A5014918578- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Computational Physics and Python Applications
- Dark Matter and Cosmic Phenomena
- Neutrino Physics Research
- Distributed and Parallel Computing Systems
- Cosmology and Gravitation Theories
- Astrophysics and Cosmic Phenomena
- Superconducting Materials and Applications
- Medical Imaging Techniques and Applications
- Advanced Data Storage Technologies
- Radiation Detection and Scintillator Technologies
University of Geneva
2024-2025
Istanbul University
2024
Abstract In modern High Energy Physics (HEP) experiments, triggers perform the important task of selecting, in real time, data to be recorded and saved for physics analyses. As a result, trigger strategies play key role extracting relevant information from vast streams produced at facilities like Large Hadron Collider (LHC). energy luminosity collisions increase, these must upgraded maintained suit experimental needs. This whitepaper presents high-level overview reviews recent developments...
IceCube Collaboration, 2012Collaboration, , 2017)), KM3NeT (KM3NeT 2016), and Baikal-GVD (Baikal-GVD 2018) have the science goal of detecting neutrinos measuring their properties origins.Reconstruction at these experiments is concerned with classifying type event or estimating interaction.
In modern High Energy Physics (HEP) experiments, triggers perform the important task of selecting, in real time, data to be recorded and saved for physics analyses. As a result, trigger strategies play key role extracting relevant information from vast streams produced at facilities like Large Hadron Collider (LHC). energy luminosity collisions increase, these must upgraded maintained suit experimental needs. This whitepaper compiled by SMARTHEP Early Stage Researchers presents high-level...
GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks neutrino telescopes using graph neural networks (GNNs). makes it fast and easy train complex models that can provide event with state-of-the-art performance, for arbitrary detector configurations, inference times are orders of magnitude faster than traditional techniques. GNNs from flexible enough be applied data all telescopes, including future...