Open Access
Issue
EPJ Web of Conferences
Volume 55, 2013
SOS 2012 – IN2P3 School of Statistics
Article Number 03002
Number of page(s) 32
Section Application to Data Analyses
DOI https://doi.org/10.1051/epjconf/20135503002
Published online 01 July 2013
  1. Y. Vardi, L. A. Shepp, L. Kaufman, “A Statistical Model for Positron Emission Tomography”, Journal of the American Statistical Association, Vol. 80, No. 389 (Mar., 1985), pp. 8–20 [http://www.jstor.org/stable/2288030] [CrossRef] [Google Scholar]
  2. O. Helene, V. R. Vanin, Z. O. Guimaraes-Filho, C. Takiya, “Variances, covariances and artifacts in image deconvolution”, Nucl. Instr. Meth. A, 580 (2007) pp.1466–1473 [CrossRef] [Google Scholar]
  3. L. Evans, P. Bryant (editors), “LHC Machine”, 2008 JINST 3, S08001, doi:10.1088/1748-0221/3/08/S08001 [http://iopscience.iop.org/1748-0221/3/08/S08001] [Google Scholar]
  4. the ATLAS Collaboration, “The ATLAS Experiment at the CERN Large Hadron Collider”, JINST 3, 2008, S08003, doi:10.1088/1748-0221/3/08/S08003 [http://iopscience.iop.org/1748-0221/3/08/S08003] [Google Scholar]
  5. V. Ahrens, A. Ferroglia, M. Neubert, B. D. Pecjak and L. L. Yang, “Renormalization-Group Improved Predictions for Top-Quark Pair Production at Hadron Colliders”, JHEP 1009, 097 (2010) [arXiv:1003.5827 [hep-ph]]. [CrossRef] [Google Scholar]
  6. the ATLAS collaboration, “Measurement of the charge asymmetry in top quark pair production in pp collisions at √s = 7 TeV using the ATLAS detector”, Eur. Phys. J. C 72, 2039 (2012) [arXiv:1203.4211 [hep-ex]]. [CrossRef] [EDP Sciences] [Google Scholar]
  7. Computer generated image of the whole ATLAS detector, CERN-GE-0803012, Photograph: Joao Pequenao, [http://cds.cern.ch/record/1095924/] [Google Scholar]
  8. Figures produced by the D0 collaboration [9] available at http://www-d0. fnal.gov/Run2Physics/top /top_public_web_pages/top_feynman_diagrams.html [Google Scholar]
  9. The D0 experiment, http://www-d0.fnal.gov [Google Scholar]
  10. P C Hansen, “Numerical tools for analysis and solution of Fredholm integral equations of the first kind”, Inverse Problems 8 (1992) 849 doi:10.1088/0266-5611/8/6/005 [http://m.iopscience.iop.org/0266-5611/8/6/005?rel=sem&relno=7] [NASA ADS] [CrossRef] [Google Scholar]
  11. Volker Blobel, “Unfolding methods in high energy physics experiments”, Report DESY 84–118, 1984 (also in Proceedings of the 1984 CERN School of Computing, CERN 85-09, pp. 88–127; see also http://www.desy.de/ blobel/). [Google Scholar]
  12. J. Beringer et al. (Particle Data Group), “The Review of Particle Physics”, Phys. Rev. D86, 010001 (2012) [http://pdg.lbl.gov/] [Google Scholar]
  13. G. Cowan, “A survey of unfolding methods for particle physics”, Conf. Proc. C 0203181, 248 (2002). [Google Scholar]
  14. Statistics” (Revised by G. Cowan) in [12] [Google Scholar]
  15. L. Lyons, “Unfolding: Introduction”, in Proceedings of the PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland, 17-20 January 2011, edited by H.B.Prosper, L.Lyons, CERN-2011-006, pp. 225–228 [http://cds.cern.ch/record/1306523] and references to unfolding therein. [Google Scholar]
  16. I. P. Nedelkov, “Improper problems in Computation Physics”, Com. Phys. Comm. 4 (1972) 157 [CrossRef] [Google Scholar]
  17. S. Leach, “Singular Value Decomposition. A Primer”, [http://people.csail.mit.edu/hasinoff/320/SingularValueDecomposition.pdf], material from CSC320S: Introduction to Visual Computing course at MIT and references therein. [Google Scholar]
  18. A. G. Frodesen, O. Skjeggestad, H. Tofte, “Probability and Statistics in particle physics”, Hardcover: 501 pages, Publisher: Universitetsforlaget (September 1979), ISBN-10:8200019063, ISBN-13: 978-8200019060 [Google Scholar]
  19. A. Hoecker, V. Kartvelishvili, “SVD Approach to data unfolding”, Nucl. Instr. Meth. A 372, 1996 (469) [Google Scholar]
  20. A. Björck, “Least squares methods”, Handbook of Numerical Analysis, voI I. (1990) 465–652, ed P. G. Ciarlet and J. L. Lions (Amsterdam: Elsevier) [Google Scholar]
  21. V. Blobel, “Unfolding methods in Particle Physics”, in Proceedings of the PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland, 17-20 January 2011, edited by H.B.Prosper, L.Lyons, CERN-2011-006, pp. 240–251 [http://cds.cern.ch/record/1306523] and references to unfolding therein. [Google Scholar]
  22. See for instance H. M. Antia, “Numerical methods for scientists and Engineers”, Birkhäuser, 2nd edition, (2002) [Google Scholar]
  23. A. N. Tikhonov ,V. Y. Arsenin “Solutions of ill-Posed Problem” Wiley, New York, (1977) [Google Scholar]
  24. See for instance T. M. Apostol (June 1967), “Calculus, Vol. 1: One-Variable Calculus with an Introduction to Linear Algebra 1” (2nd ed.),Wiley, ISBN 978-0-471-00005-1 [Google Scholar]
  25. C. E. Lawson and R. J. Hanson,“Solving Least Square Problems”, Prentice-Hall Inc., Englewood Cliffs, 1974. [Google Scholar]
  26. G. Zech “Regularization and error assignment to unfolded distributions”, in Proceedings of the PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland, 17-20 January 2011, edited by H.B.Prosper, L.Lyons, CERN-2011-006, pp. 252–259 [http://cds.cern.ch/record/1306523] and references to unfolding therein. [Google Scholar]
  27. H. N. M‘ulthei, B. Schorr, |em “On an iterative method for the unfolding of spectra”, Nucl. Instr. and Meth. A257 (1987) 371–377 [Google Scholar]
  28. L. B. Lucy “An iterative technique for the rectification of observed distributions”, Astronomical Journal 79(6) (1974) 745 [Google Scholar]
  29. See section 36.1.4. in Ref [14] and references therein. [Google Scholar]
  30. See the articles in reference 1 of [26], particularly [1] and [27]. [Google Scholar]
  31. G. D’Agostini, “A multidimensional unfolding method based on Bayes’ theorem” , Nucl. Instr. Meth. A 362 1995 (487) [Google Scholar]
  32. G. D’Agostini, “ Improved Iterative Bayesian unfolding”, http://arxiv.org/abs/1010.0632 [Google Scholar]
  33. J. H. Kuhn and G. Rodrigo, “Charge asymmetries of top quarks at hadron colliders revisited”, JHEP 1201, 063 (2012) [arXiv:1109.6830 [hep-ph]]. [Google Scholar]
  34. C. Shannon “A mathematical Theory of Communication” Bell System Technical Journal 27 (3) 379–423 [Google Scholar]
  35. J. E. Shore , “Relative Entropy, Probabilistic Inference and AI” , contribution to Proceedings of the First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-85), Corvallis, Oregon, 1985, pp 43–47, AUAI Press [http://uai.sis.pitt.edu/papers/85/p43-shore.pdf] [Google Scholar]
  36. E. T. Jaynes, “Information Theory and Statistical Mechanics”, Phys. Rev. 106 (1957) 620 [Google Scholar]
  37. M. Schmelling, “The method of reduced cross-entropy. A general approach to unfold probability distributions”, Nucl. Instr. Meth. A 340 (1994) 400–412 IN2P3 School Of Statistics, Autrans [Google Scholar]
  38. J. Skilling, “Quantified Maximum Entropy”, in Maximum Entropy and Bayesian Methods, Fundamental Theories of Physics, vol. 39, 1990, pp 341–350 and ed. P.F. Fougère (Kluwer, Dordrecht, Holland, 1990). [Google Scholar]
  39. H. P. Dembinski, M. Roth, “ARU - towards automatic unfolding of detector effects” in Proceedings of the PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland, 17-20 January 2011, edited by H.B.Prosper, L.Lyons, CERN-2011-006, pp. 285–291 [http://cds.cern.ch/record/1306523] and [http://aru.hepforge.org] [Google Scholar]
  40. C. de Boor, “A Practical Guide to Splines”, Springer Verlag (New York, Heidelberg, Berlin) (1978). [Google Scholar]
  41. R. Barlow, “Extended maximum Likelihood”, Nucl. Instrum. Meth. A297, 496 (1990) and references therein. [Google Scholar]
  42. G. Choudalakis, “Fully Bayesian Unfolding”, [arXiv:1201.4612[physics.data-an]] [Google Scholar]
  43. B. Malaescu, “An iterative, dynamically stabilized method of data unfolding”, [arXiv:0907.3791 [physics.data-an]] [Google Scholar]
  44. G. Aad et al. the ATLAS Collaboration,“Measurement of inclusive jet and dijet production in pp collisions at √s = 7 TeV using the ATLAS detector”, Phys. Rev. D 86, 014022 (2012) [arXiv:1112.6297 [hep-ex]]. [CrossRef] [Google Scholar]
  45. L. Lindemann, G. Zech, “Unfolding by Weighting Monte Carlo Events” , Nucl. Instr. Meth A 354 (1995) 516–521 [CrossRef] [Google Scholar]
  46. B. Aslan and G. Zech, “Statistical energy as a tool for binning-free, multivariate goodness- of-fit tests, two-sample comparison and unfolding”, Nucl. Instr. and Meth. A 537 (2005) 626 [CrossRef] [Google Scholar]
  47. M. Pivk, F. R. Le Diberder, “sPlot a statistical tool to unfold data distributions”, Nucl. Inst. Meth. A 555:356–369, (2005) [Google Scholar]
  48. G. Cowan, “Statistics for HEP. Lecture 4:Unfolding”, CERN Academic Training Lectures, CERN, Geneva, Switzerland, 5th April 2012, [http://indico.cern.ch/conferenceDisplay.py?confId=173729] [Google Scholar]
  49. G. Bohm and G. Zech, “Introduction to Statistics and Data Analysis for Physicists”, Verlag Deutsches Elektronen-Synchrotron (2010) [http://www-library.desy.de/elbook.html] [Google Scholar]
  50. Proceedings of the PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland, 17-20 January 2011, edited by H.B.Prosper, L.Lyons, CERN-2011-006 [http://cds.cern.ch/record/1306523] and [http://indico.cern.ch/conferenceOtherViews.py?view=standard&confId=107747] [Google Scholar]
  51. The Unfolding Framework Project, [https://www.wiki.terascale.de/index.php/Unfolding_Framework_ Project] (accessed on 9th May 2013 ) with software and references therein. [Google Scholar]
  52. T. Adye, “Unfolding algorithms and tests using RooUnfold” in Proceedings of the PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland, 17-20 January 2011, edited by H.B.Prosper, L.Lyons, CERN-2011-006, pp. 313–318 [http://cds.cern.ch/record/1306523] and [http://hepunx.rl.ac.uk/ adye/software/unfold/RooUnfold.html] [Google Scholar]
  53. R. Brun and F. Rademakers, “ROOT - An Object Oriented Data Analysis Framework”, Proceedings AIHENP’96 Workshop, Lausanne, Sep. 1996, Nucl. Inst. & Meth. in Phys. Res. A 389 (1997) 81–86. See also http://root.cern.ch/. [Google Scholar]
  54. G. D’Agostini, “Probabillity and Statistics - Improved iterative Bayesian unfolding” [http://www.roma1.infn.it/~dagos/unf2_R.tgz] written using the R Framework [ 54] [Google Scholar]
  55. R Development Core Team (2009), x“R: A language and environment for statistical computing”, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, [http://www.rproject.org]. [Google Scholar]
  56. V. Blobel, “Unfolding”, RUN program source files and Manual, [http://www.desy.de/~blobel/unfold.html] [Google Scholar]
  57. V. Kartvelishvili, “GURU”, [http://www.hep.lancs.ac.uk/guru.tar.gz] [Google Scholar]
  58. G. Hesketh, “Unfolding”, [ http://www-d0.fnal.gov/ ghesketh/unfolding/] [Google Scholar]
  59. H. P. Dembinski, M. Roth, “ARU development page”, [http://aru.hepforge.org] [Google Scholar]
  60. M. H. Kalos, P. A. Whitlock, “Monte Carlo Methods, Volume 1”, Wiley-VCH Publisher (John Wiley & Sons, Inc.), 2nd Edition, (2008) [Google Scholar]
  61. R. D. Cousins, V. L. Highland, “Incorporating systematic uncertainties into an upper limit” , Nucl. Instr. Meth. A.320 (1992) 331–335 [Google Scholar]
  62. the ATLAS Collaboration, “Procedure for the LHC Higgs boson search combination in summer 2011”, ATL-PHYS-PUB-2011-011 [https://cds.cern.ch/record/1375842] and references therein. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.