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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Identification of charm jets at LHCb. J. Instrum., 17(2), P02028–23pp.
Abstract: The identification of charm jets is achieved at LHCb for data collected in 2015-2018 using a method based on the properties of displaced vertices reconstructed and matched with jets. The performance of this method is determined using a dijet calibration dataset recorded by the LHCb detector and selected such that the jets are unbiased in quantities used in the tagging algorithm. The charm-tagging efficiency is reported as a function of the transverse momentum of the jet. The measured efficiencies are compared to those obtained from simulation and found to be in good agreement.
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Vijande, J., Tedgren, A. C., Ballester, F., Baltas, D., Papagiannis, P., Rivard, M. J., et al. (2021). Source strength determination in iridium-192 and cobalt-60 brachytherapy: A European survey on the level of agreement between clinical measurements and manufacturer certificates. Phys. Imag. Radiat. Oncol., 19, 108–111.
Abstract: Background and purpose: Brachytherapy treatment outcomes depend on the accuracy of the delivered dose distribution, which is proportional to the reference air-kerma rate (RAKR). Current societal recommendations require the medical physicist to compare the measured RAKR values to the manufacturer source calibration certificate. The purpose of this work was to report agreement observed in current clinical practice in the European Union. Materials and methods: A European survey was performed for high- and pulsed-dose-rate (HDR and PDR) highenergy sources (Ir-192 and Co-60), to quantify observed RAKR differences. Medical physicists at eighteen hospitals from eight European countries were contacted, providing 1,032 data points from 2001 to 2020. Results: Over the survey period, 77% of the Ir-192 measurements used a well chamber instead of the older Krieger phantom method. Mean differences with the manufacturer calibration certificate were 0.01% +/- 1.15% for Ir-192 and -0.1% +/- 1.3% for Co-60. Over 95% of RAKR measurements in the clinic were within 3% of the manufacturer calibration certificate. Conclusions: This study showed that the agreement level was generally better than that reflected in prior societal recommendations positing 5%. Future recommendations on high-energy HDR and PDR source calibrations in the clinic may consider tightened agreements levels.
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Calatayud-Jordan, J., Candela-Juan, C., Palma, J. D., Pujades-Claumarchirant, M. C., Soriano, A., Gracia-Ochoa, M., et al. (2021). Influence of the simultaneous calibration of multiple ring dosimeters on the individual absorbed dose. J. Radiol. Prot., 41(2), 384–397.
Abstract: Ring dosimeters for personal dosimetry are calibrated in accredited laboratories following ISO 4037-3 guidelines. The simultaneous irradiation of multiple dosimeters would save time, but has to be carefully studied, since the scattering conditions could change and influence the absorbed dose in nearby dosimeters. Monte Carlo simulations using PENELOPE-2014 were performed to explore the need to increase the uncertainty of H-p (0.07) in the simultaneous irradiation of three and five DXT-RAD 707H-2 (Thermo Scientific) ring dosimeters with beam qualities: N-30, N-80 and N-300. Results show that the absorbed dose in each dosimeter is compatible with each of the others and with the reference simulation (a single dosimeter), with a coverage probability of 95% (k = 2). Comparison with experimental data yielded consistent results with the same coverage probability. Therefore, five ring dosimeters can be simultaneously irradiated with beam qualities ranging, at least, between N-30 and N-300 with a negligible impact on the uncertainty of H-p (0.07).
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LHCb Collaboration(Aaij, R. et al), Garcia Martin, L. M., Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., et al. (2019). Measurement of the electron reconstruction efficiency at LHCb. J. Instrum., 14, P11023–20pp.
Abstract: The single electron track-reconstruction efficiency is calibrated using a sample corresponding to 1.3 fb(-1) of pp collision data recorded with the LHCb detector in 2017. This measurement exploits B+ -> J/psi (e(+)e(-))K+ decays, where one of the electrons is fully reconstructed and paired with the kaon, while the other electron is reconstructed using only the information of the vertex detector. Despite this partial reconstruction, kinematic and geometric constraints allow the B meson mass to be reconstructed and the signal to be well separated from backgrounds. This in turn allows the electron reconstruction efficiency to be measured by matching the partial track segment found in the vertex detector to tracks found by LHCb's regular reconstruction algorithms. The agreement between data and simulation is evaluated, and corrections are derived for simulated electrons in bins of kinematics. These correction factors allow LHCb to measure branching fractions involving single electrons with a systematic uncertainty below 1%.
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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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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Electron and photon energy calibration with the ATLAS detector using 2015-2016 LHC proton-proton collision data. J. Instrum., 14, P03017–60pp.
Abstract: This paper presents the electron and photon energy calibration obtained with the ATLAS detector using about 36 fb(-1) of LHC proton-proton collision data recorded at root s = 13 TeV in 2015 and 2016. The different calibration steps applied to the data and the optimization of the reconstruction of electron and photon energies are discussed. The absolute energy scale is set using a large sample of Z boson decays into electron-positron pairs. The systematic uncertainty in the energy scale calibration varies between 0.03% to 0.2% in most of the detector acceptance for electrons with transverse momentum close to 45 GeV. For electrons with transverse momentum of 10 GeV the typical uncertainty is 0.3% to 0.8% and it varies between 0.25% and 1% for photons with transverse momentum around 60 GeV. Validations of the energy calibration with J/psi -> e(+)e(-) decays and radiative Z boson decays are also presented.
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Albiol, F., Corbi, A., & Albiol, A. (2017). Evaluation of modern camera calibration techniques for conventional diagnostic X-ray imaging settings. Radiol. Phys. Technol., 10(1), 68–81.
Abstract: We explore three different alternatives for obtaining intrinsic and extrinsic parameters in conventional diagnostic X-ray frameworks: the direct linear transform (DLT), the Zhang method, and the Tsai approach. We analyze and describe the computational, operational, and mathematical background differences for these algorithms when they are applied to ordinary radiograph acquisition. For our study, we developed an initial 3D calibration frame with tin cross-shaped fiducials at specific locations. The three studied methods enable the derivation of projection matrices from 3D to 2D point correlations. We propose a set of metrics to compare the efficiency of each technique. One of these metrics consists of the calculation of the detector pixel density, which can be also included as part of the quality control sequence in general X-ray settings. The results show a clear superiority of the DLT approach, both in accuracy and operational suitability. We paid special attention to the Zhang calibration method. Although this technique has been extensively implemented in the field of computer vision, it has rarely been tested in depth in common radiograph production scenarios. Zhang's approach can operate on much simpler and more affordable 2D calibration frames, which were also tested in our research. We experimentally confirm that even three or four plane-image correspondences achieve accurate focal lengths.
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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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ATLAS Tile Calorimeter System(Abdallah, J. et al), Ferrer, A., Fiorini, L., Hernandez Jimenez, Y., Higon-Rodriguez, E., Ruiz-Martinez, A., et al. (2016). The Laser calibration of the ATLAS Tile Calorimeter during the LHC run 1. J. Instrum., 11, T10005–29pp.
Abstract: This article describes the Laser calibration system of the ATLAS hadronic Tile Calorimeter that has been used during the run 1 of the LHC. First, the stability of the system associated readout electronics is studied. It is found to be stable with variations smaller than 0.6 %. Then, the method developed to compute the calibration constants, to correct for the variations of the gain of the calorimeter photomultipliers, is described. These constants were determined with a statistical uncertainty of 0.3 % and a systematic uncertainty of 0.2 % for the central part of the calorimeter and 0.5 % for the end-caps. Finally, the detection and correction of timing mis-configuration of the Tile Calorimeter using the Laser system are also presented.
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Albiol, F., Corbi, A., & Albiol, A. (2016). Geometrical Calibration of X-Ray Imaging With RGB Cameras for 3D Reconstruction. IEEE Trans. Med. Imaging, 35(8), 1952–1961.
Abstract: We present a methodology to recover the geometrical calibration of conventional X-ray settings with the help of an ordinary video camera and visible fiducials that are present in the scene. After calibration, equivalent points of interest can be easily identifiable with the help of the epipolar geometry. The same procedure also allows the measurement of real anatomic lengths and angles and obtains accurate 3D locations from image points. Our approach completely eliminates the need for X-ray-opaque reference marks (and necessary supporting frames) which can sometimes be invasive for the patient, occlude the radiographic picture, and end up projected outside the imaging sensor area in oblique protocols. Two possible frameworks are envisioned: a spatially shifting X-ray anode around the patient/object and a moving patient that moves/rotates while the imaging system remains fixed. As a proof of concept, experiences with a device under test (DUT), an anthropomorphic phantom and a real brachytherapy session have been carried out. The results show that it is possible to identify common points with a proper level of accuracy and retrieve three-dimensional locations, lengths and shapes with a millimetric level of precision. The presented approach is simple and compatible with both current and legacy widespread diagnostic X-ray imaging deployments and it can represent a good and inexpensive alternative to other radiological modalities like CT.
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