Abstract
Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider physics data. We explore whether there is information available in calorimeter deposits that is not captured by the standard approach of isolation cones. We find that convolutional networks and particle-flow networks accessing the calorimeter cells surpass the performance of isolation cones, suggesting that the radial energy distribution and the angular structure of the calorimeter deposits surrounding the muon contain unused discrimination power. We assemble a small set of high-level observables which summarize the calorimeter information and close the performance gap with networks which analyze the calorimeter cells directly. These observables are theoretically well-defined and can be studied with collider data.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
ATLAS collaboration, Search for electroweak production of supersymmetric states in scenarios with compressed mass spectra at \( \sqrt{s} \) = 13 TeV with the ATLAS detector, Phys. Rev. D 97 (2018) 052010 [arXiv:1712.08119] [INSPIRE].
ATLAS and CMS collaborations, Search for supersymmetry with extremely compressed spectra with the ATLAS and CMS detectors, Nucl. Part. Phys. Proc. 273-275 (2016) 631 [INSPIRE].
CMS collaboration, Search for supersymmetry in the vector-boson fusion topology in proton-proton collisions at \( \sqrt{s} \) = 8 TeV, JHEP 11 (2015) 189 [arXiv:1508.07628] [INSPIRE].
I. Hoenig, G. Samach and D. Tucker-Smith, Searching for dilepton resonances below the Z mass at the LHC, Phys. Rev. D 90 (2014) 075016 [arXiv:1408.1075] [INSPIRE].
CMS collaboration, Particle-flow reconstruction and global event description with the CMS detector, 2017 JINST 12 P10003 [arXiv:1706.04965] [INSPIRE].
J. Pata, J. Duarte, J.-R. Vlimant, M. Pierini and M. Spiropulu, MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks, Eur. Phys. J. C 81 (2021) 381 [arXiv:2101.08578] [INSPIRE].
ATLAS collaboration, Muon reconstruction performance of the ATLAS detector in proton-proton collision data at \( \sqrt{s} \) = 13 TeV, Eur. Phys. J. C 76 (2016) 292 [arXiv:1603.05598] [INSPIRE].
LHCb collaboration, Search for Dark Photons Produced in 13 TeV pp Collisions, Phys. Rev. Lett. 120 (2018) 061801 [arXiv:1710.02867] [INSPIRE].
E. Hall and J. Thaler, Photon isolation and jet substructure, JHEP 09 (2018) 164 [arXiv:1805.11622] [INSPIRE].
ATLAS collaboration, Deep Learning for Pion Identification and Energy Calibration with the ATLAS Detector, Tech. Rep. ATL-PHYS-PUB-2020-018 (2020).
J. Collado, J.N. Howard, T. Faucett, T. Tong, P. Baldi and D. Whiteson, Learning to identify electrons, Phys. Rev. D 103 (2021) 116028 [arXiv:2011.01984] [INSPIRE].
C. Brust, P. Maksimovic, A. Sady, P. Saraswat, M.T. Walters and Y. Xin, Identifying boosted new physics with non-isolated leptons, JHEP 04 (2015) 079 [arXiv:1410.0362] [INSPIRE].
P. Baldi, P. Sadowski and D. Whiteson, Searching for Exotic Particles in High-Energy Physics with Deep Learning, Nature Commun. 5 (2014) 4308 [arXiv:1402.4735] [INSPIRE].
P. Baldi, Deep Learning in Science, Cambridge University Press, Cambridge, U.K. (2021).
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118 [arXiv:1407.5675] [INSPIRE].
P. Baldi, K. Bauer, C. Eng, P. Sadowski and D. Whiteson, Jet Substructure Classification in High-Energy Physics with Deep Neural Networks, Phys. Rev. D 93 (2016) 094034 [arXiv:1603.09349] [INSPIRE].
P.T. Komiske, E.M. Metodiev and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121 [arXiv:1810.05165] [INSPIRE].
S. Chang, T. Cohen and B. Ostdiek, What is the Machine Learning?, Phys. Rev. D 97 (2018) 056009 [arXiv:1709.10106] [INSPIRE].
T. Roxlo and M. Reece, Opening the black box of neural nets: case studies in stop/top discrimination, arXiv:1804.09278 [INSPIRE].
S. Wunsch, R. Friese, R. Wolf and G. Quast, Identifying the relevant dependencies of the neural network response on characteristics of the input space, Comput. Softw. Big Sci. 2 (2018) 5 [arXiv:1803.08782] [INSPIRE].
G. Agarwal et al., Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation, JHEP 05 (2021) 208 [arXiv:2011.13466] [INSPIRE].
T. Faucett, J. Thaler and D. Whiteson, Mapping Machine-Learned Physics into a Human-Readable Space, Phys. Rev. D 103 (2021) 036020 [arXiv:2010.11998] [INSPIRE].
J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
DELPHES 3 collaboration, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02 (2014) 057 [arXiv:1307.6346] [INSPIRE].
R. Brun and F. Rademakers, ROOT: An object oriented data analysis framework, Nucl. Instrum. Meth. A 389 (1997) 81 [INSPIRE].
P.T. Komiske, E.M. Metodiev and J. Thaler, Energy flow polynomials: A complete linear basis for jet substructure, JHEP 04 (2018) 013 [arXiv:1712.07124] [INSPIRE].
A.J. Larkoski, G.P. Salam and J. Thaler, Energy Correlation Functions for Jet Substructure, JHEP 06 (2013) 108 [arXiv:1305.0007] [INSPIRE].
L.M. Dery, B. Nachman, F. Rubbo and A. Schwartzman, Weakly Supervised Classification in High Energy Physics, JHEP 05 (2017) 145 [arXiv:1702.00414] [INSPIRE].
E.M. Metodiev, B. Nachman and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics, JHEP 10 (2017) 174 [arXiv:1708.02949] [INSPIRE].
M. Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv:1603.04467 [INSPIRE].
F. Chollet et al., Keras, https://keras.io (2015).
D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, arXiv:1412.6980 [INSPIRE].
A.M. Saxe, J.L. McClelland and S. Ganguli, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, arXiv:1312.6120.
L. Hertel, J. Collado, P. Sadowski, J. Ott and P. Baldi, Sherpa: Robust hyperparameter optimization for machine learning, SoftwareX 12 (2020) 100591 [arXiv:2005.04048].
X. Glorot, A. Bordes and Y. Bengio, Deep sparse rectifier neural networks, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, U.S.A., 11–13 April 2011, Proc. Mach. Learn. Res. 15 (2011) 315 [http://proceedings.mlr.press/v15/glorot11a.html].
N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929.
P. Baldi and P. Sadowski, The dropout learning algorithm, Artificial Intel. 210 (2014) 78.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ArXiv ePrint: 2102.02278
Rights and permissions
Open Access . This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Collado, J., Bauer, K., Witkowski, E. et al. Learning to isolate muons. J. High Energ. Phys. 2021, 200 (2021). https://doi.org/10.1007/JHEP10(2021)200
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/JHEP10(2021)200