Marco-Hernandez, R., Bau, M., Ferrari, M., Ferrari, V., Pedersen, F., & Soby, L. (2017). A Low-Noise Charge Amplifier for the ELENA Trajectory, Orbit, and Intensity Measurement System. IEEE Trans. Nucl. Sci., 64(9), 2465–2473.
Abstract: A low-noise head amplifier has been developed for the extra low energy antiproton ring beam trajectory, orbit, and intensity measurement system at CERN. This system is based on 24 double-electrode electrostatic beam position monitors installed around the ring. A head amplifier is placed close to each beam position monitor to amplify the electrode signals and generate a difference and a sum signal. These signals are sent to the digital acquisition system, about 50 m away from the ring, where they are digitized and further processed. The beam position can be measured by dividing the difference signal by the sum signal while the sum signal gives information relative to the beam intensity. The head amplifier consists of two discrete charge preamplifiers with junction field effect transistor (JFET) inputs, a sum and a difference stage, and two cable drivers. Special attention has been paid to the amplifier printed circuit board design to minimize the parasitic capacitances and inductances at the charge amplifier stages to meet the gain and noise requirements. The measurements carried out on the head amplifier showed a gain of 40.5 and 46.5 dB for the sum and difference outputs with a bandwidth from 200 Hz to 75 MHz and an input voltage noise density lower than 400 pV/v Hz. Twenty head amplifiers have been already installed in the ring and they have been used to detect the first beam signals during the first commissioning stage in November 2016.
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Peppa, V., Thomson, R. M., Enger, S. A., Fonseca, G. P., Lee, C. N., Lucero, J. N. E., et al. (2023). A MC-based anthropomorphic test case for commissioning model-based dose calculation in interstitial breast 192-Ir HDR brachytherapy. Med. Phys., 50(7), 4675–4687.
Abstract: PurposeTo provide the first clinical test case for commissioning of Ir-192 brachytherapy model-based dose calculation algorithms (MBDCAs) according to the AAPM TG-186 report workflow. Acquisition and Validation MethodsA computational patient phantom model was generated from a clinical multi-catheter Ir-192 HDR breast brachytherapy case. Regions of interest (ROIs) were contoured and digitized on the patient CT images and the model was written to a series of DICOM CT images using MATLAB. The model was imported into two commercial treatment planning systems (TPSs) currently incorporating an MBDCA. Identical treatment plans were prepared using a generic Ir-192 HDR source and the TG-43-based algorithm of each TPS. This was followed by dose to medium in medium calculations using the MBDCA option of each TPS. Monte Carlo (MC) simulation was performed in the model using three different codes and information parsed from the treatment plan exported in DICOM radiation therapy (RT) format. Results were found to agree within statistical uncertainty and the dataset with the lowest uncertainty was assigned as the reference MC dose distribution. Data Format and Usage NotesThe dataset is available online at ,. Files include the treatment plan for each TPS in DICOM RT format, reference MC dose data in RT Dose format, as well as a guide for database users and all files necessary to repeat the MC simulations. Potential ApplicationsThe dataset facilitates the commissioning of brachytherapy MBDCAs using TPS embedded tools and establishes a methodology for the development of future clinical test cases. It is also useful to non-MBDCA adopters for intercomparing MBDCAs and exploring their benefits and limitations, as well as to brachytherapy researchers in need of a dosimetric and/or a DICOM RT information parsing benchmark. Limitations include specificity in terms of radionuclide, source model, clinical scenario, and MBDCA version used for its preparation.
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LHCb Collaboration(Aaij, R. et al), Garcia Martin, L. M., Henry, L., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., et al. (2018). A measurement of the CP asymmetry difference between Lambda(+)(C) -> pK(-)K(+) and p pi(-)pi(+) decays. J. High Energy Phys., 03(3), 182–21pp.
Abstract: The difference between the CP asymmetries in the decays Lambda(+)(C) -> pK(-)K(+) and Lambda(+)(C) -> p pi(-)pi(+) is presented. Proton-proton collision data taken at centre-of-mass energies of 7 and 8 TeV collected by the LHCb detector in 2011 and 2012 are used, corresponding to an integrated luminosity of 3 fb(-1). The Lambda(+)(C) candidates are reconstructed as part of the Lambda(0)(b) -> Lambda(+)(c)mu X- decay chain. In order to maximize the cancellation of production and detection asymmetries in the difference, the final-state kinematic distributions of the two samples are aligned by applying phase-space-dependent weights to the Lambda(+)(C) -> pK(-)K(+) sample. This alters the definition of the integrated CP asymmetry to A(CP)(wgt)(p pi(-)pi(+)). Both samples are corrected for reconstruction and selection efficiencies across the five-dimensional Lambda(+)(C) decay phase space. The difference in CP asymmetries is found to be Delta A(CP)(wgt) = A(CP)(pK(-)K(+)) – A(CP)(wgt)(p pi(-)pi(+)) = (0.30 +/- 0.91 +/- 0.61) %, where the first uncertainty is statistical and the second is systematic.
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Arganda, E., Marcano, X., Martin Lozano, V., Medina, A. D., Perez, A. D., Szewc, M., et al. (2022). A method for approximating optimal statistical significances with machine-learned likelihoods. Eur. Phys. J. C, 82(11), 993–14pp.
Abstract: Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength. Based on supervised learning techniques, it can perform well also with high-dimensional data, when traditional techniques cannot. We apply the method to a toy model first, so we can explore its potential, and then to a LHC study of new physics particles in dijet final states. Considering as the optimal statistical significance the one we would obtain if the true generative functions were known, we show that our method provides a better approximation than the usual naive counting experimental results.
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ANTARES Collaboration(Aguilar, J. A. et al), Bigongiari, C., Dornic, D., Emanuele, U., Gomez-Gonzalez, J. P., Hernandez-Rey, J. J., et al. (2012). A method for detection of muon induced electromagnetic showers with the ANTARES detector. Nucl. Instrum. Methods Phys. Res. A, 675, 56–62.
Abstract: The primary aim of ANTARES is neutrino astronomy with upward going muons created in charged current muon neutrino interactions in the detector and its surroundings. Downward going muons are background for neutrino searches. These muons are the decay products of cosmic-ray collisions in the Earth's atmosphere far above the detector. This paper presents a method to identify and count electromagnetic showers induced along atmospheric muon tracks with the ANTARES detector. The method is applied to both cosmic muon data and simulations and its applicability to the reconstruction of muon event energies is demonstrated.
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