  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.
