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.

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques / 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., Kratschmer, I., Liko, D., Madlener, T., Mikulec, I., Rad, N., Schieck, J., Schofbeck, R., Spanring, M., et al.. - In: JOURNAL OF INSTRUMENTATION. - ISSN 1748-0221. - 15:6(2020), pp. P06005-P06005. [10.1088/1748-0221/15/06/P06005]

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

Longo E.;Organtini G.;Paramatti R.;Quaranta C.;Rahatlou S.;Santanastasio F.;
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
Large detector-systems performance; Pattern recognition, cluster finding, calibration and fitting methods
01 Pubblicazione su rivista::01a Articolo in rivista
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques / 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., Kratschmer, I., Liko, D., Madlener, T., Mikulec, I., Rad, N., Schieck, J., Schofbeck, R., Spanring, M., et al.. - In: JOURNAL OF INSTRUMENTATION. - ISSN 1748-0221. - 15:6(2020), pp. P06005-P06005. [10.1088/1748-0221/15/06/P06005]
File allegati a questo prodotto
File Dimensione Formato  
Sirunyan_Identification Of Heavy Energetic_2020.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 5.48 MB
Formato Adobe PDF
5.48 MB Adobe PDF

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/11573/1495758
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 150
  • ???jsp.display-item.citation.isi??? 121
social impact