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Optimizing Galaxy Simulations using FGST Observations

Optimizing Galaxy Simulations using FGST Observations. Andrew McLeod SULI Presentation August 13, 2009. GALPROP. Simulates the gamma ray and cosmic ray sky given a set of initial conditions and physical parameters Allows a priori predictions to be compared to astronomical data. GALPROP.

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Optimizing Galaxy Simulations using FGST Observations

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  1. Optimizing Galaxy Simulations using FGST Observations Andrew McLeod SULI Presentation August 13, 2009

  2. GALPROP • Simulates the gamma ray and cosmic ray sky given a set of initial conditions and physical parameters • Allows a priori predictions to be compared to astronomical data

  3. GALPROP Method • Calculates a density field of cosmic rays given the distribution of cosmic ray sources (pulsars, supernova remnants) as an initial condition • Computes the interaction of these cosmic rays with the interstellar gas field, radiation field, and magnetic field

  4. “Propagation of cosmic rays: nuclear physics in cosmic ray studies”, Igor V. Moskalenko Source: http://galprop.stanford.edu/web_galprop/galprop_manual/manual2.html GALPROP

  5. GALPROP Predicted gamma ray sky from Bremsstrahlung near our solar system

  6. GALPROP

  7. Simulated Fermi Data (>1 GeV, 1 yr) The Project Current parameter set optimized using EGRET data (1991-1994) Optimize to new Fermi data

  8. GaDGET Calculates how well GALPROP models fit gamma ray sky detected by Fermi • Fit-weights are computed for the energy bins of each component • Calculates model’s statistical likelihood • Produces sky-map of residual difference between fit-weight adjusted sky-map and Fermi data

  9. GaDGET

  10. Optimization GALPROP parameters can be varied • Galactic Dimensions • Cosmic Ray Injection Spectra • Source Distribution • Diffusion Coefficient ~ 40 dimensional parameter space

  11. Optimization Model Analysis (MAn) software developed for this project • Analysis settings defined in a specification file • Thirty-five different comparisons plotted • Many user-defined setting; easily adaptable for future model optimization

  12. Optimization

  13. Results Previously used GALPROP parameters physically feasible, but not optimal Current optimized parameters imply that (relative to previous estimates): • The diffusion coefficient governing the propagation of cosmic rays depends more heavily on momentum • Cosmic ray source distribution peaks more sharply • Gamma ray producing processes can occur farther away from the galactic disk

  14. Potential Applications • Indirect determination of Milky Way parameters • Better understand the processes by which cosmic rays propagate • Study extragalactic gamma ray spectrum

  15. Acknowledgments • I would like to express my deep gratitude to my mentor, Markus Ackermann, for helping me define and carry out this project, as well as to my co-workers Josh Lande and Keith Bechtol who helped in its implementation. • Also, thanks to Steve Rock, SueVon Gee, Vivian Lee, and Elizabeth Smith for their stewardship of the SULI program. • Finally, thanks to the DOE Office of Science and SLAC for sponsoring the SULI program.

  16. Works Cited • Moskalenko, Igor. “Modeling of the Galactic diffuse continuum gamma-ray emission” 6th INTEGRAL Workshop, Moscow, Russia. 2006. • Moskalenko, Igor. “Propagation of Cosmic Rays and Diffuse Galactic Gamma Rays” Nuclear Data for Science and Technology, Santa Fe, New Mexico. 2004. • Strong, Andrew. “GALPROP: a Cosmic-ray propagation and Gamma-ray code” Tools for SUSY, Annecy, France. 2006.

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