Journal
SCIPOST PHYSICS
Volume 6, Issue 6, Pages -Publisher
SCIPOST FOUNDATION
DOI: 10.21468/SciPostPhys.6.6.069
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Funding
- state of Baden-Wurttemberg through bwHPC
- German Research Foundation (DFG) [INST 39/963-1 FUGG]
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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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