Discrimination of Signal-Background Events with Supervised and Weakly Supervised Learning in the Search for New Bosons Decaying to Z + y Final State
Researcher: Nkateko Baloyi, University of the Witwatersrand, Johannesburg
Supervisors: Prof. Bruce Mellado and Dr Xifeng Ruan, University of the Witwatersrand, Johannesburg
The theory of Standard Model (SM) has successfully driven experiments and predictions since it was developed in 1975 and it has never been contradicted by experimental results. In 2012, the SM led to the discovery of Higgs boson (h) at the Large Hadron Collider (LHC) which completed the particle spectrum of the SM. The discovery of h inspired experimental studies to further understand the h scalar properties, opening the search for Beyond the Standard Model physics (BSM). BSM physics searches for new particles that can help understand and answer phenomena that cannot be explained by the SM. The LHC collides protons at high luminosity and high energy trying to recreate particles that occurred moments after the big bang and the BSM particles. The data produced during the collision requires advanced techniques that can search for relevant information in the data for signal (S) events to be identified. The production of the S events comes with a huge amount of background (B) production to which the S events cannot be easily identified. advanced machine learning (ML) and statistical techniques can be used to isolate the S events from the B events. ML is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience. This research focuses on the application of boosted decision trees (BDT) and deep neural networks (DNN) on the Monte Carlo simulated data. Supervised learning and weakly-supervised learning (WSL) approaches are implemented to discriminate the S from the B events. The supervised learning is used as a benchmark to measure the performance of the WSL approach. Pre-selection cuts are applied on the data and four different models are applied to classify the S and B events for both BDT and DNN using the supervised learning and WSL approach. The WSL approach use two samples, one sample with only B events and the other sample mixed with S and B events to train the model. The DNN model is trained on the samples and applied to classify the S and B events on the same test data used in the supervised learning approach. The performance of the WSL models are compared to the supervised learning models performance. The results show a strong bias in the WSL approach.