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August 23, 2021 to October 7, 2021
Venue: OAC conference center, Kolymbari, Crete, Greece. Participation is possible also via internet.
Europe/Athens timezone

Machine Learning (CMS)

Aug 26, 2021, 6:40 PM
Room 1

Room 1


Savannah Jennifer Thais (Princeton University (US))


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
Internet talk No

Primary author

Savannah Jennifer Thais (Princeton University (US))

Presentation materials

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