PC's.

Benefits Provide the necessary statistics on planets and their orbits for testing planetary formation models. Provide a selection of habitable zone planets for follow-up studies aimed at detecting biological activity.

Phil Gregory

Phil Gregory is a professor emeritus in physics and astronomy at the University of British Columbia. He is pioneering the development of advanced Bayesian statistical tools to detect and characterize the small signals induced by planets orbiting neighboring stars. Astronomers have already discovered over 300 extra-solar planets with the majority of these gas giants like Jupiter. The current quest is for Earth like planets within the habitable zone of the host star.

Q: How do you use Mathematica in your work?

A: My latest algorithm uses almost every state of the art numerical tool available including, parallel tempering Markov chain Monte Carlo, simulated annealing and the genetic algorithm. Mathematica provides an excellent environment for efficiently creating and visualizing the performance of these tools. Together, they have allowed me to discover 3 additional planets in a re-analysis of the data for just 4 stars.

Q: Has gridMathematicabeen important to your work?

A: Recently, I was able to port my code to gridMathematica and gain a factor of 7 in speed running on an inexpensive 8 core PC. I was surprised how easy it was to make the change to parallel computing. We suspect that most stars that currently exhibit a single planet have multiple planets. With gridMathematica, I am now in a position to search for multiple planets simultaneously. This will allow us to obtain a clearer picture of the planetary environments of neighboring stars.

Q: What do you appreciate most about Mathematica?

A: I originally made the move to Mathematica in response to my teaching responsibilties. I soon noticed that my ability to code and debug research programs in Mathematica was about 20 times faster than my experience with FORTRAN. As a consequence, the scope and complexity of the problems I can now comfortably explore has greatly increased. The fact that it is a superb all-in-one mathematics, computing, visualizing, documenting and teaching environment is a delight and a great time saver.

• Utilize one single computing environment for research and teaching needs.
• Greatly increase calculation power with minimal changes to existing programs.
• Expands the horizon of what is possible.