SOURCE-LINKED INTELLIGENCE
INnovative TRiggEr techniques for beyond the standard model PhysIcs Discovery at the LHC
igger architectures and technologies not considered in the plans of the collaboration and that could not be explored otherwise. To this end, I will use a multidisciplinary approach involving advanced Machine Learning techniques and top-of-the-line ultra-fast processing platforms to propose an innovative solution that will improve the capabilities of future trigger systems. The foreseen studies might be the only way in which LLPs can be discovered at the HL-LHC. Any manifestation of such particles will revolutionise the field of High Energy Physics and help to answer several fundamental questions regarding the energy scale and nature of the BSM physics. Beside progressing in the frontiers of science, the designed techniques can be of great use for industries requiring real-time processing of large data-volumes to extract features. Large Hadron Collider, High-Luminosity Large Hadron Collider, Trigger, Field Programmable Gate Arrays, Adaptative Compute Acceleration Platforms, Long-Lived Particles, Muon, Graph Neural-Network, Machine Learning, Real-time processing
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 1499375
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T02:21:08.944Z. This is not the publication date.