  Machine learning has played a pivotal role in advancing physics, with deep
learning notably contributing to solving complex classification problems such
as jet tagging in the field of jet physics. In this experiment, we aim to
harness the full potential of neural networks while acknowledging that, at
times, we may lose sight of the underlying physics governing these models.
Nevertheless, we demonstrate that we can achieve remarkable results obscuring
physics knowledge and relying completely on the model's outcome. We introduce
JetLOV, a composite comprising two models: a straightforward multilayer
perceptron (MLP) and the well-established LundNet. Our study reveals that we
can attain comparable jet tagging performance without relying on the
pre-computed LundNet variables. Instead, we allow the network to autonomously
learn an entirely new set of variables, devoid of a priori knowledge of the
underlying physics. These findings hold promise, particularly in addressing the
issue of model dependence, which can be mitigated through generalization and
training on diverse data sets.
