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Olleros, P., Caballero, L., Domingo-Pardo, C., Babiano, V., Ladarescu, I., Calvo, D., et al. (2018). On the performance of large monolithic LaCl3(Ce) crystals coupled to pixelated silicon photosensors. J. Instrum., 13, P03014–17pp.
Abstract: We investigate the performance of large area radiation detectors, with high energy-and spatial-resolution, intended for the development of a Total Energy Detector with gamma-ray imaging capability, so-called i-TED. This new development aims for an enhancement in detection sensitivity in time-of-flight neutron capture measurements, versus the commonly used C6D6 liquid scintillation total-energy detectors. In this work, we study in detail the impact of the readout photosensor on the energy response of large area (50 x 50 mm(2)) monolithic LaCl3(Ce) crystals, in particular when replacing a conventional mono-cathode photomultiplier tube by an 8 x 8 pixelated silicon photomultiplier. Using the largest commercially available monolithic SiPM array (25 cm(2)), with a pixel size of 6 x 6 mm(2), we have measured an average energy resolution of 3.92% FWHM at 662 keV for crystal thick-nesses of 10, 20 and 30 mm. The results are confronted with detailed Monte Carlo (MC) calculations, where optical processes and properties have been included for the reliable tracking of the scintillation photons. After the experimental validation of the MC model, we use our MC code to explore the impact of a smaller photosensor segmentation on the energy resolution. Our optical MC simulations predict only a marginal deterioration of the spectroscopic performance for pixels of 3 x 3 mm(2).
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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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NEXT Collaboration(Monrabal, F. et al), Laing, A., Alvarez, V., Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., et al. (2018). The NEXT White (NEW) detector. J. Instrum., 13, P12010–38pp.
Abstract: Conceived to host 5 kg of xenon at a pressure of 15 bar in the fiducial volume, the NEXT-White apparatus is currently the largest high pressure xenon gas TPC using electroluminescent amplification in the world. It is also a 1:2 scale model of the NEXT-100 detector for Xe-136 beta beta 0 nu decay searches, scheduled to start operations in 2019. Both detectors measure the energy of the event using a plane of photomultipliers located behind a transparent cathode. They can also reconstruct the trajectories of charged tracks in the dense gas of the TPC with the help of a plane of silicon photomultipliers located behind the anode. A sophisticated gas system, common to both detectors, allows the high gas purity needed to guarantee a long electron lifetime. NEXT-White has been operating since October 2016 at the Laboratorio Subterraneo de Canfranc (LSC), in Spain. This paper describes the detector and associated infrastructures, as well as the main aspects of its initial operation.
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Poley, L. et al, Bernabeu, J., Civera, J. V., Lacasta, C., Leon, P., Platero, A., et al. (2020). The ABC130 barrel module prototyping programme for the ATLAS strip tracker. J. Instrum., 15(9), P09004–78pp.
Abstract: For the Phase-II Upgrade of the ATLAS Detector [1], its Inner Detector, consisting of silicon pixel, silicon strip and transition radiation sub-detectors, will be replaced with an all new 100% silicon tracker, composed of a pixel tracker at inner radii and a strip tracker at outer radii. The future ATLAS strip tracker will include 11,000 silicon sensor modules in the central region (barrel) and 7,000 modules in the forward region (end-caps), which are foreseen to be constructed over a period of 3.5 years. The construction of each module consists of a series of assembly and quality control steps, which were engineered to be identical for all production sites. In order to develop the tooling and procedures for assembly and testing of these modules, two series of major prototyping programs were conducted: an early program using readout chips designed using a 250 nm fabrication process (ABCN-250) [2, 3] and a subsequent program using a follow-up chip set made using 130 nm processing (ABC130 and HCC130 chips). This second generation of readout chips was used for an extensive prototyping program that produced around 100 barrel-type modules and contributed significantly to the development of the final module layout. This paper gives an overview of the components used in ABC130 barrel modules, their assembly procedure and findings resulting from their tests.
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Poley, L. et al, Lacasta, C., & Soldevila, U. (2016). Characterisation of strip silicon detectors for the ATLAS Phase-II Upgrade with a micro-focused X-ray beam. J. Instrum., 11, P07023–12pp.
Abstract: The planned HL-LHC (High Luminosity LHC) in 2025 is being designed to maximise the physics potential through a sizable increase in the luminosity up to 6.10(34) cm(-2) s(-1). A consequence of this increased luminosity is the expected radiation damage at 3000 fb(-1) after ten years of operation, requiring the tracking detectors to withstand fluences to over 1.10(16) 1 MeV n(eq)/cm(2) . In order to cope with the consequent increased readout rates, a complete re-design of the current ATLAS Inner Detector (ID) is being developed as the Inner Tracker (ITk). Two proposed detectors for the ATLAS strip tracker region of the ITk were characterized at the Diamond Light Source with a 3 μm FWHM 15 keV micro focused X-ray beam. The devices under test were a 320 μm thick silicon stereo (Barrel) ATLAS12 strip mini sensor wire bonded to a 130 nm CMOS binary readout chip (ABC130) and a 320 μm thick full size radial (end-cap) strip sensor – utilizing bi-metal readout layers – wire bonded to 250 nm CMOS binary readout chips (ABCN-25). A resolution better than the inter strip pitch of the 74.5 μm strips was achieved for both detectors. The effect of the p-stop diffusion layers between strips was investigated in detail for the wire bond pad regions. Inter strip charge collection measurements indicate that the effective width of the strip on the silicon sensors is determined by p-stop regions between the strips rather than the strip pitch.
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