Speaker
Description
In the search for new physics Beyond the Standard Model, MVA techniques are used to extract specific signal from Standard Model background processes. In this study weakly-supervised machine learning techniques are developed and evaluated using the ATLAS experiment, di-lepton (e±μ∓) final state data, in the H → Sh search. These weakly-supervised techniques use labelled background data to extract an unlabelled signal. This allows the classification of signal information without restrictions based on previously defined physics. This study uses TMVA with ROOT to evaluate the effectiveness of weakly-supervised techniques when compared to fully-supervision techniques using Boosted Decision Tree (BDT), Multilayer Perceptron (MLP) and Deep Neural Network (DNN) methods.
