Jet Physics Notes

Knowledge base for Jet Physics at the LHC

• Basics
• Jet Algorithms
• Applications
• Recent Progress
• Tools
• Conferences
• ⭐ Brief Notes

Basics

• A Parametrization of the Properties of Quark Jets, R.D. Field, R.P. Feynman (Caltech) Nov 1977. Nucl.Phys. B136 (1978) 1. [inspire]
• Towards Jetography, Gavin P. Salam (Paris, LPTHE). arXiv:0906.1833. [inspire]
• Towards an Understanding of the Correlations in Jet Substructure D. Adams (Brookhaven) , A. Arce (Duke U.) , L. Asquith (U. Sussex (main)) , M. Backovic (Louvain U.) , T. Barillari (Munich, Max Planck Inst.) , P. Berta (Charles U.) , D. Bertolini (UC, Berkeley) , A. Buckley (Glasgow U.) , J. Butterworth (University Coll. London) , R.C. Camacho Toro (Geneva U.) et al. arXiv:1504.00679 [inspire]
• Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012 A. Altheimer (Nevis Labs, Columbia U.) , A. Arce (Duke U.) , L. Asquith (Argonne) , J. Backus Mayes (SLAC) , E. Bergeaas Kuutmann (DESY) , J. Berger (Cornell U., Phys. Dept.) , D. Bjergaard (Duke U.) , L. Bryngemark (Lund U.) , A. Buckley (Edinburgh U.) , J. Butterworth (University Coll. London) et al. arXiv:1311.2708. [inspire]
• Investigating the Quantum Properties of Jets and the Search for a Supersymmetric Top Quark Partner with the ATLAS Detector, Benjamin Nachman (Stanford U., Phys. Dept.). CERN-THESIS-2016-083, arXiv:1609.03242. [inspire]
• Precision physics at the large hadron collider, Frédéric A. Dreyer (Paris, LPTHE), [pdf] (more focused on precision calculation for jets)
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning, Andrew J. Larkoski, Ian Moult, Benjamin Nachman. e-Print: arXiv:1709.04464. [inspire]
• Jet Substructure at the Large Hadron Collider: Experimental Review, Roman Kogler(Hamburg U.), Benjamin Nachman(LBNL, Berkeley), Alexander Schmidt(RWTH Aachen U.), Lily Asquith(U. Sussex (main)), Mario Campanelli(U. Coll. London) et al. (Mar 19, 2018). arXiv: 1803.06991. [inspire]

Jet Algorithms

• Fuzzy Jets, Lester Mackey (Stanford U., Statistics Dept.) , Benjamin Nachman (Stanford U., Phys. Dept. & SLAC) , Ariel Schwartzman (SLAC) , Conrad Stansbury (Stanford U., Phys. Dept.) arXiv:1509.02216 [inspire]
• XCone: N-jettiness as an Exclusive Cone Jet Algorithm, Iain W. Stewart (MIT, Cambridge, CTP), Frank J. Tackmann (DESY), Jesse Thaler (MIT, Cambridge, CTP), Christopher K. Vermilion (LBNL, Berkeley), Thomas F. Wilkason (MIT, Cambridge, CTP). arXiv:1508.01516. [inspire]
• Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure, Stephen D. Ellis, Andrew Hornig, Tuhin S. Roy (Washington U., Seattle) , David Krohn, Matthew D. Schwartz (Harvard U.). arXiv:1201.1914. [inspire]
• Deterministic annealing as a jet clustering algorithm in hadronic collisions, L. Angelini, G. Nardulli (Bari U. & TIRES, Bari & INFN, Bari), L. Nitti (TIRES, Bari & INFN, Bari), M. Pellicoro (Bari U. & TIRES, Bari & INFN, Bari), D. Perrino (Bari U. & INFN, Bari), S. Stramaglia (Bari U. & TIRES, Bari & INFN, Bari). arXiv:hep-ph/0407214. [inspire]
• FFTJet: A Package for Multiresolution Particle Jet Reconstruction in the Fourier Domain, I. Volobouev. arXiv:0907.0270. [inspire]

Jet Observables

• Energy flow polynomials: A complete linear basis for jet substructure, Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler (MIT, Cambridge, CTP). arXiv:1712.07124. [inspire]
• Generalized Fragmentation Functions for Fractal Jet Observables, Benjamin T. Elder (MIT, Cambridge, CTP), Massimiliano Procura (CERN), Jesse Thaler (MIT, Cambridge, CTP), Wouter J. Waalewijn (Amsterdam U. & NIKHEF, Amsterdam), Kevin Zhou (MIT, Cambridge, CTP). arXiv:1704.05456. [inspire]
• Jet shapes for boosted jet two-prong decays from first-principles, Mrinal Dasgupta (Manchester U.), Lais Schunk (IPhT, Saclay), Gregory Soyez (IPhT, Saclay & Liege U.). arXiv:1512.00516. [inspire]
• Jet Observables Without Jet Algorithms, Daniele Bertolini, Tucker Chan, Jesse Thaler (MIT, Cambridge, CTP). arXiv:1310.7584. [inspire]

Applications

Jet Substructure

• Systematics of quark/gluon tagging, Philippe Gras (IRFU, Saclay) , Stefan Hoeche (SLAC) , Deepak Kar (Witwatersrand U.) , Andrew Larkoski (Reed Coll.) , Leif Lönnblad (Lund U. & Lund U., Dept. Theor. Phys.) , Simon Plätzer (Durham U. & Durham U., IPPP & Manchester U.) , Andrzej Siódmok (CERN & Cracow, INP) , Peter Skands (Monash U.) , Gregory Soyez (IPhT, Saclay) , Jesse Thaler (MIT, Cambridge, CTP). arXiv:1704.03878. [inspire]
• Quark and Gluon Jet Substructure, Jason Gallicchio (UC, Davis) , Matthew D. Schwartz (Harvard U., Phys. Dept.). arXiv:1211.7038 [inspire]
• [BDRS] Jet substructure as a new Higgs search channel at the LHC, Jonathan M. Butterworth, Adam R. Davison (University Coll. London), Mathieu Rubin, Gavin P. Salam (Paris, LPTHE). arXiv:0802.2470. [inspire]

Jet Superstructure

• Seeing in Color: Jet Superstructure, Jason Gallicchio, Matthew D. Schwartz (Harvard U.). Phys.Rev.Lett. 105 (2010) 022001. [inspire]

Jet Charge

• Jet Charge at the LHC, David Krohn, Matthew D. Schwartz (Harvard U., Phys. Dept.), Tongyan Lin (Chicago U., KICP & Chicago U., EFI), Wouter J. Waalewijn (UC, San Diego). Sep 2012. 5 pp. Phys.Rev.Lett. 110 (2013) no.21, 212001. [inspire]

Jet Flavor

• Infrared safe definition of jet flavor, Andrea Banfi (Cambridge U., DAMTP & Cambridge U. & Milan Bicocca U. & INFN, Milan) , Gavin P. Salam (Paris, LPTHE) , Giulia Zanderighi (Fermilab & CERN), e-Print: hep-ph/0601139. [inspire]

Recent Progress

• Recursive Soft Drop, Frédéric A. Dreyer (MIT, Cambridge, CTP), Lina Necib (Caltech), Gregory Soyez (IPhT, Saclay), Jesse Thaler (MIT, Cambridge, CTP). arXiv:1804.03657. [inspire]
• Generalized Fragmentation Functions for Fractal Jet Observables, Benjamin T. Elder (MIT, Cambridge, CTP) , Massimiliano Procura (CERN) , Jesse Thaler (MIT, Cambridge, CTP) , Wouter J. Waalewijn (Amsterdam U. & NIKHEF, Amsterdam) , Kevin Zhou (MIT, Cambridge, CTP), arXiv:1704.05456. [inspire]
• Fractal based observables to probe jet substructure of quarks and gluons, Joe Davighi (Cambridge U., DAMTP) , Philip Harris (CERN), arXiv:1703.00914.[inspire]
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables, Ian Moult (LBNL, Berkeley & UC, Berkeley), Benjamin Nachman (LBL, Berkeley), Duff Neill (Los Alamos Natl. Lab., Theor. Div.). arXiv:1710.06859. [inspire]

High Precision Jet Calculation

• Factorization for groomed jet substructure beyond the next-to-leading logarithm, Christopher Frye, Andrew J. Larkoski, Matthew D. Schwartz, Kai Yan (Harvard U., Phys. Dept.) JHEP 1607 (2016) 064. [inspire]
• Analytic Boosted Boson Discrimination, Andrew J. Larkoski, Ian Moult, Duff Neill (MIT, Cambridge, CTP) JHEP 1605 (2016) 117 [inspire]

Neural Networks for Jet Physics

• Deep Learning as a Parton Shower, James William Monk. arXiv:1807.03685. [inspire]

• CWoLa Hunting: Extending the Bump Hunt with Machine Learning, Jack H. Collins (Maryland U. & Johns Hopkins U.), Kiel Howe (Fermilab), Benjamin Nachman (UC, Berkeley & LBL, Berkeley). arXiv:1807.03685. [inspire]

• Novel Jet Observables from Machine Learning, Kaustuv Datta, Andrew J. Larkoski. Oct 3, 2017 - 13 pages, e-Print: arXiv:1710.01305 [hep-ph] [inspire]

• How Much Information is in a Jet?, Kaustuv Datta, Andrew Larkoski, arXiv:1704.08249. [inspire]

Unsupervised Learning

• JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics, Anders Andreassen (Harvard U.), Ilya Feige (Control Data, London), Christopher Frye, Matthew D. Schwartz (Harvard U.). arXiv:1804.09720. [inspire]

Supervised Learning

• Neural Message Passing for Jet Physics,I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe and G. Rochette. NIPS2017. [pdf]
• Boosting H→bb¯ with Machine Learning, Joshua Lin, Marat Freytsis, Ian Moult, Benjamin Nachman. [inspire]
• Deep-learned Top Tagging with a Lorentz Layer, Anja Butter (U. Heidelberg, ITP), Gregor Kasieczka (Zurich, ETH), Tilman Plehn (U. Heidelberg, ITP), Michael Russell (Glasgow U.). arXiv:1707.08966. [inspire]
• QCD-Aware Recursive Neural Networks for Jet Physics, Gilles Louppe, Kyunghyun Cho, Cyril Becot, Kyle Cranmer. [inspire]
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks, Daniel Guest, Julian Collado (UC, Irvine) , Pierre Baldi (UC, Irvine & UC, Irvine) , Shih-Chieh Hsu (Washington U., Seattle & Washington U., Seattle) , Gregor Urban (UC, Irvine) , Daniel Whiteson (UC, Irvine & UC, Irvine). arXiv:1607.08633. [inspire]
• Deep learning in color: towards automated quark/gluon jet discrimination, Patrick T. Komiske, Eric M. Metodiev (MIT, Cambridge, CTP) , Matthew D. Schwartz (Harvard U., Phys. Dept. & Harvard U.) [inspire]
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks, James Barnard, Edmund Noel Dawe, Matthew J. Dolan, Nina Rajcic (ARC, CoEPP, Melbourne). arXiv:1609.00607. [inspire]
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks, Pierre Baldi, Kevin Bauer (UC, Irvine) , Clara Eng (UC, Berkeley, Chem. Eng. Dept.) , Peter Sadowski, Daniel Whiteson (UC, Irvine),arXiv:1603.09349. [inspire]
• Playing Tag with ANN: Boosted Top Identification with Pattern Recognition, Leandro G. Almeida (Ecole Normale Superieure, CNRS) , Mihailo Backović (Louvain U., CP3) , Mathieu Cliche (Cornell U., LEPP) , Seung J. Lee (KAIST, Taejon & Korea Inst. Advanced Study, Seoul) , Maxim Perelstein (Cornell U., LEPP) [inspire]
• Jet-images — deep learning edition, Luke de Oliveira (Stanford U., Math. Dept.) , Michael Kagan (SLAC) , Lester Mackey (Stanford U., Statistics Dept.) , Benjamin Nachman, Ariel Schwartzman (SLAC) [inspire]
• Searching for Exotic Particles in High-Energy Physics with Deep Learning, Pierre Baldi, Peter Sadowski (UC, Irvine), Daniel Whiteson (UC, Irvine & Pennsylvania U. & UC, Irvine). arXiv:1402.4735 [inspire]
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, Josh Cogan, Michael Kagan, Emanuel Strauss, Ariel Schwarztman (SLAC) [inspire]

Generative Models and Others

• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks, Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson (UC, Irvine) , Edward Goul (MIT, Cambridge, Dept. Phys.) , Andreas Søgaard (Edinburgh U.), e-Print: arXiv:1703.03507. [inspire]
• Weakly Supervised Classification in High Energy Physics, Lucio Mwinmaarong Dery (Stanford U., Phys. Dept.) , Benjamin Nachman (LBL, Berkeley) , Francesco Rubbo, Ariel Schwartzman (SLAC) arXiv:1702.00414. [inspire]
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis, Luke de Oliveira (LBNL, Berkeley) , Michela Paganini (LBNL, Berkeley & Yale U.) , Benjamin Nachman (LBNL, Berkeley) , arXiv:1701.05927 [inspire] > using GAN to do MC

Traditional but useful Examples

• Enhanced Higgs Boson to τ+τ− Search with Deep Learning, Pierre Baldi, Peter Sadowski, Daniel Whiteson (UC, Irvine). arXiv:1410.3469. [inspire]

Event Level

• Double-charming Higgs identification using machine-learning assisted jet shapes, Alexander Lenz, Michael Spannowsky, Gilberto Tetlalmatzi-Xolocotzi, e-Print: arXiv:1708.03517 [hep-ph]. [inspire]

Flavor Tagging

• Disentangling Heavy Flavor at Colliders, Philip Ilten (MIT, LNS) , Nicholas L. Rodd, Jesse Thaler (MIT, Cambridge, CTP) , Mike Williams (MIT, LNS), arXiv:1702.02947 [inspire]

Interesting Asepcts

• Jet clustering dependence of VBF Higgs production, Michael Rauch, Dieter Zeppenfeld (KIT, Karlsruhe, TP). arXiv:1703.05676. [inspire]

Experimental Database

• HEPData: https://hepdata.net/
• CERN OpenData: http://opendata.cern.ch/
• OpenML: https://www.openml.org

Tools

• FastJet
• Scikit-HEP

Conferences

• BOOST2019
• Machine Learning for Jet Physics
• Deep Learning for Physical Sciences (NIPS2017)
• Boost 2018
• Boost 2017 [Notes]
• Boost 2016
• Boost 2015