Search results for "DETECT"
showing 10 items of 5902 documents
LBNO-DEMO (WA105): a large demonstrator of the Liquid Argon double phase TPC
2016
LBNO-DEMO (WA105) is a large demonstrator of the double phase liquid argon TPC intended to develop and test the main elements of the GLACIER-based design for the purpose of scaling it up to the 10–50 kton size needed for Long Baseline Neutrino Oscillation studies. The crucial components of the design are: ultra-high argon purity in non-evacuable tank, long drifts, very high drift voltages, large area Micro Pattern Gas Detectors, and cold preamplifiers. The active volume of the demonstrator is 6×6×6 m3 (approximately 300t). WA105 is under construction at CERN and will be exposed to charged particle beams (0.5-20 GeV/c) in the North Area in 2018. The data will provide the necessary calibratio…
Seasonal Modulation of the $^7$Be Solar Neutrino Rate in Borexino
2017
We detected the seasonal modulation of the $^7$Be neutrino interaction rate with the Borexino detector at the Laboratori Nazionali del Gran Sasso in Italy. The period, amplitude, and phase of the observed time evolution of the signal are consistent with its solar origin, and the absence of an annual modulation is rejected at 99.99\% C.L. The data are analyzed using three methods: the sinusoidal fit, the Lomb-Scargle and the Empirical Mode Decomposition techniques, which all yield results in excellent agreement.
Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition.
2020
A pivotal tenet in modern behavioral sciences is that the study of behavior, in its most intimate structure, necessarily deals with time and, for this reason, behavioral dynamics are not intuitively perceivable and/or detectable (Eibl-Eibesfeldt, 1970). In reality, the possibility to describe a given behavior in terms of its structural/temporal features makes available new and detailed information otherwise unavailable. The aim of the present paper is to discuss the possible application of T-pattern detection and analysis, i.e., a multivariate approach specifically developed to describe the temporal structure of behavior, to the study of an important and still scantly investigated issue, na…
Preprocessing methods for nodule detection in lung CT
2005
Abstract The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images.
Separation of atomic and molecular ions by ion mobility with an RF carpet
2021
Gas-filled stopping cells are used at accelerator laboratories for the thermalization of high-energy radioactive ion beams. Common challenges of many stopping cells are a high molecular background of extracted ions and limitations of extraction efficiency due to space-charge effects. At the FRS Ion Catcher at GSI, a new technique for removal of ionized molecules prior to their extraction out of the stopping cell has been developed. This technique utilizes the RF carpet for the separation of atomic ions from molecular contaminant ions through their difference in ion mobility. Results from the successful implementation and test during an experiment with a 600~MeV/u $^{124}$Xe primary beam are…
Towards 14C-free liquid scintillator
2017
A series of measurements has been started where the 14C concentration is determined from several liquid scintillator samples. A dedicated setup has been designed and constructed with the aim of measuring concentrations smaller than 10−18. Measurements take place in two underground laboratories: in the Baksan Neutrino Observatory, Russia, and in the new Callio Lab in the Pyhäsalmi mine, Finland. Low-energy neutrino detection with a liquid scintillator requires that the intrinsic 14C concentration in the liquid is extremely low. In the Borexino CTF detector the concentration of 2 × 10−18 has been achieved being the lowest value ever measured. In principle, the older the oil or gas source that…
Measuring the 14C content in liquid scintillators
2016
We are going to perform a series of measurements where the 14C/12C ratio will be measured from several liquid scintillator samples with a dedicated setup. The setup is designed with the aim of measuring ratios smaller than 10−18. Measurements take place in two underground laboratories: in the Baksan Neutrino Observatory, Russia and in the Pyh¨asalmi mine, Finland. In Baksan the measurements started in 2015 and in Pyh¨asalmi they start in the beginning of 2015. In order to fully understand the operation of the setup and its background contributions a development of simulation packages has also been started. Low-energy neutrino detection with a liquid scintillator requires that the intrinsic …
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
2020
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino…
Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images
2023
Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho …
Node co-activations as a means of error detection : Towards fault-tolerant neural networks
2022
Context: Machine learning has proved an efficient tool, but the systems need tools to mitigate risks during runtime. One approach is fault tolerance: detecting and handling errors before they cause harm. Objective: This paper investigates whether rare co-activations – pairs of usually segregated nodes activating together – are indicative of problems in neural networks (NN). These could be used to detect concept drift and flagging untrustworthy predictions. Method: We trained four NNs. For each, we studied how often each pair of nodes activates together. In a separate test set, we counted how many rare co-activations occurred with each input, and grouped the inputs based on whether its class…