Tagging ultra-boosted jets at FCC-hh using machine learning techniques (2501.06702v1)

Abstract: The Future Circular Hadron Collider (FCC-hh) will probe unprecedented energy regimes, enabling direct searches for new elementary particles at a scale of tens of TeV. FCC-hh is currently in the planning stage, and one of its primary physics goals is to search for physics beyond the Standard Model by exploring a previously inaccessible kinematic domain. While venturing into uncharted high-energy territories promises excitement, reconstructing objects with enormous transverse momenta will require overcoming major experimental challenges. This work investigates the identification of boosted $W$ bosons and boosted top quarks in the context of three beyond the Standard Model scenarios: heavy vector-like quark ($B'$), heavy neutral gauge boson ($Z'$), and heavy neutral Higgs boson ($H$). We employ machine learning techniques, including eXtreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN), to identify these ultra-boosted objects in the collider from their SM background counterpart. We evaluate the performance of these techniques in distinguishing $W$ jets and top jets from QCD jets at extremely high transverse momenta ($p_{T}$) values, demonstrating their potential for future FCC-hh analyses.