In the search for new physics at the Large Hadron Collider (LHC) it is necessary to accurately learn the representation of events that may be described in different ways (point clouds, graphs, grids). Different detector systems can lead to different optimal representations and no single approach is ideal for all detector systems. Conventional representations are usually discrete (point clouds, grids etc.). This project focuses on an alternative approach of parametrizing the representation in terms of a continuous function and approximating it with a neural network.
Total project length: 175/350 hours.
Please use this link to access the test for this project. The test is due by April 3rd, however please keep in mind that it takes about 1 week to craft a good proposal and proposals need to be submitted via GSoC portal by April 4
Please DO NOT contact mentors directly by email. Instead, please email firstname.lastname@example.org with Project Title and include your CV and test results. The mentors will then get in touch with you.