Important: This exciting new stream will begin in Spring 2019!

The bold goal of High Energy Physics is to understand the characteristics of the fundamental particles and forces that compose the universe; it strives to understand the ultimate nature of everything. High Energy Physics has made great progress in developing the Standard Model of particle physics, culminating with the discovery of the Higgs boson in 2012, a particle predicted by the Standard Model that gives matter its mass. Despite the great successes of the Standard Model, it is known to be incomplete. Of particular interest is the possibility of extending the Standard Model to incorporate dark matter. Compelling astronomical evidence points to the existence of dark matter, material that does not interact with photons and therefore is non-luminous, yet which constitutes 85% of all the matter in the universe. If this dark matter is composed of as-yet undetected subatomic particles, it will take a new generation of particle detectors to discover them.

This FIRE stream takes you on a journey to help create the next generation of particle detectors, ones that will be used by scientists all over the world to learn more about the basic building blocks of matter and the forces that govern their interactions - especially dark matter. Along the way, you will learn fundamental physics as well as the computational and research skills needed to contribute to this exciting field. The only background needed for this stream is a high school physics course.

You will learn to optimize subatomic particle detector elements through calculation and simulation, investigate how detector design will impact data analysis, work with large data sets, and work in a team to organize, plan, execute, and present your research. Completing this FIRE stream should position you to compete effectively for Research Experiences for Undergraduates at Fermilab, CERN, and other institutions and government labs. The research skills you learn will be useful in many other fields dealing with large data sets, such as astrophysics, climatology, computer science, engineering, and economics.

Faculty Advisor
Dr. Sarah Eno

Faculty Advisor
Dr. Alberto Belloni

Research Educator
Dr. Muge Karagoz