1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Machine learning for the anomaly detection and characterization of the 24 GeV/c proton beam at CERN IRRAD Facility

THPM110
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Jaroslaw Szumega (European Organization for Nuclear Research)

Description

The accurate assessment of beam quality is the most important aspect in the irradiation facilities operation such as IRRAD at CERN. The Beam Profile Monitor (BPM) sensor system developed for the high-intensity proton beam at IRRAD features minimal particle interaction, improved radiation hardness and higher sensitivity and sampling rate than previous systems. It provides a wealth of high-quality BPM data not available earlier, enabling the development of data processing more advanced than before. To take advantage already today of this upgraded BPM system’s features, we propose innovative Machine Learning (ML) techniques to adapt and improve upon existing DAQ technology.
This paper details the application study of (1) autoencoder architectures to perform the automatic pattern recognition and anomaly detection of proton beam profiles, and (2) deep learning techniques to predict relevant beam parameters. We applied this approach to a new dataset (made publicly available) of BPM data taken during the recent runs of IRRAD; our preliminary results demonstrate good performance in comparison to existing methods. This work is a first step towards the "intelligent" irradiation facilities.

Funding Agency

European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101057511 (EURO-LABS)

Region represented Europe
Paper preparation format LaTeX

Author

Jaroslaw Szumega (European Organization for Nuclear Research)

Co-authors

Lamine bougueroua (École FRançaise d'Électronique et d'Informatique) Blerina Gkotse (MINES Paris) Pierre Jouvelot (MINES ParisTech) Federico Ravotti (European Organization for Nuclear Research)

Presentation materials

There are no materials yet.