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ATLAS TRT collaboration(Mindur, B. et al), Mitsou, V. A., & Valls Ferrer, J. A. (2016). Gas gain stabilisation in the ATLAS TRT detector. J. Instrum., 11, P04027–19pp.
Abstract: The ATLAS (one of two general purpose detectors at the LHC) Transition Radiation Tracker (TRT) is the outermost of the three tracking subsystems of the ATLAS Inner Detector. It is a large straw-based detector and contains about 350,000 electronics channels. The performance of the TRT as tracking and particularly particle identification detector strongly depends on stability of the operation parameters with most important parameter being the gas gain which must be kept constant across the detector volume. The gas gain in the straws can vary significantly with atmospheric pressure, temperature, and gas mixture composition changes. This paper presents a concept of the gas gain stabilisation in the TRT and describes in detail the Gas Gain Stabilisation System (GGSS) integrated into the Detector Control System (DCS). Operation stability of the GGSS during Run-1 is demonstrated.
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BABAR Collaboration(Aubert, B. et al), Azzolini, V., Lopez-March, N., Martinez-Vidal, F., Milanes, D. A., & Oyanguren, A. (2013). The BABAR detector: Upgrades, operation and performance. Nucl. Instrum. Methods Phys. Res. A, 729, 615–701.
Abstract: The BABAR detector operated successfully at the PEP-Il asymmetric e(+) e(-) collider at the SLAC National Accelerator Laboratory from 1999 to 2008. This report covers upgrades, operation, and performance of the collider and the detector systems, as well as the trigger, online and offline computing, and aspects of event reconstruction since the beginning of data taking.
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Babiano, V., Caballero, L., Calvo, D., Ladarescu, I., Olleros, P., & Domingo-Pardo, C. (2019). gamma-Ray position reconstruction in large monolithic LaCl3(Ce) crystals with SiPM readout. Nucl. Instrum. Methods Phys. Res. A, 931, 1–22.
Abstract: We report on the spatial response characterization of large LaCl3(Ce) monolithic crystals optically coupled to 8 x 8 pixel silicon photomultiplier (SiPM) sensors. A systematic study has been carried out for 511 keV gamma-rays using three different crystal thicknesses of 10 mm, 20 mm and 30 mm, all of them with planar geometry and a base size of 50 x 50 mm(2). In this work we investigate and compare two different approaches for the determination of the main gamma-ray hit location. On one hand, methods based on the fit of an analytical model for the scintillation light distribution provide the best results in terms of linearity and field of view, with spatial resolutions close to similar to 1 mm FWHM. On the other hand, position reconstruction techniques based on neural networks provide similar linearity and field-of-view, becoming the attainable spatial resolution similar to 3 mm FWHM. For the third space coordinate z or depth-of-interaction we have implemented an inverse linear calibration approach based on the cross-section of the measured scintillation-light distribution at a certain height. The detectors characterized in this work are intended for the development of so-called Total Energy Detectors with Compton imaging capability (i-TED), aimed at enhanced sensitivity and selectivity measurements of neutron capture cross sections via the time-of-flight (TOF) technique.
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Bach, E. et al, Bernabeu, J., Lacasta, C., Solaz, C., & Soldevila, U. (2024). Analysis of the quality assurance results from the initial part of production of the ATLAS18 ITK strip sensors. Nucl. Instrum. Methods Phys. Res. A, 1064, 169435–8pp.
Abstract: The production of strip sensors for the ATLAS Inner Tracker (ITk) started in 2021. Since then, a Quality Assurance (QA) program has been carried out continuously, by using specific test structures, in parallel to the Quality Control (QC) inspection of the sensors. The QA program consists of monitoring sensor-specific characteristics and the technological process variability, before and after the irradiation with gammas, neutrons, and protons. After two years, half of the full production volume has been reached and we present an analysis of the parameters measured as part of the QA process. The main devices used for QA purposes are miniature strip sensors, monitor diodes, and the ATLAS test chip, which contains several test structures. Such devices are tested by several sites across the collaboration depending on the type of samples (non-irradiated components or irradiated with protons, neutrons, or gammas). The parameters extracted from the tests are then uploaded to a database and analyzed by Python scripts. These parameters are mainly examined through histograms and timeevolution plots to obtain parameter distributions, production trends, and meaningful parameter-to-parameter correlations. The purpose of this analysis is to identify possible deviations in the fabrication or the sensor quality, changes in the behavior of the test equipment at different test sites, or possible variability in the irradiation processes. The conclusions extracted from the QA program have allowed test optimization, establishment of control limits for the parameters, and a better understanding of device properties and fabrication trends. In addition, any abnormal results prompt immediate feedback to a vendor.
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Balibrea-Correa, J., Lerendegui-Marco, J., Babiano-Suarez, V., Caballero, L., Calvo, D., Ladarescu, I., et al. (2021). Machine Learning aided 3D-position reconstruction in large LaCl3 crystals. Nucl. Instrum. Methods Phys. Res. A, 1001, 165249–17pp.
Abstract: We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.
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