Calculations for the Jet Mass with Grooming

  • Simone Marzani
  • Gregory Soyez
  • Michael Spannowsky
Part of the Lecture Notes in Physics book series (LNP, volume 958)


In this chapter we will revisit the calculations performed in Chap.  4 and extend them in order to describe jet mass distributions with grooming algorithms. In what follows, we are not going to present state-of-the art theoretical calculations, but instead we aim to keep the our discussion as simple as possible. Therefore, the theoretical accuracy of the calculations that we will present will be the minimum one which is required to capture the essential feature of the distributions. We will mostly concentrate of QCD jets, which present the most interesting and intricate features, while a discussion about jets originated to a boosted heavy particles will be presented in Sect. 6.4.


  1. 35.
    S. Catani, M.H. Seymour, A general algorithm for calculating jet cross sections in NLO QCD. Nucl. Phys. B485, 291–419 (1997). [hep-ph/9605323]Google Scholar
  2. 36.
    S. Catani, M.H. Seymour, The dipole formalism for the calculation of QCD jet cross sections at next-to-leading order. Phys. Lett. B378, 287–301 (1996) . [hep-ph/9602277]Google Scholar
  3. 56.
    M. Cacciari, G.P. Salam, G. Soyez, FastJet user manual. Eur. Phys. J. C72, 1896 (2012). [1111.6097]Google Scholar
  4. 63.
    M. Cacciari, G.P. Salam, G. Soyez, The anti-k(t) jet clustering algorithm. J. High Energy Phys. 04, 063 (2008). [0802.1189]CrossRefGoogle Scholar
  5. 77.
    M. Cacciari, G.P. Salam, Dispelling the N 3 myth for the k t jet-finder. Phys. Lett. B641, 57–61 (2006). [hep-ph/0512210]Google Scholar
  6. 79.
    M. Dasgupta, L. Magnea, G.P. Salam, Non-perturbative QCD effects in jets at hadron colliders. J. High Energy Phys. 0802, 055 (2008). [0712.3014]CrossRefGoogle Scholar
  7. 122.
    J.R. Andersen et al., Les Houches 2017: physics at TeV colliders Standard Model Working Group Report, in 10th Les Houches Workshop on Physics at TeV Colliders (PhysTeV 2017), Les Houches, June 5–23, 2017 (2018). 1803.07977ADSGoogle Scholar
  8. 123.
    M. Dasgupta, A. Fregoso, S. Marzani, G.P. Salam, Towards an understanding of jet substructure. J. High Energy Phys. 1309, 029 (2013). [1307.0007]Google Scholar
  9. 158.
    M. Dasgupta, A. Powling, A. Siodmok, On jet substructure methods for signal jets. J. High Energy Phys. 08, 079 (2015). [1503.01088]Google Scholar
  10. 177.
    S. Marzani, L. Schunk, G. Soyez, A study of jet mass distributions with grooming. J. High Energy Phys. 07, 132 (2017). [1704.02210]Google Scholar
  11. 178.
    T. Sjöstrand, S. Ask, J.R. Christiansen, R. Corke, N. Desai, P. Ilten et al., An introduction to PYTHIA 8.2. Comput. Phys. Commun. 191, 159–177 (2015). [1410.3012]ADSCrossRefGoogle Scholar
  12. 179.
    P. Skands, S. Carrazza, J. Rojo, Tuning PYTHIA 8.1: the Monash 2013 tune. Eur. Phys. J. C74, 3024 (2014). [1404.5630]Google Scholar
  13. 180.
    S. Marzani, L. Schunk, G. Soyez, The jet mass distribution after Soft Drop. Eur. Phys. J. C78, 96 (2018). [1712.05105]Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Simone Marzani
    • 1
  • Gregory Soyez
    • 2
  • Michael Spannowsky
    • 3
  1. 1.Dipartimento di FisicaUniversità di GenovaGenovaItaly
  2. 2.Institut de Physique TheoriqueCNRS UMR 3681, CEA SaclayGif-sur-Yvette cedexFrance
  3. 3.Department of Physics, Institute for Particle Physics PhenomenologyDurham UniversityDurhamUK

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