Quark-gluon tagging: Machine learning vs detector
Keyword(s):
Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.
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2017 ◽
Vol 7
(7)
◽
pp. 172
2017 ◽
Vol 7
(6)
◽
pp. 812-816
Keyword(s):
2018 ◽
Vol 8
(1)
◽
pp. 14
2018 ◽
Vol 6
(6)
◽
pp. 300
◽
2019 ◽
Vol 7
(6)
◽
pp. 842-846
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