Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb-1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.

Sirunyan A.M., Tumasyan A., Adam W., Ambrogi F., Bergauer T., Dragicevic M., et al. (2020). Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. JOURNAL OF INSTRUMENTATION, 15(6), 1-85 [10.1088/1748-0221/15/06/P06005].

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Battilana C.;Bonacorsi D.;Borgonovi L.;Braibant-Giacomelli S.;Castro A.;Cuffiani M.;Fanfani A.;Fontanesi E.;Guiducci L.;Iemmi F.;Primavera F.;Rovelli T.;Siroli G. P.;
2020

Abstract

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb-1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
2020
Sirunyan A.M., Tumasyan A., Adam W., Ambrogi F., Bergauer T., Dragicevic M., et al. (2020). Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. JOURNAL OF INSTRUMENTATION, 15(6), 1-85 [10.1088/1748-0221/15/06/P06005].
Sirunyan A.M.; Tumasyan A.; Adam W.; Ambrogi F.; Bergauer T.; Dragicevic M.; Ero J.; Del Valle A.E.; Flechl M.; Fruhwirth R.; Jeitler M.; Krammer N.; ...espandi
File in questo prodotto:
File Dimensione Formato  
Sirunyan_2020_J._Inst._15_P06005.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 3.59 MB
Formato Adobe PDF
3.59 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/803941
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 107
  • ???jsp.display-item.citation.isi??? 86
social impact