Topic Modeling in New Physics Detection

In this work, we apply topic modeling to detect new physics in proton-proton collisions at the LHC in an unsupervised way. We investigate three new physics scenarios where fully leptonic $t\bar{t}\to b\bar{b}{\mathrm{\ell }}^{+}{\mathrm{\ell }}^{-}{\nu }_{\mathrm{\ell }}{\bar{\nu }}_{\mathrm{\ell }}$ is the main source of background without relying on jet substructure variables. We demonstrate that the algorithm remains effective even in this low-particle multiplicity framework, complementing jet tagging studies, where it is typically employed. Moreover, we demonstrate that the performance of topic modeling is competitive or even better than well-known outlier detectors, such as isolation forest and variational autoencoders, with moderate and high background pollution in almost all new physics scenarios considered.