July 31, 2017 to August 4, 2017
Fermi National Accelerator Laboratory
US/Central timezone

Exploring end-to-end image-based deep learning for particle & event classification

Jul 31, 2017, 7:07 PM
Reception Area (Fermi National Accelerator Laboratory)

Reception Area

Fermi National Accelerator Laboratory

Poster Computing, Analysis Tools and Data Handling Poster Session and Reception


Michael Andrews (Carnegie Mellon University)


An essential part of new physics searches at the Large Hadron Collider at CERN involves event classification, or distinguishing signal decays from one of its many background look-alikes. Traditional techniques have relied on reconstructed particle candidates and their physical attributes. However, such reconstructed data are the result of a long, potentially lossy process of forcing the raw sensor data into progressively more physically intuitive quantities. Meanwhile, powerful image-based machine learning algorithms have emerged that are able to directly digest raw sensory data and output a prediction---so called end-to-end deep learning classifiers. We explore the use of such algorithms to perform physics classification using raw sensory data from the CMS detector. As proof of concept, we classify photon- versus electron-induced ECAL showers and compare the performance of using raw sensor data versus shower shape data.

Primary author

Michael Andrews (Carnegie Mellon University)


Manfred Paulini (Carnegie Mellon University) Sergei Gleyzer (University of Florida)

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