Paul Glaysher

Jose M. Clavijo, Paul Glaysher, Judith M. Katzy et al.

We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimising the difference in response of the network to background samples originating from different MC models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the LHC with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification and discuss implications and limitations of the method

Paul Glaysher, Judith M. Katzy, Sitong An

Training features used to analyse physical processes are often highly correlated and determining which ones are most important for the classification is a non-trivial tasks. For the use case of a search for a top-quark pair produced in association with a Higgs boson decaying to bottom-quarks at the LHC, we compare feature ranking methods for a classification BDT. Ranking methods, such as the BDT Selection Frequency commonly used in High Energy Physics and the Permutational Performance, are compared with the computationally expense Iterative Addition and Iterative Removal procedures, while the latter was found to be the most performant.

Kim Albertsson, Piero Altoe, Dustin Anderson et al.

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.