A5023212828- Distributed and Parallel Computing Systems
- Research Data Management Practices
- Particle physics theoretical and experimental studies
- Advanced Data Storage Technologies
- Scientific Computing and Data Management
- Particle Detector Development and Performance
- Computational Physics and Python Applications
- High-Energy Particle Collisions Research
European Organization for Nuclear Research
2020-2023
Abstract Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus application when developing code, better research productivity pleads high-level programming language. A popular approach consists combining Python, used interface, C++, intensive part code. more convenient efficient would...
Abstract Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss case of detector simulation. The need simulated events, expected future LHC experiments and their High Luminosity upgrades, is increasing dramatically requires new fast simulation solutions. Here present updated results on development 3DGAN, one first examples using three-dimensional convolutional Generative Adversarial Networks to simulate high granularity...
The infrastructure behind home.cern and 1000 other Drupal websites serves more than 15,000 unique visitors daily. To best serve the site owners, a small engineering team needs development speed to adapt their evolving operational velocity troubleshoot emerging problems rapidly. We designed new Web Frameworks platform by extending Kubernetes replace ageing physical reduce dependency on homebrew components. is modular, built around standard components thus less complex operate. Some...