Deep Learning Methods for High Energy Physics

BIDS Research Affiliate Benjamin Nachman offers this project through UC Berkeley's Undergraduate Research Apprentice Program (URAP).

Project Description:

This is an exciting time in high energy physics: there are many experimental and theoretical hints for new phenomena (such as dark matter), yet we do not yet have any significant evidence for new particles or forces of nature since the discovery of the Higgs Boson in 2012. This could be because our experiments are not sensitive enough, that the new particles are rare, or that we are not looking in the right place. The goal of this project is to develop, adapt, and deploy state-of-the-art deep learning methods to enhance the search for new particles.

Our group has developed a variety of deep learning methods to automatically explore high-dimensional particle physics data with as little model bias as possible. This URAP project will involve extending and/or applying these techniques to a variety of physical systems including collider physics (proton-proton, electron-proton, and electron-positron colliders such as the Large Hadron Collider, the Electron Ion Collider, and the International Linear Collider), neutrino physics, and astroparticle physics (e.g. the Gaia space observatory).

The exact work will depend on the experience, availability, interest, and progress of the student. Research in this area is at the intersection of theory, experiment, and applied statistics/machine learning. At least 6-8 hours are typically needed to make significant progress.

Qualifications: Applicants should be interested in particle physics and machine learning solutions to physics challenges. Majoring/minoring in Physics or Astronomy would be great, but this is not required; majors in EECS/CS/Data Science/Math/Statistics or related disciplines would be most welcome with significant interest in physics topics. Experience with at least one programming language (Java/C++/Matlab/Python/Julia/etc.) is required. The research will mostly be carried out in Python, so this would be desired but is not required. Great teamwork (e.g. communication skills, punctuality, organization) are necessary for a success. While not required, recommended skills include familiarity with basic probability and statistics, experience with communication and collaboration tools like Slack and Github, experience with deep learning packages like Keras/Tensorflow or PyTorch. If you have any experience with these topics, please mention it in your application.

Off-Campus Research Site: Our default mode of operation will be virtual, meeting on Zoom and communicating via Slack and email. Depending on the status of the pandemic, it may be possible to meet in person at the Berkeley Institute for Data Science (BIDS) on campus and/or at the Physics Division (building 50) at Berkeley Lab (20 min walk / 10 min shuttle ride from campus). 

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BIDS Affiliates

Benjamin Nachman

Physics Division, LBNL
Research Affiliate