Permutationless many-jet event reconstruction with symmetry preserving attention networks - INSPIRE

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Permutationless many-jet event reconstruction with symmetry preserving attention networks

Michael James Fenton
(
- UC, Irvine
)
,
Alexander Shmakov
(
- UC, Irvine (main)
)
,
Ta-Wei Ho
(
- Taiwan, Natl. Tsing Hua U.
)
,
Shih-Chieh Hsu
(
- Washington U., Seattle
)
,
Daniel Whiteson
(
- UC, Irvine
)

Oct 19, 2020

Published in:

Phys.Rev.D 105 (2022) 11, 112008

Published: Jun 1, 2022

e-Print:

2010.09206 [hep-ex]

DOI:

10.1103/PhysRevD.105.112008 (publication)

View in:

ADS Abstract Service

reference search49 citations

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Abstract: (APS)

Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques. The most common decay mode, the “all-jet” channel, results in a 6-jet final-state which is particularly difficult to reconstruct in

p p

collisions due to the large number of permutations possible. We present a novel approach to this class of problem, based on neural networks using a generalized attention mechanism, that we call symmetry preserving attention networks (spa-net). We train one such network to identify the decay products of each top quark unambiguously and without combinatorial explosion as an example of the power of this technique. This approach significantly outperforms existing state-of-the-art methods, correctly assigning all jets in 80.7% of 6-jet, 66.8% of 7-jet, and 52.3% of

\geq 8

-jet events respectively.

Note:

replaced with final published version

p p: colliding beams
top: pair production
final state: ((n)jet)
particle: massive
p p: scattering
network
neural network
CERN LHC Coll
hadronization
top: decay modes

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