|
Soderstrom, P. A. et al, Agramunt, J., Egea, J., Gadea, A., & Huyuk, T. (2019). Neutron detection and gamma-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537. Nucl. Instrum. Methods Phys. Res. A, 916, 238–245.
Abstract: In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and gamma rays. Special emphasis is put on the application of artificial neural networks. The results show a systematically higher gamma-ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of gamma rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neutrons could be identified in BC-501A using artificial neural networks down to a recoil proton energy of 800 keV compared to a recoil deuteron energy of 1200 keV for BC-537. We conclude that using artificial neural networks it is possible to obtain the same gamma-ray rejection quality from both BC-501A and BC-537 for neutrons above a low-energy threshold. This threshold is, however, lower for BC-501A, which is important for nuclear structure spectroscopy experiments of rare reaction channels where low-energy interactions dominates.
|
|
|
Blanco, A., Belver, D., Cabanelas, P., Diaz, J., Fonte, P., Garzon, J. A., et al. (2012). RPC HADES-TOF wall cosmic ray test performance. Nucl. Instrum. Methods Phys. Res. A, 661, S114–S117.
Abstract: In this work we present results concerning the cosmic ray test, prior to the final installation and commissioning of the new Resistive Plate Chamber (RPC) Time of Flight (TOF) wall for the High-Acceptance DiElectron Spectrometer (HADES) at GSI. The TOF wall is composed of six equal sectors, each one constituted by 186 individual 4-gaps glass-aluminium shielded RPC cells distributed in six columns and 31 rows in two partially overlapping layers, covering an area of 1.26 m(2). All sectors were tested with the final Front End Electronic (FEE) and Data AcQuisition system (DAQ) together with Low Voltage (LV) and High Voltage (HV) systems. Results confirm a very uniform average system time resolution of 77 ps sigma together with an average multi-hit time resolution of 83 ps. Crosstalk levels below 1% (in average), moderate timing tails along with an average longitudinal position resolution of 8.4 mm sigma are also confirmed.
|
|
|
Azevedo, C. D. R., Baeza, A., Chauveau, E., Corbacho, J. A., Diaz, J., Domange, J., et al. (2020). Simulation results of a real-time in water tritium monitor. Nucl. Instrum. Methods Phys. Res. A, 982, 164555–7pp.
Abstract: In this work we present simulation results for a modular tritium in-water real-time monitor. The system allows for scalability in order to achieve the required sensitivity. The modules are composed by 340 uncladed scintillating fibers immersed in water and 2 photosensors in coincidence for light readout. Light yield and Birks' coefficient uncertainties for low energy beta particles is discussed. A study of the detection efficiency according to the fiber length is presented. Discussion on the system requirements and background mitigation for a device with sensitivity of 100 Bq/L, required to comply with the European directive 2013/51/Euratom, is presented. Due to the low energetic beta emission from tritium a detection efficiency close to 3.3% was calculated for a single 2 mm round fiber.
|
|
|
Guadilla, V. et al, Algora, A., Tain, J. L., Agramunt, J., Jordan, D., Monserrate, M., et al. (2017). Characterization of a cylindrical plastic beta-detector with Monte Carlo simulations of optical photons. Nucl. Instrum. Methods Phys. Res. A, 854, 134–138.
Abstract: In this work we report on the Monte Carlo study performed to understand and reproduce experimental measurements of a new plastic beta-detector with cylindrical geometry. Since energy deposition simulations differ from the experimental measurements for such a geometry, we show how the simulation of production and transport of optical photons does allow one to obtain the shapes of the experimental spectra. Moreover, taking into account the computational effort associated with this kind of simulation, we develop a method to convert the simulations of energy deposited into light collected, depending only on the interaction point in the detector. This method represents a useful solution when extensive simulations have to be done, as in the case of the calculation of the response function of the spectrometer in a total absorption gamma-ray spectroscopy analysis.
|
|
|
Carles, M., Lerche, C. W., Sanchez, F., Mora, F., & Benlloch, J. M. (2011). Position correction with depth of interaction information for a small animal PET system. Nucl. Instrum. Methods Phys. Res. A, 648, S176–S180.
Abstract: In this work we study the effects on the spatial resolution when depth of interaction (001) information is included in the parameterization of the line of response (LOR) for a small animal positron emission tomography (PET) system. One of the most important degrading factors for PET is the parallax error introduced in systems that do not provide DOI information of the recorded gamma-rays. Our group has designed a simple and inexpensive method for DOI determination in continuous scintillation crystals. This method is based, on one hand, in the correlation between the scintillation light distribution width in monolithic crystals and the DOI, and, on the other hand, on a small modification of the widely applied charge dividing circuits (CDR). In this work we present a new system calibration that includes the DOI information, and also the development of the correction equations that relates the LOR without and with DOI information. We report the results obtained for different measurements along the transaxial field of view (FOV) and the image quality enhancement achieved specially at the edge of the FOV.
|
|