Machine Learning at the CMS experiment with selected examples Invited presentation within the Particles Physics Seminar by Dr. Mehmet Özgür Sahin (Université Paris-Saclay) Machine learning (ML) algorithms are widely used in HEP experiments. Recent advancements in deep learning techniques contributed to the explosive growth of their applications. With the planned High Luminosity upgrade of the CERN Large Hadron Collider (LHC), the deep learning techniques will be even more prominent and are expected to be used in almost every step of readout and analysis of data collected by the Compact Muon Solenoid (CMS) detector. In this talk, examples of this wide variety of ML applications from data acquisition and reconstruction to end-to-end analyses will be presented. In addition, future applications being explored by the IRFU CMS group will be discussed. Kindly supported by the Bonn Global Cooperation Fund https://www.uni-bonn.de/de/veranstaltungen/machine-learning-at-the-cms-experiment-with-selected-examples https://www.uni-bonn.de/++resource++plone-logo.svg

Invited presentation within the Particles Physics Seminar by Dr. Mehmet Özgür Sahin (Université Paris-Saclay)

Machine learning (ML) algorithms are widely used in HEP experiments. Recent advancements in deep learning techniques contributed to the explosive growth of their applications. With the planned High Luminosity upgrade of the CERN Large Hadron Collider (LHC), the deep learning techniques will be even more prominent and are expected to be used in almost every step of readout and analysis of data collected by the Compact Muon Solenoid (CMS) detector. In this talk, examples of this wide variety of ML applications from data acquisition and reconstruction to end-to-end analyses will be presented. In addition, future applications being explored by the IRFU CMS group will be discussed.

Kindly supported by the Bonn Global Cooperation Fund