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Andrea Sciandra (Brookhaven National Laboratory (US))22/10/2025, 12:15Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
Jet flavour identification plays a central role in unlocking the full physics potential of the Future Circular Collider (FCC). In particular, flavour tagging is essential for the FCC-ee Higgs programme, where hadronic decays dominate. The ability to efficiently distinguish between b-, c-, s-, and gluon jets enables the study of rare Higgs decay modes that remain inaccessible at the LHC,...
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Taikan Suehara (ICEPP, The University of Tokyo)22/10/2025, 12:30Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
Jet flavor tagging for linear Higgs factories (ILC, CLIC) has long been done with BDT-based algorithm. Stimulated from recent improvements in LHC experiments, the update has been done with DNN-based algorithm, namely Particle Transformer (ParT). It already shows great improvement of around factor 10 in background rejection for b and c tagging. It also enables to do strange tagging as well as...
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Prof. Freya Blekman (Deutsches Elektronen-Synchrotron (DE))22/10/2025, 12:45Flavour, Top and QCDTalk
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.
The DeepJetTransformer network uses information from particle flow-style objects and secondary vertex reconstruction, as is standard for $b$- and $c$-jet identification,...
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Benjamin Schwenker (University Goettingen)22/10/2025, 13:00Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
We report on a new flavor tagging algorithm developed to determine the quark-flavor content of bottom mesons at Belle II. Our new end-to-end algorithm, TFlat, uses transformer blocks to predict the flavor of neutral mesons produced in decays. It improves previous algorithms by using the information from all charged and neutral final-state particles. In contrast to previous algorithms, TFlat...
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Hao Liang (Centre National de la Recherche Scientifique (FR))22/10/2025, 13:15Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
Future high-luminosity electron–positron colliders such as the CEPC and FCC-ee, with a circumference of around100 km, will produce predominantly hadronic events, offering a rich environment to study jet physics.
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A central focus of this work is jet origin identification (JOI), a general framework extending beyond traditional jet flavor tagging. JOI distinguishes among 11 categories, including... -
Alan Price (Jagiellonian University)23/10/2025, 11:30Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
Accurate, fully differential predictions are essential for precise experimental analyses at linear colliders, and are typically delivered by parton-shower Monte Carlo programs. Last year, the event generator Sherpa has been released in a new version (3.0), with an emphasis on incorporating higher-order electroweak and QCD effects. We will summarise its new features, particularly those relevant...
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Krzysztof Mekala23/10/2025, 11:45Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
We report on current developments and future plans for the Whizard Monte-Carlo generator framework: On the physics part, we focus on recent progress and applications of the NLO EW automation, both for SM and BSM models, as well as the effective vector boson approxomation and EW PDFs for EW interactions at the higest energies. On the technological part, we comment on the current status of the...
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Alan Price (Jagiellonian University)23/10/2025, 12:00Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
Future electron–positron Higgs/Top/Electroweak factories will require Monte Carlo event generation at the same level of precision as the experimental measurements. Since several generators can provide predictions of comparable accuracy, it is essential to compare them consistently to identify deviations and evaluate systematic uncertainties arising from the event generation stage.
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We give an... -
Thomas Madlener (Deutsches Elektronen-Synchrotron (DE))23/10/2025, 12:15Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
The Key4hep software ecosystem provides a common software stack for studying the physics potential at future collider facilities. It provides all the necessary tools for physics studies ranging from event generation and detector simulation to reconstruction and analysis. The shared effort of several communities, including ILC, CLIC, FCC and CEPC, have made Key4hep the de-facto standard for...
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Bryan Bliewert23/10/2025, 12:30Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
Measuring the Higgs potential represents one of the main goals of the physics programs of future colliders. At center-of-mass energies of $\sqrt{s} \geq 500 \mathrm{GeV}$, direct access to the self-coupling $\lambda$ is enabled through the ZHH process. The ongoing update of the ILD ZHH analysis focuses on the final state $HH \rightarrow b\bar{b}b\bar{b}$. In our contribution, we discuss recent...
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Ulrich Einhaus (KIT)23/10/2025, 12:45Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
One current focus point of calorimeter reconstruction algorithm developments has been the utilisation of time information as the fifth dimension of calorimeter data.
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In this talk, recent work from the CALO5D team is presented, which studies timing in the highly granular CALICE-type calorimeter. While we study this calorimeter concept as implemented in the International Large Detector (ILD)... -
Hao Liang (Centre National de la Recherche Scientifique (FR))23/10/2025, 13:00Software (Simulation, Reconstruction, MC generators & Machine Learning)Talk
This work presents a preliminary attempt to extend the Particle Flow Algorithm (PFA) APRIL by incorporating time information into its reconstruction framework. The goal is to improve cluster energy purity and efficiency, finally the overall Particle Flow Object (PFO) reconstruction quality.
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Our main modification is to change the seeding based on timing information and the check the time... -
Graham Wilson (The University of Kansas (US)), Graham Wilson23/10/2025, 13:15Software (Simulation, Reconstruction, MC generators & Machine Learning)
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