Jet energy calibration with deep learning as a Kubeflow pipeline

Abstract

Precise measurements of the energy of jets emerging from particle collisions at the LHC are essential for a vast majority of physics searches at the CMS experiment. In this study, we leverage well-established deep learning models for point clouds and CMS open data to improve the energy calibration of particle jets. To enable production-ready machine learning based jet energy calibration an end-to-end pipeline is built on the Kubeflow cloud platform. The pipeline allowed us to scale up our hyperparameter tuning experiments on cloud resources, and serve optimal models as REST endpoints. We present the results of the parameter tuning process and analyze the performance of the served models in terms of inference time and overhead, providing insights for future work in this direction. The study also demonstrates improvements in both flavor dependence and resolution of the energy response when compared to the standard jet energy corrections baseline.

20 pages, 10 figures. Code repository available via https://zenodo.org/record/7799179

References in corpus (5)

• An Introduction to PYTHIA 8.2
• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• Particle-flow reconstruction and global event description with the CMS detector
• Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks