Learning Parametrization with Implicit Neural Representations


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.

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Please use this link to access the test for this project.



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