ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
Aad, G. ; Åkesson, T.P.A. LU
; Corrigan, E.E.
LU
; Doglioni, C.
LU
; Ekman, P.A.
LU
; Geisen, J.
LU
; Hedberg, V.
LU
; Herde, H.
LU
; Jarlskog, G.
LU
and Konya, B.
LU
, et al.
(2023)
In European Physical Journal C
83(7).
- Abstract
- The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of s=13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model tt¯ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained. © 2023, The Author(s).
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/053d2cb0-d07d-478d-b4cd-cdddd62f8616
- author
- Aad, G.
; Åkesson, T.P.A.
LU
; Corrigan, E.E.
LU
; Doglioni, C.
LU
; Ekman, P.A.
LU
; Geisen, J.
LU
; Hedberg, V.
LU
; Herde, H.
LU
; Jarlskog, G.
LU
and Konya, B.
LU
, et al. (More)
- Aad, G.
; Åkesson, T.P.A.
LU
; Corrigan, E.E.
LU
; Doglioni, C.
LU
; Ekman, P.A.
LU
; Geisen, J.
LU
; Hedberg, V.
LU
; Herde, H.
LU
; Jarlskog, G.
LU
; Konya, B.
LU
; Lytken, E.
LU
; Mjörnmark, J.U.
LU
; Poettgen, R.
LU
; Simpson, N.D.
LU
; Skorda, E.
LU
; Smirnova, O.
LU
and Zwalinski, L.
(Less)
- author collaboration
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- in
- European Physical Journal C
- volume
- 83
- issue
- 7
- article number
- 681
- publisher
- Springer
- external identifiers
-
- scopus:85167625195
- ISSN
- 1434-6044
- DOI
- 10.1140/epjc/s10052-023-11699-1
- language
- English
- LU publication?
- yes
- id
- 053d2cb0-d07d-478d-b4cd-cdddd62f8616
- date added to LUP
- 2023-12-20 15:23:03
- date last changed
- 2023-12-20 15:23:58
@article{053d2cb0-d07d-478d-b4cd-cdddd62f8616,
abstract = {{The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of s=13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model tt¯ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained. © 2023, The Author(s).}},
author = {{Aad, G. and Åkesson, T.P.A. and Corrigan, E.E. and Doglioni, C. and Ekman, P.A. and Geisen, J. and Hedberg, V. and Herde, H. and Jarlskog, G. and Konya, B. and Lytken, E. and Mjörnmark, J.U. and Poettgen, R. and Simpson, N.D. and Skorda, E. and Smirnova, O. and Zwalinski, L.}},
issn = {{1434-6044}},
language = {{eng}},
number = {{7}},
publisher = {{Springer}},
series = {{European Physical Journal C}},
title = {{ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset}},
url = {{http://dx.doi.org/10.1140/epjc/s10052-023-11699-1}},
doi = {{10.1140/epjc/s10052-023-11699-1}},
volume = {{83}},
year = {{2023}},
}