DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC

Abstract A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5, Pythia8, and Delphes3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network. The code can be checked out or forked from the publicly accessible online repository https://gitlab.cern.ch/disipio/DiJetGAN.

[1]  David Rousseau,et al.  Further developments of FORM , 2018, Journal of Physics: Conference Series.

[2]  S. Frixione,et al.  Matching NLO QCD computations and parton shower simulations , 2002, hep-ph/0204244.

[3]  G. Salam Towards jetography , 2009, 0906.1833.

[4]  Benjamin Nachman,et al.  Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. , 2017, Physical review letters.

[5]  Johannes Bellm,et al.  Herwig 7.0/Herwig++ 3.0 release note , 2015, 1512.01178.

[6]  M. Krohn,et al.  Search for new resonances decaying into boosted W, Z and H bosons at CMS , 2017, 1710.02217.

[7]  Scoap The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning , 2017 .

[8]  M. Cacciari,et al.  The anti-$k_t$ jet clustering algorithm , 2008, 0802.1189.

[9]  J. Favereau,et al.  DELPHES 3: a modular framework for fast simulation of a generic collider experiment , 2013, Journal of High Energy Physics.

[10]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[11]  Marco Rossini,et al.  Search for dijet resonances in proton–proton collisions at √s=13TeV and constraints on dark matter and other models , 2017 .

[12]  M. P. Casado,et al.  Search for flavour-changing neutral current top quark decays t → Hq in pp collisions at s=8$$ \sqrt{s}=8 $$ TeV with the ATLAS detector , 2015 .

[13]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[14]  E. Re,et al.  A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX , 2010, 1002.2581.

[15]  Martin Erdmann,et al.  Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network , 2018, Computing and Software for Big Science.

[16]  Michela Paganini,et al.  CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks , 2017, ArXiv.

[17]  J. Winter,et al.  Event generation with , 2009 .

[18]  Walter Lampl,et al.  A Roadmap for HEP Software and Computing R&D for the 2020s , 2019 .

[19]  A. S. Mete,et al.  Measurements of tt¯ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in pp collisions at s=13  TeV using the ATLAS detector , 2018, Physical Review D.

[20]  Luke de Oliveira,et al.  Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis , 2017, Computing and Software for Big Science.

[21]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[22]  Anna Zilnyk A brief introduction to… , 2011 .

[23]  Damian Podareanu,et al.  Event generation and statistical sampling for physics with deep generative models and a density information buffer , 2019, Nature Communications.

[24]  Tianqi Chen,et al.  Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.

[25]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Matteo Cacciari,et al.  FastJet: a code for fast k_t clustering, and more , 2006, hep-ph/0607071.

[28]  Robert M. Harris,et al.  SEARCHES FOR DIJET RESONANCES AT HADRON COLLIDERS , 2011, 1110.5302.

[29]  F. Maltoni,et al.  MadGraph 5: going beyond , 2011, 1106.0522.

[30]  F. Siegert,et al.  Event generation with SHERPA 1.1 , 2008, 0811.4622.

[31]  G. Aad,et al.  The ATLAS Experiment at the CERN Large Hadron Collide , 2008 .

[32]  Maurizio Pierini,et al.  LHC analysis-specific datasets with Generative Adversarial Networks , 2019, ArXiv.

[33]  Eckhard Elsen,et al.  A Roadmap for HEP Software and Computing R&D for the 2020s , 2019, Computing and Software for Big Science.

[34]  Peter Skands,et al.  A brief introduction to PYTHIA 8.1 , 2007, Comput. Phys. Commun..

[35]  Leif Lönnblad,et al.  Merging multi-leg NLO matrix elements with parton showers , 2012, 1211.7278.

[36]  Stefan Spanier,et al.  The CMS experiment at the CERN LHC, CMS Collaboration , 2008 .

[37]  Wojciech Fedorko,et al.  Jet Constituents for Deep Neural Network Based Top Quark Tagging , 2017, ArXiv.

[38]  F. Krauss,et al.  Uncertainties in MEPS@NLO calculations of h + jets , 2014, 1401.7971.

[39]  Timo Aila,et al.  A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Olof Mogren,et al.  C-RNN-GAN: Continuous recurrent neural networks with adversarial training , 2016, ArXiv.

[41]  Martin Erdmann,et al.  Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks , 2018, Computing and Software for Big Science.

[42]  R. Frederix,et al.  Merging meets matching in MC@NLO , 2012, 1209.6215.

[43]  K. Strimmer,et al.  Optimal Whitening and Decorrelation , 2015, 1512.00809.

[44]  P. Mendez Lorenzo,et al.  Three dimensional Generative Adversarial Networks for fast simulation , 2018, Journal of Physics: Conference Series.

[45]  Luke de Oliveira,et al.  Jet-images — deep learning edition , 2015, Journal of High Energy Physics.

[46]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[47]  Rob Verheyen,et al.  Event Generation and Statistical Sampling with Deep Generative Models , 2019 .