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Jul 24 – 26, 2023
Princeton University
America/New_York timezone

Cuts and NN - living together in peace

Jul 25, 2023, 9:30 AM
Princeton University

Princeton University

Talk Particle Physics Applications in Particle Physics


Gordon Watts (University of Washington (US))


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 programing 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 tradeoffs would help the analyzer 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.

Primary author

Gordon Watts (University of Washington (US))

Presentation materials