BIDS Research Affiliate Ben Nachman is offering this project (#3) through UC Berkeley's Undergraduate Research Apprentice Program (URAP) for the Fall 2020 academic semester. Eligible undergraduates may apply online August 19-31, 2020.
This is an exciting time in fundamental physics, with many current or planned experiments producing complex data. There are many experimental and theoretical hints for new phenomena (such as dark matter), but 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 investigate this last possibility. We have developed a variety of deep learning methods to automatically explore the high-dimensional data with as little model bias as possible (“less than supervised”). This project will involve developing, integrating, and/or deploying deep learning-based anomaly detection techniques to a variety of physical systems including collider physics (e.g. the Large Hadron Collider) and indirect dark matter detection (e.g. Gaia space observatory). This project will involve developing, integrating, and/or deploying deep learning-based anomaly detection techniques to a variety of physical systems including collider physics (e.g. the Large Hadron Collider) and indirect dark matter detection (e.g. 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.
- Interest 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 ore 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.). 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).
- Basic probability and statistics.
- Experience with communication and collaboration tools like Slack and Github.
- Experience with deep learning packages like Keras/Tensorflow or PyTorch.