  In the deep learning era, improving the neural network performance in jet
physics is a rewarding task as it directly contributes to more accurate physics
measurements at the LHC. Recent research has proposed various network designs
in consideration of the full Lorentz symmetry, but its benefit is still not
systematically asserted, given that there remain many successful networks
without taking it into account. We conduct a detailed study on the
Lorentz-symmetric design. We propose two generalized approaches for modifying a
network - these methods are experimented on Particle Flow Network, ParticleNet,
and LorentzNet, and exhibit a general performance gain. We also reveal that the
notable improvement attributed to the "pairwise mass" feature in the network is
due to its introduction of a structure that fully complies with Lorentz
symmetry. We confirm that Lorentz-symmetry preservation serves as a strong
inductive bias of jet physics, hence calling for attention to such general
recipes in future network designs.
