Conveners
Parallel - WG2
- Fulvio Piccinini
- Dirk ZERWAS (DMLab and IJCLab)
- Patrizia Azzi (INFN)
Parallel - WG2
- Dirk ZERWAS (DMLab and IJCLab)
- Patrizia Azzi (INFN)
- Fulvio Piccinini
Parallel - WG2
- Dirk ZERWAS (DMLab and IJCLab)
- Fulvio Piccinini
- Patrizia Azzi (INFN)
The ILD detector concept has originally been developed for the International Linear Collider (ILC). Detailed simulations gauged against the performance of prototype components have shown that ILD in its ILC incarnation is ideally suited to pursue the physics program of a linear Higgs factory as well as of a higher energy e+e− collider. Recently, the ILD collaboration has started to investigate...
This talk gives a brief overview of the novel reconstruction tool CPID (Comprehensive Particle Identification) covering its structure and module library, usage for physics analyses and as developer, as well as first applications.
We are working on updating jet flavor tagging using Particle Transformer (ParT) from last year. We implemented b and c tagging feature and obtained a significant performance improvement from the previous software (also developed by the authors), LCFIPlus which was already reported at the previous workshop. We optimized parameters of the network and input structure to further improve the...
We present an ML-based particle flow algorithm for the CLD detector. Particle candidates are built from hits and fitted tracks, which are represented as a graph. A geometric graph neural network is then trained using object condensation loss to reconstruct a set of particle candidates from the hits and tracks. In the second step, additional heads are used to estimate the energy and momentum of...
The precision study of the Higgs boson is a primary goal for future e+e- colliders. Accurate identification of its decay products is crucial for these measurements. Utilizing full simulation of proposed detector concepts provides a realistic estimate of the expected physics performance. In this talk, I will present the first results on jet flavor tagging in full simulation for the proposed CLD...
In place of traditional cut-and-count analyses, machine learning methods can provide powerful ways to analyse physics data. In this work, we present techniques involving boosted decision trees (BDT) and deep neural networks (DNN) to increase the existing projected 95% CL limits for the HNL discovery potential at the FCC-ee, specifically as the HNLs decay into the final state of an electron and...
I will review the status and latest developments of the Sherpa event generator and its application in particular to future Higgs/EW/Top factories. The newly released version 3.0 of Sherpa provides much needed upgrades while continuing the traditional focus of the framework on higher order corrections both in QCD and EW calculations that will be crucial for a successful physics program at a...
Several key observables of the high-precision physics program at
future lepton colliders will critically depend on the knowledge of the machine absolute luminosity. The determination of the luminosity relies on the knowledge of some process which is in principle not affected by unknown physics, so that its cross section can be computed within a well-established theory, like the Standard...
In this presentation, I will provide an update on the technical benchmarking of Monte Carlo generators. I will showcase some preliminary results and discuss the roadmap for their contribution to the final ECFA report.
Beam-beam interactions constitute an important source of beam-induced background (BIB) at any $e^{+}e^{-}$ collider, with implications for the design and optimization of detectors at these machines and, ultimately, their physics reach. In this talk, we will present the status of BIB simulations for the Cool Copper Collider (C$^3$). We will report results for the simulation of incoherent...
The Cool Copper Collider (C3) accelerator concept has been found to be a compact, energy efficient accelerator design that is sufficient for studying the Higgs Boson in great detail. Studies to simultaneously optimize the scientific and environmental impact of C3’s luminosity production have been underway for some time and here we present studies of these re-optimized machine configurations in...
Large scale Monte Carlo studies are only possible with sufficient computing power. To make efficient use of these distributed resources, the DIRAC framework, and its instance for future High Energy Lepton Collider Studies, iLCDirac, offers the end users and production managers a user friendly interface. While studies for the ILC and CLIC have made use of iLCDirac for years, this presentation...
Full simulation studies are an essential tool to estimate the physics
reach for future colliders. Developing optimal reconstruction tools
for future colliders is one of the main goals for Key4hep. To properly
estimated performances, it is of particular importance to correctly
treat beam-induced backgrounds and estimate how they affect
reconstruction efficiencies and resolutions for...
We present an ML-based end-to-end algorithm for adaptive reconstruction in different FCC detectors. The algorithm takes detector hits from different subdetectors as input and reconstructs higher-level objects. For this, it exploits a geometric graph neural network, trained with object condensation, a graph segmentation technique. We apply this approach to study the performance of pattern...
Charged Long Lived Particles can produce a characteristic "kinked" track when they decay. Since the ILD-TPC can measure more than 200 hits along a particle's trajectory, it is a very powerful tool to detect such kinked tracks. In this study, based on full detector simulation, a new improved kink finding method was developed for the TPC. The potential to constrain different BSM scenarios will...
In this work we will present first results on the comparison of strange-quark jet tagging for full-detector and Delphes fast simulation using the SiD detector concept. Strange tagging plays a crucial role in the complete exploration of the second-generation Yukawa couplings and in probing new physics frontiers with the strange quark, inaccessible at the LHC. At future electron-positron (e+e-)...
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 supplemented...
