Abstract
The Higgs bosons and the top quark decay into rich and diverse final states, containing both light and heavy quarks, gluons, photons as well as W and Z bosons. This article reviews the challenges involved in reconstructing Higgs and top events at the FCC-ee and identifies the areas where novel developments are needed. The precise identification and reconstruction of these final states at the FCC-ee rely on the capability of the detector to provide excellent flavour tagging, jet energy and angular resolution, and global kinematic event reconstruction. Excellent flavour tagging performance requires low-material vertex and tracking detectors, and advanced machine learning techniques as successfully employed in LHC experiments. In addition, the Z pole run will provide abundant samples of heavy flavour partons that can be used for calibration of the tagging algorithms. For the reconstruction of jets, leptons, and missing energy, particle-flow algorithms are crucial to explore the full potential of the highly granular tracking and calorimeter systems, and give access to excellent energy–momentum resolution and precise identification of heavy bosons in their hadronic decays. This enables, among many other key elements, the reconstruction of Higgsstrahlung processes with leptonically and hadronically decaying Z bosons, and an almost background-free identification of top quark pair events. Exploiting the full available kinematic constraints together with exclusive jet clustering algorithms will allow for the optimisation of global event reconstruction with kinematic fitting techniques.
Highlights
It is expected that significant additional improvement on several aspects of jet and event reconstruction with Particle flow (PF) can be achieved with machine learning techniques that use the available information on various levels: from low-level object reconstruction, to jet finding, to global event reconstruction aspects such as jet pairing and other key aspects of precision measurements of the complex final states characterising many Higgs boson and top quark events at the FCC-ee
With the object reconstruction that is required for precise hadronic energy measurements already well developed, the limitations on global event reconstruction imposed by the uncertainties of the association of individual particles to jets emerge as a key challenge for the full exploitation of the physics potential of the inherently clean e+e− collisions
It is observed that the detector concepts currently being discussed for future e+e− colliders, with high resolution, low mass trackers, and highly granular calorimetry, enable object reconstruction and flavour tagging with unprecedented precision
Summary
The crucial detector feature to achieve this performance is a high spatial granularity in the calorimeter systems [7], which is exploited in the pattern recognition required to achieve an accurate matching of subdetector information, and provides the potential for improved calorimetric reconstruction using software compensation methods [8,9] This principle is adopted for the CLD detector concept [10] for FCC-ee, which is derived from the CLIC detector design, adjusted for the different experimental conditions in terms of collision energy and operation mode. It is expected that significant additional improvement on several aspects of jet and event reconstruction with PF can be achieved with machine learning techniques that use the available information on various levels: from low-level object reconstruction, to jet finding, to global event reconstruction aspects such as jet pairing and other key aspects of precision measurements of the complex final states characterising many Higgs boson and top quark events at the FCC-ee. Measurements of top quark properties crucially rely on the identification of
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