Jet-images: computer vision inspired techniques for jet tagging

Cogan, J. G.; Kagan, M.; Strauss, E.; Schwarztman, Ariel

doi:10.1007/jhep02(2015)118

Cited by 211 publications

(231 citation statements)

References 59 publications

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“…An example of such an approach are wavelets, describing patterns of hadronic weak boson decays [20,21]. 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].…”

Section: Introductionmentioning

confidence: 99%

Deep-learning top taggers or the end of QCD?

Kasieczka

Plehn²,

Russell

et al. 2017

J. High Energ. Phys.

234

285

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Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.

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Section: Introductionmentioning

confidence: 99%

Deep-learning top taggers or the end of QCD?

Kasieczka

Plehn²,

Russell

et al. 2017

J. High Energ. Phys.

234

285

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“…Recent work demonstrates encouraging results with shallow classification models trained on jet images [26][27][28]. Deep networks have shown additional promise in particle-level studies [29].…”

Section: Introductionmentioning

confidence: 99%

Jet substructure classification in high-energy physics with deep neural networks

et al. 2016

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At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a twodimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally-connected and fully-connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.

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“…We start by tackling a constrained version of the larger problem, where we use the concept of a jet image [19] to show that the idealized 2D radiation pattern from high energy quarks and gluons can be efficiently and effectively reproduced by a GAN. The compositionality property of events affords us the possibility of simulating each jet individually with a GAN.…”

Section: Introductionmentioning

confidence: 99%

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

Oliveira

Paganini

Nachman

2017

Comput Softw Big Sci

243

207

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We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many orders of magnitude and exhibit the desired low-dimensional physical properties (i.e., jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a novel empirical validation of image quality and validity of GAN-produced simulations of the natural world. This work provides a base for further explorations of GANs for use in faster simulation in High Energy Particle Physics.

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Jet-images: computer vision inspired techniques for jet tagging

Cited by 211 publications

References 59 publications

Deep-learning top taggers or the end of QCD?

Deep-learning top taggers or the end of QCD?

Jet substructure classification in high-energy physics with deep neural networks

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

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