  The challenging environment of real-time data processing systems at the Large
Hadron Collider (LHC) strictly limits the computational complexity of
algorithms that can be deployed. For deep learning models, this implies that
only models with low computational complexity that have weak inductive bias are
feasible. To address this issue, we utilize knowledge distillation to leverage
both the performance of large models and the reduced computational complexity
of small ones. In this paper, we present an implementation of knowledge
distillation, demonstrating an overall boost in the student models' performance
for the task of classifying jets at the LHC. Furthermore, by using a teacher
model with a strong inductive bias of Lorentz symmetry, we show that we can
induce the same inductive bias in the student model which leads to better
robustness against arbitrary Lorentz boost.
