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In the deep learning era, improving the neural network performance in jetphysics is a rewarding task as it directly contributes to more accurate physicsmeasurements at the LHC. Recent research has proposed various network designsin consideration of the full Lorentz symmetry, but its benefit is still notsystematically asserted, given that there remain many successful networkswithout taking it into account. We conduct a detailed study on theLorentz-symmetric design. We propose two generalized approaches for modifying anetwork - these methods are experimented on PFN, ParticleNet, and LorentzNet,and exhibit a general performance gain. We also reveal that the notableimprovement attributed to the "pairwise mass" feature in the network is due toits introduction of a structure that fully complies with Lorentz symmetry. Weconfirm that Lorentz-symmetry preservation serves as a strong inductive bias ofjet physics, hence calling for attention to such general recipes in futurenetwork designs.

Does Lorentz-symmetric design boost network performance in jet physics?