2 Result(s)

Search for Magnetic Monopoles: from the Large Hadron Collider to Space

Student can get involved in the following aspects of the project: 1. Development of novel radiation detectors optimized for the magnetic monopole searches. The detectors are to be deployed at the Large Hadron Collider, CERN, and in the Low Earth Orbit using CubeSat satellites. No prior experience with detector development required. 2. Applying Deep Learning techniques to the data analysis of the MoEDAL experiment at the LHC. The work involves operation of a GPU based computing system, application of convolutional neural networks for event classification. No prior experience with machine learning methods required. Familiarity with programming languages (Python, C) is advantageous. Position is initially unpaid. ...

Required Availability
Fall 2017 | Spring 2018 | Summer 2018
Course Credit?
Yes - PH495
Paid Position?
No

Data analysis and optical simulation for the EXO-200/nEXO experiments

Students can work on the following aspects: 1. Development of data quality scripts for the EXO-200 experiment. 2. Understanding light propagation in the EXO-200/nEXO detectors using novel GPU-based optical simulation software. 3. Using convolutional neural networks and other Deep Learning techniques for the EXO-200 data analysis and reconstruction. The work with be performed using the in-house GPU based computing system and the NERSC supercomputer facility at Berkeley. No prior experience with machine learning methods is required. Familiarity with programming languages (Python and/or C) is advantageous. Position is initially unpaid....

Required Availability
Spring 2018 | Summer 2018 | Fall 2017
Course Credit?
Yes - PH495
Paid Position?
No