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Jet Reconstruction and Performance

  • Steven SchrammEmail author
Chapter
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Part of the Springer Theses book series (Springer Theses)

Abstract

Unlike the other objects discussed in Chap.  3, jets are not a physical particle from the SM, rather they are a tool designed to represent an underlying physical process. While electrons exist as an independent entity, jets are defined only by the method which was used to build them. When a muon is observed, all of the different reconstruction algorithms aim to reproduce the single muon to the best precision with a single set of detector observations. On the other hand, the same set of detector observations can be used to build radically different types of jets, many of which are independently useful. Examples of the independent uses for different types of jet algorithms will be provided.

Keywords

Primary Vertex Ghost Particle Muon System Hadronic Shower Tile Calorimeter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    ATLAS Collaboration, Performance of jet substructure techniques for large-\(R\) jets in proton-proton collisions at \(\sqrt{s} = 7 TeV\) using the ATLAS detector. J. High Energy Phys. 1309, 076 (2013). arXiv:1306.4945 [hep-ex]
  2. 2.
    W. Lamp et al., Calorimeter clustering algorithms: description and performance, Technical report, ATL-LARG-PUB-2008-002, CERN, Geneva, Apr 2008Google Scholar
  3. 3.
    J.E. Huth et al., Toward a standardization of jet definitions. Technical report, FERMILAB-CONF-90-249-E, Dec 1990Google Scholar
  4. 4.
    G.P. Salam, Towards jetography. Eur. Phys. J. C 67, 637–686 (2009). arXiv:0906.1833 [hep-ph]
  5. 5.
    M. Cacciari, G. Salam, G. Soyez, FastJet user manual. Eur. Phys. J. C 72(3) (2012). doi: 10.1140/epjc/s10052-012-1896-2
  6. 6.
    M. Cacciari, G.P. Salam, G. Soyez, The anti-kt jet clustering algorithm. J. High Energy Phys. 04, 063 (2008)Google Scholar
  7. 7.
    ATLAS Collaboration, Selection of jets produced in proton–proton collisions with the ATLAS detector using 2011 data. Technical report, ATLAS-CONF-2012-020, CERN, Geneva, Mar 2012Google Scholar
  8. 8.
    ATLAS Collaboration, Luminosity determination in \(pp\) collisions at \(\sqrt{s}=7\) TeV using the ATLAS detector at the LHC. Eur. Phys. J. C 71, 1630 (2011). arXiv:1101.2185 [hep-ex]
  9. 9.
    ATLAS Collaboration, Pile-up subtraction and suppression for jets in ATLAS. Technical report, ATLAS-CONF-2013-083, CERN, Geneva, Aug 2013Google Scholar
  10. 10.
    ATLAS Collaboration, Tagging and suppression of pileup jets with the ATLAS detector. Technical report, ATLAS-CONF-2014-018, CERN, Geneva, May 2014Google Scholar
  11. 11.
    T. Gabriel et al., Energy dependence of hadronic activity. Nucl. Instrum. Methods A 338(2), 336–347 (1994)ADSCrossRefGoogle Scholar
  12. 12.
    D.E. Groom, Energy flow in a hadronic cascade: application to hadron calorimetry. Nucl. Instrum. Methods A 572(2), 633–653 (2007). doi: 10.1016/j.nima.2006.11.070 ADSCrossRefGoogle Scholar
  13. 13.
    ATLAS Collaboration, Jet energy measurement and its systematic uncertainty in proton–proton collisions at \(\sqrt{s}=7\) TeV with the ATLAS detector. arXiv:1406.0076 [hep-ex]
  14. 14.
    ATLAS Collaboration, Properties of jets and inputs to jet reconstruction and calibration with the ATLAS detector using proton–proton collisions at \(\sqrt{s}=7\) TeV. Technical report, ATLAS-CONF-2010-053, CERN, Geneva, July 2010Google Scholar
  15. 15.
    S. Batista et al., Global sequential calibration with the ATLAS detector in proton–proton collisions at sqrt(s) = 8 TeV with ATLAS 2012 data. Technical report, ATL-COM-PHYS-2014-753, CERN, Geneva, June 2014Google Scholar
  16. 16.
    C.W. Fabjan, T. Ludlam, Calorimetry in high-energy physics. Annu. Rev. Nucl. Part. Sci. 32(1), 335–389 (1982)ADSCrossRefGoogle Scholar
  17. 17.
    T. Sjostrand, S. Mrenna, P. Skands, PYTHIA 8.1. Comput. Phys. Commun. 178, 852 (2008)ADSCrossRefzbMATHGoogle Scholar
  18. 18.
    M. Bahr et al., Herwig++ physics and manual. Eur. Phys. J. C 58, 639–707 (2008). arXiv:0803.0883 [hep-ph]
  19. 19.
    S. Frixione, P. Nason, G. Ridolf, A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction. J. High Energy Phys. 0709, 126 (2007). arXiv:0707.3088
  20. 20.
    T. Gleisberg et al., Event generation with Sherpa 1.1. J. High Energy Phys. 02, 007 (2009). arXiv:0811.4622 [hep-ph]
  21. 21.
    ATLAS Collaboration, Jet/EtMiss approved 2013 JES uncertainty, https://twiki.cern.ch/twiki/bin/view/AtlasPublic/JetEtmissApproved2013JESUncertainty
  22. 22.
    ATLAS Collaboration, Jet energy measurement with the ATLAS detector in proton–proton collisions at \(\sqrt{s}=7\) TeV. Eur. Phys. J. C 73, 2304 (2013). arXiv:1112.6426 [hep-ex]
  23. 23.
    S. Gupta, Punch-through derivation plots, Private communications, 2013–2014Google Scholar
  24. 24.
    ATLAS Collaboration, The ATLAS experiment at the CERN large hadron collider. J. Instrum. 3, S08003 (2008). doi: 10.1088/1748-0221/3/08/S08003
  25. 25.
    ATLAS Collaboration, Jet energy resolution in proton–proton collisions at \(\sqrt{s}=7\) TeV recorded in 2010 with the ATLAS detector. Eur. Phys. J. C 73, 2306 (2013). arXiv:1210.6210 [hep-ex]
  26. 26.
    ATLAS Collaboration, Jet/EtMiss approved 2013 JER 2011, https://twiki.cern.ch/twiki/bin/view/AtlasPublic/JetEtmissApproved2013Jer2011
  27. 27.
    ATLAS Collaboration, Approved tile calorimeter plots—detector status, https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ApprovedPlotsTile#Detector_Status
  28. 28.
    K. Perez, Masked tile calorimeter corrections. Private communication, internal talk on 18 Nov 2010Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Section de PhysiqueUniversity of GenevaGenevaSwitzerland

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