Speaker
Michael Andrews
(Carnegie Mellon University)
Description
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)
Co-authors
Manfred Paulini
(Carnegie Mellon University)
Sergei Gleyzer
(University of Florida)