Abstract
In this article, we report our work on the Kaggle Challenge: Flavours of Physics (2016). The main goal of this challenge is to develop powerful classifiers for the detection of “new physics” – specifically, violation the lepton flavour conservation guaranteed by the standard model – in the Large Hadron Collider (LHC). We build several models for the challenge, which include boosted decision trees and neural networks, and report our findings on their performance. We include a cautionary tale of how the use of some specific features of the data can dramatically impact the physical relevance of the results.
Type
Publication
Technical report, 2017
