Topic Model for four-top at the LHC

TL;DR

This paper explores applying a Topic Model algorithm to four-top quark searches at the LHC, aiming to reduce reliance on Monte Carlo simulations despite the system's complexity and uncertainties.

Contribution

It demonstrates a novel application of Topic Models to four-top searches, including background reconstruction and Monte Carlo tuning methods.

Findings

Background can be reconstructed independently of Monte Carlo.

Using the anchor bin allows for background estimation in the signal region.

Machine Learning provides slight improvements to the Topic Model.

Abstract

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.