Explore our coming soon modular short courses across data acquisition, software, and data science.
Understand the creative risks of using AI in scientific thinking and research workflows.
Apply real-time classification with ML to improve trigger decision making at the LHC.
Explore our previous modular short courses across data acquisition, software, and data science.
This work package focuses on an enhancement of the already ambitious upgrade of the ATLAS experiment’s trigger and data acquisition system for the High Luminosity Phase of the LHC (HL-LHC) scheduled to start in 2029. Novel approaches to trigger event selection will extend the ATLAS physics potential, state-of-the-art Machine Learning techniques.
This work package aims to rethink the CMS data acquisition system allowing the CMS physics program to operate over all the collisions produced by the LHC. This is achieved through the High-Level Trigger (HLT) Real-time Reconstruction Revolution (R³) and a novel L1-trigger scouting stream.
Researchers in NextGen are making use of neural networks optimisation, quantum-inspired algorithms, high-performance computing, and field-programmable gate array (FPGA) techniques to improve the theoretical modelling and optimise their tools and instruments for the search of ultra rare events.