Speaker
Description
Irradiation facilities, infrastructures for assessing devices and material radiation-hardness, face a variety of challenges, from the management of the experiment-selection process to the monitoring of the beam quality they need. While addressing vastly different issues, the answers may be found in carefully engineered Machine Learning and Artificial Intelligence (AI) solutions.
The applications of AI models in High-Energy-Physics (HEP) data analysis are well-established, in particular with neural networks and deep-learning algorithms. We suggest that recent advances in Natural-Language-Processing techniques such as transformer architectures may be used for the experiments’ proposals assessment and the development of new attention-based monitoring and anomaly detection tools used during their execution.
We provide supporting evidence for our approach by describing 1) how we help assess HEP-related scientific proposals within the RADNEXT EU-project and 2) how we monitor and evaluate the transverse beam profile quality in real-time at the CERN IRRAD facility in the EURO-LABS EU-project.
| What of the following keywords match your abstract best? | Real-time algorithms |
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| Please tick if you are a PhD student and wish to take part to the poster prize competition! | I am a PhD student |
