0000000000650170
AUTHOR
A. Boujrad
Digital Front-End Electronics for the Neutron Detector NEDA
19th Real Time Conference (RT) -- MAY 26-30, 2014 -- Nara, JAPAN
A New Front-End High-Resolution Sampling Board for the New-Generation Electronics of EXOGAM2 and NEDA Detectors
19th Real Time Conference (RT) -- MAY 26-30, 2014 -- Nara, JAPAN WOS: 000356458000028 This paper presents the final design and results of the FADC Mezzanine for the EXOGAM (EXOtic GAMma array spectrometer) and NEDA (Neutron Detector Array) detectors. The measurements performed include those of studying the effective number of bits, the energy resolution using HP-Ge detectors, as well as timing histograms and discrimination performance. Finally, the conclusion shows how a common digitizing device has been integrated in the experimental environment of two very different detectors which combine both low-noise acquisition and fast sampling rates. Not only the integration fulfilled the expected …
Performance of the Advanced GAmma Tracking Array at GANIL
The performance of the Advanced GAmma Tracking Array (AGATA) at GANIL is discussed, on the basis of the analysis of source and in-beam data taken with up to 30 segmented crystals. Data processing is described in detail. The performance of individual detectors are shown. The efficiency of the individual detectors as well as the efficiency after $\gamma$-ray tracking are discussed. Recent developments of $\gamma$-ray tracking are also presented. The experimentally achieved peak-to-total is compared with simulations showing the impact of back-scattered $\gamma$ rays on the peak-to-total in a $\gamma$-ray tracking array. An estimate of the achieved position resolution using the Doppler broadeni…
Artificial neural networks for neutron/ γ discrimination in the neutron detectors of NEDA
Three different Artificial Neural Network architectures have been applied to perform neutron/? discrimination in NEDA based on waveform and time-of-flight information. Using the coincident ?-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms. Narodowe Centrum Nau…