Jeffery Jorges, '17
Jeffery Jorges was born and raised in South Florida. He is pursuing a Bachelor’s degree in Physics, specializing in Astronomy. He serves as the current president of UCF’s Astronomy Society and vice-president of UCF’s chapter of the Society of Physics students working to help share his love of science with others by participating in science outreach for his university and general public. Since January 2015 he has also worked as a research assistant at UCF’s Center Of Microgravity Research studying low-velocity collisional systems to better understand the evolution of planetary ring systems and the formation of planetesimals. His academic interest is in using computational methods in astrophysics to study dark matter as well as the formation and evolution of galaxies. Jeffery aspires to obtain his Ph.D. in Astrophysics and become faculty at a university doing research on dark matter and galaxies.
Dr. Adrienne Dove
Physics, Astronomy Specialization
Ph.D. in Astrophysics
Scaling the Peaks: Cataloging Halos from Cosmological N-Body Simulations
Conducted at the University of California, Santa Cruz
Mentors: : Dr. Brant Robertson, Department of Astronomy & Astrophysics, University of California, Santa Cruz
Abstract: Dark matter comprises most of the mass of structures in the universe, and forms gravitationally-bound dark matter halos that host galaxies. Although we are unable to study dark matter structures through direct observations, we can use cosmological N-body simulations to model theoretically the formation of dark matter halos and learn about their properties. This modeling requires the rapid and reliable identification of dark matter halos in the simulation data through specially-designed “halo finder” algorithms. Current algorithms developed to analyze these types of simulations are limited by the size of the simulation, causing them to become more inefficient as the size of the simulation grows. We present a new halo finder code ScalePeaks designed to process large cosmological simulations quickly and efficiently, identify dark matter halos, and measure their properties. Our code processes the simulation data in a massively parallel way, allowing us to leverage new computer architectures for increase computational efficiency. We describe the physical algorithm of our method and show how to perform end-to-end analysis of cosmological simulations including generating initial conditions, evolving the system from early times to the present day, and identifying and characterizing the resulting population of dark matter halos. ScalePeaks allows us to better understand our universe by being able to now study it thought these simulations with an increased speed, at larger sizes, and in more detail to be able learn more about the large scale structure of our universe.
Jorges, Jeffery, "Jeffery Jorges, '17" (2017). McNair Scholars. 10.