In recent years, Machine Learning (ML) methods have become ubiquitous in High Energy Physics (HEP) research. This talk will explore current areas of ML for HEP research including event classification, object reconstruction, jet tagging, and accelerated ML inference for trigger environments. I will also discuss current opportunities and challenges in the ML for physics space and ways researchers can use these tools to advance science. Although this talk is focused on successful implementations of novel ML methods in the CMS experiment, the techniques and topics covered are relevant to a wide range of HEP experiments.
Thais, Savannah Jennifer
|Is this abstract from experiment?||Yes|
|Name of experiment and experimental site||CMS|
|Is the speaker for that presentation defined?||Yes|