Neural network approach for efficient calculation of the current correction value in the femtoampere range for a new generation of ionizing radiation monitors at CERN

• Published in: Radiation physics and chemistry (Oxford, England : 1993) Vol. 188; p. 109539
• Main Authors: Szumega, Jarosław M., Boukabache, Hamza, Perrin, Daniel
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2021; Elsevier BV
• Subjects: Algorithms; Artificial neural networks; CERN; Femtoampere current measurement; Field programmable gate arrays; FPGA; Ionizing radiation; Ionizing radiation monitoring; Machine learning; Monitoring; Monitors; Neural networks; Nuclear safety; Particle beams; Radiation detectors; Radiation measurement; Radiation measurement performance; Radiation protection; Real time operation; Safety critical; System-on-chip; Vibration measurement; Femtoampere current measurement; FPGA; Ionizing radiation monitoring; Artificial neural networks; Machine learning; System-on-chip; Radiation measurement performance

The European Organization for Nuclear Research (CERN) conducts experiments that involve colliding beams of particles either together or into stationary targets. During these interactions, stray radiation may be generated. The ionizing radiation detectors installed at several locations close to the b...