Machine Learning Applications in Atlas and CMS Experiments at LHC

Dear Colleagues,

A major aim of the physics program of the Atlas and CMS experiments is to search for signs of new physics in an immense number of collisions at CERN’s Large Hadron Collider, either by finding rare signals of new particles produced among overwhelming amounts of background-originated collisions or by looking for deviations from Standard Model predictions small enough to have escaped previous detection attempts. The data collected by the LHC experiments are high-dimensional and complex, and the complexity is growing with the increase of LHC performance. The increasingly challenging experimental conditions of LHC also demand continuous advancements in reconstruction techniques and in noise rejection strategies at all levels of data taking.

Thus, the physics reach of the experiments is strongly dependent on the physics performance of the algorithms and on the quality of data.

Neural networks have been used in HEP for a long time; however, the introduction of deep learning has led to a new generation of machine learning algorithms that, in many circumstances, outperform more conventional ones.

This Special Issue focuses on the latest research and development in machine learning application in Atlas and CMS experiments at LHC applied in the context of improving the final analysis selection, object reconstruction, object calibration, object identification, triggering, simulation, and automation.

Prof. Dr. Francesco Conventi
Dr. Orso Iorio
Guest Editors

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