Differentiable Programming: Neural Networks and Selection Cuts Working Together

University of Washington, Department of Physics, BOX 351560, Seattle, Washington, 98122, USA

^* Corresponding author: gwatts@uw.edu

Published online: 6 May 2024

Abstract

Differentiable Programming could open even more doors in HEP analysis and computing to Artificial Intelligence/Machine Learning. Current common uses of AI/ML in HEP are deep learning networks – providing us with sophisticated ways of separating signal from background, classifying physics, etc. This is only one part of a full analysis – normally skims are made to reduce dataset sizes by applying selection cuts, further selection cuts are applied, perhaps new quantities calculated, and all of that is fed to a deep learning network. Only the deep learning network stage is optimized using the AI/ML gradient decent technique. Differentiable programming offers us a way to optimize the full chain, including selection cuts that occur during skimming. This contribution investigates applying selection cuts in front of a simple neural network using differentiable programming techniques to optimize the complete chain on toy data. There are several well-known problems that must be solved – e.g., selection cuts are not differentiable, and the interaction of a selection cut and a network during training is not well understood. This investigation was motived by trying to automate reduced dataset skims and sizes during analysis – HL-LHC analyses have potentially multi-TB dataset sizes and an automated way of reducing those dataset sizes and understanding the trade-offs would help the analyser make a judgement between time, resource usages, and physics accuracy. This contribution explores the various techniques to apply a selection cut that are compatible with differentiable programming and how to work around issues when it is bolted onto a neural network. Code is available.