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NEXT Collaboration(Alvarez, V. et al), Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., Gil, A., et al. (2013). Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment. J. Instrum., 8, P04002–25pp.
Abstract: NEXT-DEMO is a large-scale prototype of the NEXT-100 detector, an electroluminescent time projection chamber that will search for the neutrinoless double beta decay of Xe-136 using 100-150 kg of enriched xenon gas. NEXT-DEMO was built to prove the expected performance of NEXT-100, namely, energy resolution better than 1% FWHM at 2.5MeV and event topological reconstruction. In this paper we describe the prototype and its initial results. A resolution of 1.75% FWHM at 511 keV (which extrapolates to 0.8% FWHM at 2.5 MeV) was obtained at 10 bar pressure using a gamma-ray calibration source. Also, a basic study of the event topology along the longitudinal coordinate is presented, proving that it is possible to identify the distinct dE/dx of electron tracks in high-pressure xenon using an electroluminescence TPC.
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NEXT Collaboration(Alvarez, V. et al), Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., Gil, A., et al. (2013). Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array. J. Instrum., 8, P09011–20pp.
Abstract: NEXT-DEMO is a high-pressure xenon gas TPC which acts as a technological test-bed and demonstrator for the NEXT-100 neutrinoless double beta decay experiment. In its current configuration the apparatus fully implements the NEXT-100 design concept. This is an asymmetric TPC, with an energy plane made of photomultipliers and a tracking plane made of silicon photomultipliers (SiPM) coated with TPB. The detector in this new configuration has been used to reconstruct the characteristic signature of electrons in dense gas, demonstrating the ability to identify the MIP and “blob” regions. Moreover, the SiPM tracking plane allows for the definition of a large fiducial region in which an excellent energy resolution of 1.82% FWHM at 511 keV has been measured (a value which extrapolates to 0.83% at the xenon Q(beta beta)).
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NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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Ortiz Arciniega, J. L., Carrio, F., & Valero, A. (2019). FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions. J. Instrum., 14, P09002–13pp.
Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.
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Perez-Calatayud, J., Ballester, F., Tedgren, C., DeWerd, L. A., Papagiannis, P., Rivard, M. J., et al. (2022). GEC-ESTRO ACROP recommendations on calibration and traceability of HE HDR-PDR photon-emitting brachytherapy sources at the hospital level. Radiother. Oncol., 176, 108–117.
Abstract: The vast majority of radiotherapy departments in Europe using brachytherapy (BT) perform temporary implants of high-or pulsed-dose rate (HDR-PDR) sources with photon energies higher than 50 keV. Such techniques are successfully applied to diverse pathologies and clinical scenarios. These recommen-dations are the result of Working Package 21 (WP-21) initiated within the BRAchytherapy PHYsics Quality Assurance System (BRAPHYQS) GEC-ESTRO working group with a focus on HDR-PDR source cal-ibration. They provide guidance on the calibration of such sources, including practical aspects and issues not specifically accounted for in well-accepted societal recommendations, complementing the BRAPHYQS WP-18 Report dedicated to low energy BT photon emitting sources (seeds). The aim of this report is to provide a European-wide standard in HDR-PDR BT source calibration at the hospital level to maintain high quality patient treatments.
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