An initial measurement of the lifetime of the positive muon to a precision of 16 parts per million (ppm) has been performed with the FAST1 detector at the Paul Scherrer Institute. The result is ...τμ=2.197083(32)(15)μs, where the first error is statistical and the second is systematic. The muon lifetime determines the Fermi constant, GF=1.166352(9)×10−5GeV−2 (8 ppm).
Design and performance of the FAST detector Casella, C.; Barczyk, A.; Barone, G. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
02/2013, Letnik:
700
Journal Article
Recenzirano
The Fibre Active Scintillator Target (FAST) experiment at the Paul Scherrer Institute (PSI) is designed for a high-precision measurement of the μ+ lifetime, in the order of a few parts per million. ...This paper describes the design, construction and performance of the FAST detector and its readout electronics, trigger and data acquisition system.
Commonly used sums-of-squares-based error or deviation statistics—like the standard deviation, the standard error, the coefficient of variation, and the root-mean-square error—often are misleading ...indicators of average error or variability. Sums-of-squares-based statistics are functions of at least two dissimilar patterns that occur within data. Both the mean of a set of error or deviation magnitudes (the average of their absolute values) and their variability influence the value of a sum-of-squares-based error measure, which confounds clear assessment of its meaning. Interpretation problems arise, according to Paul Mielke, because sums-of-squares-based statistics do not satisfy the triangle inequality. We illustrate the difficulties in interpreting and comparing these statistics using hypothetical data, and recommend the use of alternate statistics that are based on sums of error or deviation magnitudes.
Plant‐extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large‐scale ...computations of the soil‐water balance. Empirical evidence, however, suggests that this assumption is incorrect. In this paper, we estimate the global distribution of the plant‐extractable water capacity of soil.
A representative soil profile, characterized by horizon (layer) particle size data and thickness, was created for each soil unit mapped by FAO (Food and Agriculture Organization of the United Nations)/Unesco. Soil organic matter was estimated empirically from climate data. Plant rooting depths and ground coverages were obtained from a vegetation characteristic data set. At each 0.5°×0.5° grid cell where vegetation is present, unit available water capacity (cm water per cm soil) was estimated from the sand, clay, and organic content of each profile horizon, and integrated over horizon thickness. Summation of the integrated values over the lesser of profile depth and root depth produced an estimate of the plant‐extractable water capacity of soil.
The global average of the estimated plant‐extractable water capacities of soil is 8ċ6cm (Greenland, Antarctica and bare soil areas excluded). Estimates are less than 5, 10 and 15 cm—over approximately 30, 60, and 89 per cent of the area, respectively. Estimates reflect the combined effects of soil texture, soil organic content, and plant root depth or profile depth. The most influential and uncertain parameter is the depth over which the plant‐ extractable water capacity of soil is computed, which is usually limited by root depth. Soil texture exerts a lesser, but still substantial, influence. Organic content, except where concentrations are very high, has relatively little effect.
We are developing a PET insert for existing MRI equipment to be used in clinical PET/MR studies of the human brain. The proposed scanner is based on annihilation gamma detection with monolithic ...blocks of cerium-doped lutetium yttrium orthosilicate (LYSO:Ce) coupled to magnetically-compatible avalanche photodiodes (APD) matrices. The light distribution generated on the LYSO:Ce block provides the impinging position of the 511 keV photons by means of a positioning algorithm. Several positioning methods, from the simplest Anger Logic to more sophisticate supervised-learning Neural Networks (NN), can be implemented to extract the incidence position of gammas directly from the APD signals. Finally, an optimal method based on a two-step Feed-Forward Neural Network has been selected. It allows us to reach a resolution at detector level of 2 mm, and acquire images of point sources using a first BrainPET prototype consisting of two monolithic blocks working in coincidence. Neural networks provide a straightforward positioning of the acquired data once they have been trained, however the training process is usually time-consuming. In order to obtain an efficient positioning method for the complete scanner it was necessary to find a training procedure that reduces the data acquisition and processing time without introducing a noticeable degradation of the spatial resolution. A grouping process and posterior selection of the training data have been done regarding the similitude of the light distribution of events which have one common incident coordinate (transversal or longitudinal). By doing this, the amount of training data can be reduced to about 5% of the initial number with a degradation of spatial resolution lower than 10%.
The second level trigger system of FAST Martínez, G.; Barcyzk, A.; Berdugo, J. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
10/2009, Letnik:
609, Številka:
2
Journal Article
Recenzirano
The Fibre Active Scintillator Target (FAST) experiment is a novel imaging particle detector currently operating in a high-intensity π
+ beam at the Paul Scherrer Institute (PSI), Villigen, ...Switzerland. The detector is designed to perform a high precision measurement of the μ
+ lifetime, in order to determine the Fermi constant,
G
f, to 1
ppm precision. A dedicated second level (LV2) hardware trigger system has been developed for the experiment. It performs an online analysis of the π/μ decay chain by identifying the stopping position of each beam particle and detecting the subsequent appearance of the muon. The LV2 trigger then records the muon stop pixel and selectively triggers the Time-to-Digital Converters (TDCs) in the vicinity. A detailed description of the trigger system is presented in this paper.
We are developing a positron emission tomography (PET) insert based on avalanche photodiode (APD) arrays and monolithic LYSO:Ce scintillators for human brain functional studies to be used inside a ...clinical magnetic resonance imaging (MRI) equipment. In a previous work 1, we demonstrated the performance of our detectors by implementing an experimental setup consisting of two monolithic blocks working in coincidence, which were read out by the first version of an application-specific integrated circuit (ASIC), VATA240, followed by external coincidence and digitalization modules. This preliminary demonstrator showed good spatial resolution at detector level on the order of 2.2 mm full-width at half-maximum (FWHM) and good imaging qualities, which achieved reconstructed images of super(22)Na point sources with spatial resolutions of 2.1 mm FWHM. Nevertheless, we detected image distortions and compressions due to the non-linearities close to the edge of the crystals and the simplicity of that demonstrator with the absence of neighbor blocks 1. In this work we have implemented a larger scale PET demonstrator, which is based on the new updated ASIC (VATA241) 2 and is formed by two sectors of four monolithic detector blocks placed face-to-face. This new prototype demonstrator has been built for validating the data readout architecture, the coincidence processing implemented in a Xilinx Virtex 5 field programmable gate array (FPGA), as well as the continuous neural networks (NN) training method required to determine the points of entrance over the surface of our monolithic detector blocks.
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