Speaker
Alex Roman
(University of Florida)
Description
Relevant information from collision events from the Large Hadron Collider (LHC) and other colliders can be represented as spatial data in the appropriate phase space. Features such as sharp discontinuities in the event number density may signal the presence of new physics. Extraction of features from the data relies upon estimation of the functional value of the underlying distribution. We attempt to use properties of the Voronoi tessellation of the data along with Machine Learning techniques to improve upon traditional methods of density estimation.
Primary authors
Alex Roman
(University of Florida)
Konstantin Matchev
(University of Florida (US))
