Jul 9 – 13, 2018
Sofia, Bulgaria
Europe/Sofia timezone

End-to-end Deep Learning Applications for Event Classification at CMS

Jul 11, 2018, 11:30 AM
Hall 9 (National Palace of Culture)

Hall 9

National Palace of Culture

presentation Track 6 – Machine learning and physics analysis T6 - Machine learning and physics analysis


Michael Andrews (Carnegie-Mellon University (US))


An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing signal events from the background. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to raw detector-level data from the CMS experiment to directly learn useful features, in what we call, “end-to-end event classification”. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.

Primary authors

Michael Andrews (Carnegie-Mellon University (US)) Manfred Paulini (Carnegie-Mellon University (US)) Sergei Gleyzer (University of Florida (US))

Presentation materials