Speaker
Description
We have developed a machine learning-based generative model to estimate the kernel density of the data using the Gaussian kernel and then have generated additional samples from this distribution. This model uses scikit-learn to generate a list of particle four-momenta from the proton-proton collisions produced at the Large Hadron Collider (LHC). We demonstrate the ability of this approach to reproduce a set of kinematic features, that are used for the search for new resonances decaying to Z(ll)γ final states at the LHC. This model is constructed to take the pre-processed Zγ events and generate sample data with accurate statistics, mimicking the original distributions and achieving better performances compared to the standard event Monte-Carlo generators.
Level for award;(Hons, MSc, PhD, N/A)?
PhD
| Apply to be considered for a student ; award (Yes / No)? | Yes |
|---|
