Speaker
Description
Quark-gluon discrimination could greatly improve the sensitivity of a
number of analyses at the LHC, and as such has received a significant
amount of investigation. Because the differences between quark and
gluon jets are largely contained in the jet substructure and are often
very subtle, this problem lends itself to machine learning techniques.
We explore this question in the LoLa framework, and demonstrate that
we see good discrimination for pure quark and gluon jets, both at
particle level and after including a fast detector simulation. Next,
we apply our network to a physics problem, a monojet Higgs -> invisible
signal (gluon dominated) with a Z+monojet background (quark dominated).
We investigate how this differs from the pure quark-gluon case and how
the jet transverse momentum affects the network performance.
