Year: 2025
Type: article
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 vectorlike 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 ultraboosted objects in the collider from their SM background counterpart. We evaluate the performance of these techniques in distinguishing W jets and top jets from quantum chromodynamics (QCD) jets at extremely high transverse momenta ( p T ) values, demonstrating their potential for future FCC-hh analyses.
DOI: 10.1103/nl33-vx8t
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415944611