Speaker
Matthew Drnevich
(NYU)
Description
In this work, we consider dijet production and present a model for the distribution of three-momenta of particles constituting the two boosted jets. Our method involves starting with a simple probability distribution for the momenta in the rest frame of the jets’ parent. Then, we use the Lorentz transformation to map the rest frame momentum distribution into a model of the boosted momenta parameterized by the velocity of the jets’ parent. Maximum likelihood estimation can then quickly estimate this velocity for a dataset. Future work could use this model for efficiently generating jet data, tagging jets, or inferring other physical parameters.
Primary authors
Kyle Stuart Cranmer
(New York University (US))
Matthew Drnevich
(NYU)
