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Deep learning in color: towards automated quark/gluon jet discrimination · Around

2016

Deep learning in color: towards automated quark/gluon jet discrimination

Komiske, Patrick T., Metodiev, Eric M., Schwartz, Matthew D.

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Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics.

To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists.
Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits.
We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts.

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