Speaker
Description
In the study of DM 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 hadronisation constant, dark meson masses and the fraction of the invisible DM hadrons will be part on the extense amount of hidden sector parameters that manage how the standard model hadrons will decay eventually into kinematical observables. Our study will review different tools at a 2-component analysis, global-level jet kinematics and jet-substructure level to study Semi-Visible Jets (SVJs), formed by the decay of a resonant heavy gauge boson $Z^\prime$. We characterise a di-jet system focused on final-state global jet observables such as the transverse momentum, azimuthal angle and pseudorapidity $(p_{T},\phi,\eta)$, with modern related MET studies as the global MET and the difference on the azimuthal angle between the leading jet and the global MET, alonside jet-substructure metrics such as the Energy-Energy Correlation Functions (EECs), Angularity ($\tau$), SM Hadrons multiplicity and the Lund Jet Plane (LJP) as kinematical features. We do all this characterisation since our ultimately goal is to differentiate between QCD and SVJs event signals with modern Deep Learning (DL) analyses, we implemented a Vision Transformer ($\text{ViT}$) neural network (NN) focused on Lund plane images and a MultiLayer Perceptron ($\text{MLP}$) NN for the rest of high-level observables and finally use evaluation metrics like $\text{AUC}$, accuracy and $\text{ROC}$ curve to evaluate not only the classification performance but also the signal rejection in SVJs searches for future experimental searches.
It is worthly saying that the paper should be soon on arxiv.