Another Unorthodox Introduction to QCD and now Machine Learning

These are lecture notes presented at the online 2020 Hadron Collider Physics Summer School hosted by Fermilab. These are an extension of lectures presented at the 2017 and 2018 CTEQ summer schools in arXiv:1709.06195 and still introduces perturbative QCD and its application to jet substructure from a bottom-up perspective based on the approximation of QCD as a weakly-coupled, conformal field theory. With machine learning becoming an increasingly important tool of particle physics, I discuss its utility exclusively from the biased view for increasing human knowledge. A simple argument that the likelihood for quark versus gluon discrimination is infrared and collinear safe is presented as an example of this approach. End-of-lecture exercises are also provided.

[1]  E. Metodiev,et al.  A theory of quark vs. gluon discrimination , 2019, Journal of High Energy Physics.

[2]  A. Larkoski.  Elementary Particle Physics , 2019 .

[3]  Steffen Schumann,et al.  Event generation with Sherpa 2.2 , 2019, SciPost Physics.

[4]  Naftali Tishby,et al.  Machine learning and the physical sciences , 2019, Reviews of Modern Physics.

[5]  Kazuhiro Terao,et al.  Machine learning at the energy and intensity frontiers of particle physics , 2018, Nature.

[6]  Eli Upfal,et al.  Machine Learning in High Energy Physics Community White Paper , 2018, Journal of Physics: Conference Series.

[7]  D. Whiteson,et al.  Deep Learning and Its Application to LHC Physics , 2018, Annual Review of Nuclear and Particle Science.

[8]  A. Larkoski.  An Unorthodox Introduction to QCD , 2017, 1709.06195.

[9]  B. Nachman,et al.  Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning , 2017, Physics Reports.

[10]  A. Larkoski,et al.  How much information is in a jet? , 2017, Journal of High Energy Physics.

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

[12]  Peter Skands,et al.  An introduction to PYTHIA 8.2 , 2014, Comput. Phys. Commun..

[13]  D. Neill,et al.  Jet shapes with the broadening axis , 2014, 1401.2158.

[14]  J. Thaler,et al.  Unsafe but calculable: ratios of angularities in perturbative QCD , 2013, 1307.1699.

[15]  J. Thaler,et al.  Maximizing boosted top identification by minimizing N-subjettiness , 2011, Journal of High Energy Physics.

[16]  J. Thaler,et al.  Identifying boosted objects with N-subjettiness , 2010, 1011.2268.

[17]  S. D. Ellis,et al.  Jet shapes and jet algorithms in SCET , 2010, 1001.0014.

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

[19]  Joseph Virzi,et al.  Substructure of high-pTjets at the LHC , 2008, Physical Review D.

[20]  M. Gigg,et al.  Herwig++ physics and manual , 2008, 0803.0883.

[21]  S. Mrenna,et al.  PYTHIA 6.4 Physics and Manual , 2006, hep-ph/0603175.

[22]  V. Lemaître,et al.  Studies of QCD at e(+)e(-) centre-of-mass energies between 91 and 209 GeV , 2004 .

[23]  G. Sterman,et al.  Event shape / energy flow correlations , 2003, hep-ph/0303051.

[24]  A. Pinkus,et al.  Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function , 1993 .

[25]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[26]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[27]  L. Lönnblad,et al.  Coherence effects in deep inelastic scattering , 1989 .

[28]  T. D. Lee,et al.  Degenerate Systems and Mass Singularities , 1964 .

[29]  T. Kinoshita.  Mass singularities of Feynman amplitudes , 1962 .

[30]  V. Sudakov.  Vertex parts at very high-energies in quantum electrodynamics , 1954 .

[31]  F. Bloch,et al.  Note on the Radiation Field of the electron , 1937 .

[32]  M. Keller.  Qcd And Collider Physics , 2016 .

[33]  J. Virzi,et al.  J ul 2 00 8 YITP-SB-0831 Substructure of high-p T Jets at the LHC , 2008 .

[34]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .