15 Statistical Issues in Particle Physics
689
“Statistical issues towards PHYSTATν 2019”, NuPhys2018: Prospects in Neutrino Physics,
https://indico.ph.qmul.ac.uk/indico/conferenceDisplay.py?confId=289
21. CERN European Schools, https://physicschool.web.cern.ch/physicschool/ESHEP/previous_
eshep.html;
CERN Latin-American Schools, https://physicschool.web.cern.ch/physicschool/CLASHEP/
previous_clashep.html; and
CERN Asia-Pacific Schools, http://aepshep.org/previous-schools.html.
22. L. Lyons, “Statistical Issues in Particle Physics” in “Elementary Particles: Detectors for
Particles and Radiation, Part 1: Principles and Methods” Eds C. Fabjan and H. Schopper,
(Landolt-Bornstein, 21B1 2011).
23. ATLAS Collaboration, “Observation of a new particle in the search for the Standard Model
Higgs boson with the ATLAS detector at the LHC”, Phys. Lett. B716 (2012) 1;
CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS
experiment at the LHC”, Phys. Lett. B716 (2012) 30.
24. N. Reid, “Some aspects of design of experiments”, ref. [7], p. 94.
25. R. Neal, “Computing likelihood functions when distributions are defined by simulations with
nuisance parameters”, ref. [7], p. 101; and in ref. [6].
26. J. Linnemann, “A pitfall in estimating systematic errors”, ref. [7], p. 94.
27. J. Heinrich and L. Lyons, Annual Reviews of Nuclear and Particle Science 57 (2007) 145.
28. L. Lyons, “Bayes and Frequentism: a Particle Physicist’s perspective”, (2013) https://arxiv.
org/pdf/1301.1273.pdf
29. R. D. Cousins, Am. J. Phys. 63 (1995) 398.
30. R. Barlow, “Asymmetric errors”, ref. [4], p. 250; and ref. [5], p. 56.
31. F. Garwood, “Fiducial limits for Poisson the distribution”, Biometrica 28 (1936) 437.
32. J. Heinrich, “Coverage of error bars for Poisson data” (2003),
http://www-cdf.fnal.gov/publications/cdf6438_coverage.pdf.
33. J. Heinrich, “Pitfalls of Goodness-of-Fit from Likelihood”, ref. [4], p. 52.
34. S. Baker and R. D. Cousins, “Clarification of the use of χ 2 and likelihood functions in fits to
histograms”, NIM 221 issue 2 (1984) 437.
35. G. Cowan, Eur Phys J C (2019) 79:133.
36. D. Cox, private communication
37. L. Lyons and E. Chapon, “Combining parameter values or p-values” (2017) https://arxiv.org/
pdf/1704.05540.pdf
38. P. Dauncey et al, “Handling uncertainties in background shapes: the discrete profiling
method”, JINST 10 no.04 (2015) 04015
39. G. Punzi, “Comments on likelihood fits with variable resolution”, ref. [4], p. 235.
40. P. Catastini and G. Punzi, “Bias-free estimation of multicomponent maximum likelihood fits
with component-dependent templates”, ref. [5], p. 60.
41. H. B. Prosper, “Multivariate methods: a unified perspective”, ref. [3], p. 91.
42. J. H. Friedman, “Recent advances in predictive (machine) learning”, ref. [4], p. 196; and
“Separating signal from background using ensembles of rules”, ref. [5], p. 127.
43. I. Narsky, “StatPatternRecognition in analysis of HEP and Astrophysics data”, ref. [7], p. 188.
44. A. Hocker et al, “TMVA, Toolkit for Multi-Variate data Analysis with ROOT”, ref. [7], p. 184.
45. D. Guest, K. Cranmer and D. Whiteson, “Deep Learning and Its Application to LHC Physics”,
Annual Review of Nuclear and Particle Science 68 (2018) 161;
A. Radovic et al, “Machine learning at the energy and intensity frontiers of particle physics”,
Nature 560 (2018) 41;
A. J. Larkoski, I. Moult and B. Nachman “Deep Learning and Its Application to LHC Physics”
(2017) http://arxiv.org/abs/arXiv:1709.04464;
I. Goodfellow, Y. Bengio and A. Courville, “Deep Learning” (20116), MIT Press.
46. CERN’s “Inter-Experimental LHC Machine Learning Working Group (IML)”, https://iml.
web.cern.ch/
47. Fermilab’s “ML at the Intensity and Cosmic Frontiers”, https://machinelearning.fnal.gov/
48. L. Lyons, Nucl. Inst. Meth. A324 (1993) 565.
689
“Statistical issues towards PHYSTATν 2019”, NuPhys2018: Prospects in Neutrino Physics,
https://indico.ph.qmul.ac.uk/indico/conferenceDisplay.py?confId=289
21. CERN European Schools, https://physicschool.web.cern.ch/physicschool/ESHEP/previous_
eshep.html;
CERN Latin-American Schools, https://physicschool.web.cern.ch/physicschool/CLASHEP/
previous_clashep.html; and
CERN Asia-Pacific Schools, http://aepshep.org/previous-schools.html.
22. L. Lyons, “Statistical Issues in Particle Physics” in “Elementary Particles: Detectors for
Particles and Radiation, Part 1: Principles and Methods” Eds C. Fabjan and H. Schopper,
(Landolt-Bornstein, 21B1 2011).
23. ATLAS Collaboration, “Observation of a new particle in the search for the Standard Model
Higgs boson with the ATLAS detector at the LHC”, Phys. Lett. B716 (2012) 1;
CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS
experiment at the LHC”, Phys. Lett. B716 (2012) 30.
24. N. Reid, “Some aspects of design of experiments”, ref. [7], p. 94.
25. R. Neal, “Computing likelihood functions when distributions are defined by simulations with
nuisance parameters”, ref. [7], p. 101; and in ref. [6].
26. J. Linnemann, “A pitfall in estimating systematic errors”, ref. [7], p. 94.
27. J. Heinrich and L. Lyons, Annual Reviews of Nuclear and Particle Science 57 (2007) 145.
28. L. Lyons, “Bayes and Frequentism: a Particle Physicist’s perspective”, (2013) https://arxiv.
org/pdf/1301.1273.pdf
29. R. D. Cousins, Am. J. Phys. 63 (1995) 398.
30. R. Barlow, “Asymmetric errors”, ref. [4], p. 250; and ref. [5], p. 56.
31. F. Garwood, “Fiducial limits for Poisson the distribution”, Biometrica 28 (1936) 437.
32. J. Heinrich, “Coverage of error bars for Poisson data” (2003),
http://www-cdf.fnal.gov/publications/cdf6438_coverage.pdf.
33. J. Heinrich, “Pitfalls of Goodness-of-Fit from Likelihood”, ref. [4], p. 52.
34. S. Baker and R. D. Cousins, “Clarification of the use of χ 2 and likelihood functions in fits to
histograms”, NIM 221 issue 2 (1984) 437.
35. G. Cowan, Eur Phys J C (2019) 79:133.
36. D. Cox, private communication
37. L. Lyons and E. Chapon, “Combining parameter values or p-values” (2017) https://arxiv.org/
pdf/1704.05540.pdf
38. P. Dauncey et al, “Handling uncertainties in background shapes: the discrete profiling
method”, JINST 10 no.04 (2015) 04015
39. G. Punzi, “Comments on likelihood fits with variable resolution”, ref. [4], p. 235.
40. P. Catastini and G. Punzi, “Bias-free estimation of multicomponent maximum likelihood fits
with component-dependent templates”, ref. [5], p. 60.
41. H. B. Prosper, “Multivariate methods: a unified perspective”, ref. [3], p. 91.
42. J. H. Friedman, “Recent advances in predictive (machine) learning”, ref. [4], p. 196; and
“Separating signal from background using ensembles of rules”, ref. [5], p. 127.
43. I. Narsky, “StatPatternRecognition in analysis of HEP and Astrophysics data”, ref. [7], p. 188.
44. A. Hocker et al, “TMVA, Toolkit for Multi-Variate data Analysis with ROOT”, ref. [7], p. 184.
45. D. Guest, K. Cranmer and D. Whiteson, “Deep Learning and Its Application to LHC Physics”,
Annual Review of Nuclear and Particle Science 68 (2018) 161;
A. Radovic et al, “Machine learning at the energy and intensity frontiers of particle physics”,
Nature 560 (2018) 41;
A. J. Larkoski, I. Moult and B. Nachman “Deep Learning and Its Application to LHC Physics”
(2017) http://arxiv.org/abs/arXiv:1709.04464;
I. Goodfellow, Y. Bengio and A. Courville, “Deep Learning” (20116), MIT Press.
46. CERN’s “Inter-Experimental LHC Machine Learning Working Group (IML)”, https://iml.
web.cern.ch/
47. Fermilab’s “ML at the Intensity and Cosmic Frontiers”, https://machinelearning.fnal.gov/
48. L. Lyons, Nucl. Inst. Meth. A324 (1993) 565.