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

Abstract

Artificial intelligence offers the potential to automate challenging dataprocessing 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. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

Deep-learning top taggers or the end of QCD?

“…We have constructed a ConvNet setup, inspired by standard image recognition techniques [23,27]. To optimize the network architecture, train the network, and test the performance we have used independent event samples.…”

“…In jet physics the basic idea is to view the azimuthal angle vs rapidity plane with calorimeter entries as a sparsely filled image, where the filled pixels correspond to the calorimeter cells with nonzero energy deposits and the pixel intensities to the deposited energy. After some image pre-processing, a training sample of signal and background images can be fed through a convolutional network, designed to learn the signal-like and background-like features of these jet images [23,27]. The final layer of the network converts the learned features of the image into a probability of it being either signal or background.…”

“…While an arbitrarily large non-convolutional DNN should be able to learn features in the image directly, the convolution layers lead to much faster convergence of the model. Image recognition in terms of ConvNets has only recently been tested for LHC applications [23,27]. The machine learning side of our comparison will be based on ConvNets.…”

“…For our early study we do not include track information in our jet image, some ideas in this direction are pointed out in ref. [27] All events are passed through a fast detector simulation with Delphes3 towers with FastJet3 [44,45] to R = 1.5 anti-k T [46] jets with |η| < 1.0. These anti-k T jets give us a smooth outer shape of the fat jet and a well-defined jet area for our jet image.…”

“…Even more generally, we can apply image recognition techniques to the two-dimensional azimuthal angle vs rapidity plane, for example searching for hadronic decays of weak bosons [22][23][24][25] or top quarks [26]. The same techniques can be applied to separate quark-like and gluon-like jets [27].…”