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.

利用知识蒸馏提高学生模型对大型强子对撞机上轰击粒子分类任务的性能，并通过使用具有洛伦兹对称的强归纳偏差的教师模型，引入相同的归纳偏差以提高模型的鲁棒性