Speaker
Description
An essential part of new physics searches at the Large Hadron Collider
at CERN involves event classification, or distinguishing signal decays
from potentially many background sources. Traditional techniques have
relied on reconstructing particle candidates and their physical
attributes from raw sensor data. However, such reconstructed data are
the result of a potentially lossy process of forcing raw data into
progressively more physically intuitive kinematic quantities. However,
powerful image-based machine learning algorithms have emerged that are
able to directly digest raw 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 identification using data from the CMS electromagnetic
calorimeter. We show that for single particle shower images, we are able
to exploit higher-order features in the shower to improve discrimination
versus traditional shower shape variables. Furthermore, for full event
classification, we show that these techniques are able to exploit
correlations between different showers in the event to achieve strong
discrimination.
