One of the most important aspects of data analysis at the LHC experiments is the particle identification (PID). In LHCb, several different sub-detectors provide PID information: two Ring Imaging Cherenkov (RICH) detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, we have developed models based on deep learning and gradient boosting. The new approaches, tested on simulated samples, provide higher identification performances than the current solution for all charged particle types. It is also desirable to achieve a flat dependency of efficiencies from spectator variables such as particle momentum, in order to reduce systematic uncertainties in the physics results. For this purpose, models that improve the flatness property for efficiencies have also been developed. This paper presents this new approach and its performance.

Machine-Learning-based global particle-identification algorithms at the LHCb experiment / Derkach, Denis; Hushchyn, Mikhail; Likhomanenko, Tatiana; Rogozhnikov, Alex; Kazeev, Nikita; Chekalina, Victoria; Neychev, Radoslav; Kirillov, Stanislav; Ratnikov, Fedor. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - 1085:(2018), p. 042038. [10.1088/1742-6596/1085/4/042038]

Machine-Learning-based global particle-identification algorithms at the LHCb experiment

Kazeev, Nikita;
2018

Abstract

One of the most important aspects of data analysis at the LHC experiments is the particle identification (PID). In LHCb, several different sub-detectors provide PID information: two Ring Imaging Cherenkov (RICH) detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, we have developed models based on deep learning and gradient boosting. The new approaches, tested on simulated samples, provide higher identification performances than the current solution for all charged particle types. It is also desirable to achieve a flat dependency of efficiencies from spectator variables such as particle momentum, in order to reduce systematic uncertainties in the physics results. For this purpose, models that improve the flatness property for efficiencies have also been developed. This paper presents this new approach and its performance.
2018
particle physics machine learning
01 Pubblicazione su rivista::01a Articolo in rivista
Machine-Learning-based global particle-identification algorithms at the LHCb experiment / Derkach, Denis; Hushchyn, Mikhail; Likhomanenko, Tatiana; Rogozhnikov, Alex; Kazeev, Nikita; Chekalina, Victoria; Neychev, Radoslav; Kirillov, Stanislav; Ratnikov, Fedor. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - 1085:(2018), p. 042038. [10.1088/1742-6596/1085/4/042038]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

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/11573/1180881
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 5
social impact