Imperial College London
Alternate

Professor Geoffrey Hall FRS

Faculty of Natural Sciences, Department of Physics

Professor of Physics
 
 
 
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Contact

 

+44 (0)20 7594 7800g.hall Website

 
 
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Assistant

 

Ms Paula Brown +44 (0)20 7594 7823

 
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Location

 

511ABlackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sirunyan:2020:06/P06005,
author = {Sirunyan, AM and Tumasyan, A and Adam, W and Ambrogi, F and Bergauer, T and Brandstetter, J and Dragicevic, M and Eroe, J and Del, Valle AE and Flechl, M and Fruehwirth, R and Jeitler, M and Krammer, N and Kraetschmer, I and Liko, D and Madlener, T and Mikulec, I and Rad, N and Schieck, J and Schoefbeck, R and Spanring, M and Spitzbart, D and Waltenberger, W and Wulz, C-E and Zarucki, M and Drugakov, V and Mossolov, V and Gonzalez, JS and Darwish, MR and De, Wolf EA and Di, Croce D and Janssen, X and Lelek, A and Pieters, M and Sfar, HR and Van, Haevermaet H and Van, Mechelen P and Van, Putte S and Van, Remortel N and Blekman, F and Bols, ES and Chhibra, SS and D'Hondt, J and De, Clercq J and Lontkovskyi, D and Lowette, S and Marchesini, I and Moortgat, S and Python, Q and Skovpen, K and Tavernier, S and Van, Doninck W and Van, Mulders P and Burns, D and Beghin, D and Bilin, B and Brun, H and Clerbaux, B and De, Lentdecker G and Delannoy, H and Dorney, B and Favart, L and Grebenyuk, A },
doi = {06/P06005},
journal = {Journal of Instrumentation},
pages = {1--87},
title = {Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques},
url = {http://dx.doi.org/10.1088/1748-0221/15/06/P06005},
volume = {15},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Sirunyan,AM
AU  - Tumasyan,A
AU  - Adam,W
AU  - Ambrogi,F
AU  - Bergauer,T
AU  - Brandstetter,J
AU  - Dragicevic,M
AU  - Eroe,J
AU  - Del,Valle AE
AU  - Flechl,M
AU  - Fruehwirth,R
AU  - Jeitler,M
AU  - Krammer,N
AU  - Kraetschmer,I
AU  - Liko,D
AU  - Madlener,T
AU  - Mikulec,I
AU  - Rad,N
AU  - Schieck,J
AU  - Schoefbeck,R
AU  - Spanring,M
AU  - Spitzbart,D
AU  - Waltenberger,W
AU  - Wulz,C-E
AU  - Zarucki,M
AU  - Drugakov,V
AU  - Mossolov,V
AU  - Gonzalez,JS
AU  - Darwish,MR
AU  - De,Wolf EA
AU  - Di,Croce D
AU  - Janssen,X
AU  - Lelek,A
AU  - Pieters,M
AU  - Sfar,HR
AU  - Van,Haevermaet H
AU  - Van,Mechelen P
AU  - Van,Putte S
AU  - Van,Remortel N
AU  - Blekman,F
AU  - Bols,ES
AU  - Chhibra,SS
AU  - D'Hondt,J
AU  - De,Clercq J
AU  - Lontkovskyi,D
AU  - Lowette,S
AU  - Marchesini,I
AU  - Moortgat,S
AU  - Python,Q
AU  - Skovpen,K
AU  - Tavernier,S
AU  - Van,Doninck W
AU  - Van,Mulders P
AU  - Burns,D
AU  - Beghin,D
AU  - Bilin,B
AU  - Brun,H
AU  - Clerbaux,B
AU  - De,Lentdecker G
AU  - Delannoy,H
AU  - Dorney,B
AU  - Favart,L
AU  - Grebenyuk,A
AU  - Kalsi,AK
AU  - Popov,A
AU  - Postiau,N
AU  - Starling,E
AU  - Thomas,L
AU  - Vander,Velde C
AU  - Vanlaer,P
AU  - Vannerom,D
AU  - Fang,W
AU  - Gao,X
AU  - Cornelis,T
AU  - Dobur,D
AU  - Khvastunov,I
AU  - Niedziela,M
AU  - Roskas,C
AU  - Trocino,D
AU  - Tytgat,M
AU  - Verbeke,W
AU  - Vermassen,B
AU  - Vit,M
AU  - Bondu,O
AU  - Bruno,G
AU  - Caputo,C
AU  - David,P
AU  - Delaere,C
AU  - Delcourt,M
AU  - Giammanco,A
AU  - Lemaitre,V
AU  - Prisciandaro,J
AU  - Saggio,A
AU  - Marono,MV
AU  - Vischia,P
AU  - Zobec,J
AU  - Alves,FL
AU  - Alves,GA
AU  - Correia,Silva G
AU  - Hensel,C
AU  - Moraes,A
AU  - Rebello,Teles P
AU  - Belchior,Batista Das Chagas E
AU  - Carvalho,W
AU  - Chinellato,J
AU  - Coelho,E
AU  - Da,Costa EM
AU  - Da,Silveira GG
AU  - De,Jesus Damiao D
AU  - De,Oliveira Martins C
AU  - Fonseca,De Souza S
AU  - Huertas,Guativa LM
AU  - Malbouisson,H
AU  - Martins,J
AU  - Matos,Figueiredo D
AU  - Medina,Jaime M
AU  - Melo,De Almeida M
AU  - Mora,Herrera C
AU  - Mundim,L
AU  - Nogima,H
AU  - Prado,Da Silva WL
AU  - Sanchez,Rosas LJ
AU  - Santoro,A
AU  - Sznajder,A
AU  - Thiel,M
AU  - Tonelli,Manganote EJ
AU  - Torres,Da Silva De Araujo F
AU  - Vilela,Pereira A
AU  - Bernardes,CA
AU  - Calligaris,L
AU  - Fernandez,Perez Tomei TR
AU  - Novaes,SF
AU  - Padula,S
AU  - Gregores,EM
AU  - Lemos,DS
AU  - Mercadante,PG
AU  - Aleksandrov,A
AU  - Antchev,G
AU  - Hadjiiska,R
AU  - Iaydjiev,P
AU  - Misheva,M
AU  - Rodozov,M
AU  - Shopova,M
AU  - Sultanov,G
AU  - Bonchev,M
AU  - Dimitrov,A
AU  - Ivanov,T
AU  - Litov,L
AU  - Pavlov,B
AU  - Petkov,P
AU  - Yuan,L
AU  - Ahmad,M
AU  - Hu,Z
AU  - Wang,Y
AU  - Dozen,C
AU  - Chen,GM
AU  - Chen,HS
AU  - Chen,M
AU  - Jiang,CH
AU  - Leggat,D
AU  - Liao,H
AU  - Liu,Z
AU  - Spiezia,A
DO  - 06/P06005
EP  - 87
PY  - 2020///
SN  - 1748-0221
SP  - 1
TI  - Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
T2  - Journal of Instrumentation
UR  - http://dx.doi.org/10.1088/1748-0221/15/06/P06005
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000545350900005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://iopscience.iop.org/article/10.1088/1748-0221/15/06/P06005
UR  - http://hdl.handle.net/10044/1/80869
VL  - 15
ER  -
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