  Jet identification is one of the fields in high energy physics that machine
learning has begun to make an impact. More often than not, convolutional neural
networks are used to classify jet images with the benefit that essentially no
physics input is required. Inspired by a recent work by Datta and Larkoski, we
study the classification of quark/gluon-initiated jets based on fully-connected
neural networks (FNNs), where expert-designed physical variables are taken as
input. FNNs are applied in two ways: trained separately on various narrow jet
transverse momentum $p_{TJ}$ bins; trained on a wide region of $p_{TJ} \in
[200,~1000]$ GeV. We find their performances are almost the same. The
performance is better when the $p_{TJ}$ is larger. Jet discrimination with FNN
is studied on both particle and detector level data. The results based on
particle level data are comparable with those from deep convolutional neural
networks, while the significance improvement characteristic (SIC) from detector
level data would at most decrease by $15\%$. We also test the performance of
FNNs with full set or subsets of jet observables as input features. The FNN
with one subset consisting of fourteen observables shows nearly no degradation
of performance. This indicates that these fourteen expert-designed observables
could have captured the most necessary information for separating quark and
gluon jets.
