30 research outputs found
New vertebrate assemblage from marginal-marine and land Lower Keuper strata (Ladinian, Middle Triassic) of Miedary, Silesia, SW Poland
Vertebrate remains, mostly nothosaurid vertebrae and long bones, archosaur partially preserved long bones and teeth (probably rauisuchid remains), fish teeth and scales, are described from the Lower Keuper Miedary Beds (Ladinian, Middle Triassic) of Miedary, Silesia, SW Poland. The analyzed vertebrate fossils were collected from three lithologically different types of deposits. The first assemblage, which contains nothosaurid and fish remains, occurs in yellowish dolomites, where poorly preseved invertebrate macrofossils were also found (marine bivalves and brachiopods). In grey yellowish, sometimes greenish or red clays, rare isolated bones and teeth of large archosaur were found. In sandstone intercalations partially preserved and highly disarticulated fish fossils were identified. In all assemblages osteological remains are generally very well preserved. This new interesting vertebrate bones association from the upper part of the Middle Triassic marginal-marine strata of Poland has been correlated with age equivalents from other regions of Europe
Neural Network Parameterizations of Electromagnetic Nucleon Form Factors
The electromagnetic nucleon form-factors data are studied with artificial
feed forward neural networks. As a result the unbiased model-independent
form-factor parametrizations are evaluated together with uncertainties. The
Bayesian approach for the neural networks is adapted for chi2 error-like
function and applied to the data analysis. The sequence of the feed forward
neural networks with one hidden layer of units is considered. The given neural
network represents a particular form-factor parametrization. The so-called
evidence (the measure of how much the data favor given statistical model) is
computed with the Bayesian framework and it is used to determine the best form
factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the
prior assumptions is added. The manuscript contains 4 new figures and 2 new
tables (32 pages, 15 figures, 2 tables
Collins and Sivers asymmetries in muonproduction of pions and kaons off transversely polarised protons
Measurements of the Collins and Sivers asymmetries for charged pions and charged and neutral kaons produced in semi-inclusive deep-inelastic scattering of high energy muons off transversely polarised protons are presented. The results were obtained using all the available COMPASS proton data, which were taken in the years 2007 and 2010. The Collins asymmetries exhibit in the valence region a non-zero signal for pions and there are hints of non-zero signal also for kaons. The Sivers asymmetries are found to be positive for positive pions and kaons and compatible with zero otherwise. © 2015
First muon-neutrino disappearance study with an off-axis beam
We report a measurement of muon-neutrino disappearance in the T2K experiment. The 295-km muon-neutrino beam from Tokai to Kamioka is the first implementation of the off-axis technique in a long-baseline neutrino oscillation experiment
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Liquid argon time projection chamber detector technology provides high
spatial and calorimetric resolutions on the charged particles traversing liquid
argon. As a result, the technology has been used in a number of recent neutrino
experiments, and is the technology of choice for the Deep Underground Neutrino
Experiment (DUNE). In order to perform high precision measurements of neutrinos
in the detector, final state particles need to be effectively identified, and
their energy accurately reconstructed. This article proposes an algorithm based
on a convolutional neural network to perform the classification of energy
deposits and reconstructed particles as track-like or arising from
electromagnetic cascades. Results from testing the algorithm on data from
ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network
identifies track- and shower-like particles, as well as Michel electrons, with
high efficiency. The performance of the algorithm is consistent between data
and simulation.Comment: 31 pages, 15 figure