Related papers: Jet Flavour Tagging for Future Colliders with Fast…
Jet flavor tagging is of utmost importance for unlocking the full physics potential of any future collider experiment. The performance of any jet flavor identification algorithm depends both on its underlying architecture and on the…
Jet flavour identification in multi-TeV e+e- collisions is expected to provide insights on new phenomena at scales beyond those probed by the LHC. The anticipated high track density and jet collimation represent a new challenge to jet…
We report on the progress in flavor identification tools developed for a future $e^+e^-$ linear collider such as the International Linear Collider (ILC) and Compact Linear Collider (CLIC). Building on the work carried out by the LCFIVertex…
Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, is presented, which exploits a transformer-based neural network that is substantially faster to train than state-of-the-art graph…
The accurate identification of heavy-flavour jets, those which originate from bottom or charm quarks, is crucial for precision studies of the Standard Model and searches for new physics. However, assigning flavour to jets presents…
Classification of jets as originating from light-flavor or heavy-flavor quarks is an important task for inferring the nature of particles produced in high-energy collisions. The large and variable dimensionality of the data provided by the…
The correct identification of charged hadrons plays a crucial role in flavor-physics measurements. The final detector configurations at the proposed Future Circular Collider are yet to be determined and this study aims to contribute to this…
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern…
Identifying the flavour of reconstructed hadronic jets is critical for precision phenomenology and the search for new physics at collider experiments, as it allows to pinpoint specific scattering processes and reject backgrounds. Jet…
Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton-proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a…
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of $\sqrt s = 13$ TeV $pp$ collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on…
A calibration of the ATLAS flavour-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the $b$-jet, $c$-jet, and light-flavour jet classification…
Circular colliders have the advantage of delivering collisions to multiple interaction points, which allow different detector designs to be studied and optimized - up to four for FCC-ee. On the one hand, the detectors must satisfy the…
The Linear Collider Flavour Identification (LCFI) collaboration studies CCD detectors for quark flavour identification in the framework of a future linear e+e- collider. The flavour identification is based on precision reconstruction of…
The extensive and ambitious physics program planned at the Future Circular Collider for electrons and positrons (FCC-ee) imposes strict constraints on detector performance. This work investigates how different detector properties impact jet…
We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the…
This paper presents a new tool to perform various steps in jet tagger development in an efficient and comprehensive way. A common data structure is used for training, as well as for performance evaluation in data. The introduction of this…
Identification of quark flavor is essential for collider experiments in high-energy physics, relying on the flavor tagging algorithm. In this study, using a full simulation of the Circular Electron Positron Collider (CEPC), we investigated…
Machine Learning is a rapidly expanding field with a wide range of applications in science. In the field of physics, the Large Hadron Collider, the world's largest particle accelerator, utilizes Neural Networks for various tasks, including…
Identification of hadronic jets originating from heavy-flavor quarks is extremely important to several physics analyses in High Energy Physics, such as studies of the properties of the top quark and the Higgs boson, and searches for new…