Speaker
Description
In the study of dark matter detection at colliders, novel candidates have emerged to bridge between experimental data and theoretical models. Dark Showers (DS) are being studied as an extension of the Standard Model (SM), containing both invisible and visible particles that allow us to predict scenarios involving collider observables. Among these, Semi-Visible Jets (SVJs), represent a novel promising new signature, particularly those mediated by a massive $Z^{\prime}$ boson that enables the production of heavy dark hadrons. In such a scenario, jets enclose visible SM particles and invisible dark matter components, having more Missing Transverse Energy $E_{T}^{miss}$ (MET).
The complexity is further heightened since the poorly constrained nature of the dark sector; factors such as the dark confinement scale, dark meson masses and the fraction of the invisible DM hadrons. Our study considers different topologies, including a resonant heavy gauge boson $Z^\prime$, resulting in different approaches to study the kinematic features of the produced SVJs compared to those of the QCD ones, we are looking at correlations between the MET respect to their jet axes, alongside the EMD, Energy Correlation Functions and the Lund Jet Plane with the purpose of classifying SVJs from QCD jets and characterising them by finding a right description for a Machine Learning (ML) model to work with. For it, we are looking at a transformer-based NN to not only classify between SVJs and QCD jets but also, create pseudo-labels to cluster the data between invisible and/or visible particles.