Tagging ultraboosted jets at the FCC-hh using machine learning techniques

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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi>W</a:mi> </a:math> bosons and boosted top quarks in the context of three beyond the Standard Model scenarios: heavy vectorlike quark ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:msup> <c:mi>B</c:mi> <c:mo>′</c:mo> </c:msup> </c:math> ), heavy neutral gauge boson ( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:msup> <e:mi>Z</e:mi> <e:mo>′</e:mo> </e:msup> </e:math> ), and heavy neutral Higgs boson ( <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"> <g:mi>H</g:mi> </g:math> ). 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 <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"> <i:mi>W</i:mi> </i:math> jets and top jets from quantum chromodynamics (QCD) jets at extremely high transverse momenta ( <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"> <k:msub> <k:mi>p</k:mi> <k:mi>T</k:mi> </k:msub> </k:math> ) values, demonstrating their potential for future FCC-hh analyses.