  We study the implementation of a Topic Model algorithm in four-top searches
at the LHC as a test-probe of a not ideal system for applying this technique.
We study this Topic Model behavior as its different hypotheses such as mutual
reducibility and equal distribution in all samples shift from true. The
four-top final state at the LHC is not only relevant because it does not
fulfill these conditions, but also because it is a difficult and inefficient
system to reconstruct and current Monte Carlo modeling of signal and
backgrounds suffers from non-negligible uncertainties. We implement this Topic
Model algorithm in the Same-Sign lepton channel where S/B is of order one and
all backgrounds cannot have more than two b-jets at parton level. We define
different mixtures according to the number of b-jets and we use the total
number of jets to demix. Since only the background has an anchor bin, we find
that we can reconstruct the background in the signal region independently of
Monte Carlo. We propose to use this information to tune the Monte Carlo in the
signal region and then compare signal prediction with data. We also explore
Machine Learning techniques applied to this Topic Model algorithm and find
slight improvements as well as potential roads to investigate. Although our
findings indicate that still with the full LHC run 3 data the implementation
would be challenging, we pursue through this work to find ways to reduce the
impact of Monte Carlo simulations in four-top searches at the LHC.
