  The Shower Deconstruction methodology is pivotal in distinguishing signal and
background jets, leveraging the detailed information from perturbative parton
showers. Rooted in the Neyman-Pearson lemma, this method is theoretically
designed to differentiate between signal and background processes optimally in
high-energy physics experiments. A key challenge, however, arises from the
combinatorial growth associated with increasing jet constituents, which hampers
its computational feasibility. We address this by demonstrating that the
likelihood derived from comparing the most probable signal and background
shower histories is equally effective for discrimination as the conventional
approach of summing over all potential histories in top quark versus Quantum
Chromodynamics (QCD) scenarios. We propose a novel approach by conceptualising
the identification of the most probable shower history as a Markov Decision
Process (MDP). Utilising a sophisticated modular point-transformer
architecture, our method efficiently learns the optimal policy for this task.
The developed neural agent excels in constructing the most likely shower
history and demonstrates robust generalisation capabilities on unencountered
test data. Remarkably, our approach mitigates the complexity inherent in the
inference process, achieving a linear scaling relationship with the number of
jet constituents. This offers a computationally viable and theoretically sound
method for signal-background differentiation, paving the way for more effective
data analysis in particle physics.
