Определение эффективности нейтронного детектора из пластического сцинтиллятора o100?200 мм

Рассчитывается и экспериментально проверяется эффективность детектора. к нейтронам сверхвысоких (десятки и сотни МэВ) энергий

A Bisognano-Wichmann-like Theorem in a Certain Case of a Non Bifurcate Event Horizon related to an Extreme Reissner-Nordstr\"om Black Hole

Thermal Wightman functions of a massless scalar field are studied within the framework of a ``near horizon'' static background model of an extremal R-N black hole. This model is built up by using global Carter-like coordinates over an infinite set of Bertotti-Robinson submanifolds glued together. The analytical extendibility beyond the horizon is imposed as constraints on (thermal) Wightman's functions defined on a Bertotti-Robinson sub manifold. It turns out that only the Bertotti-Robinson vacuum state, i.e. $T=0$, satisfies the above requirement. Furthermore the extension of this state onto the whole manifold is proved to coincide exactly with the vacuum state in the global Carter-like coordinates. Hence a theorem similar to Bisognano-Wichmann theorem for the Minkowski space-time in terms of Wightman functions holds with vanishing ``Unruh-Rindler temperature''. Furtermore, the Carter-like vacuum restricted to a Bertotti-Robinson region, resulting a pure state there, has vanishing entropy despite of the presence of event horizons. Some comments on the real extreme R-N black hole are given

Improved limits on nuebar emission from mu+ decay

We investigated mu+ decays at rest produced at the ISIS beam stop target. Lepton flavor (LF) conservation has been tested by searching for \nueb via the detection reaction p(\nueb,e+)n. No \nueb signal from LF violating mu+ decays was identified. We extract upper limits of the branching ratio for the LF violating decay mu+ -> e+ \nueb \nu compared to the Standard Model (SM) mu+ -> e+ nue numub decay: BR < 0.9(1.7)x10^{-3} (90%CL) depending on the spectral distribution of \nueb characterized by the Michel parameter rho=0.75 (0.0). These results improve earlier limits by one order of magnitude and restrict extensions of the SM in which \nueb emission from mu+ decay is allowed with considerable strength. The decay \mupdeb as source for the \nueb signal observed in the LSND experiment can be excluded.Comment: 10 pages, including 1 figure, 1 tabl

The KATRIN Pre-Spectrometer at reduced Filter Energy

The KArlsruhe TRItium Neutrino experiment, KATRIN, will determine the mass of the electron neutrino with a sensitivity of 0.2 eV (90% C.L.) via a measurement of the beta-spectrum of gaseous tritium near its endpoint of E_0 =18.57 keV. An ultra-low background of about b = 10 mHz is among the requirements to reach this sensitivity. In the KATRIN main beam-line two spectrometers of MAC-E filter type are used in a tandem configuration. This setup, however, produces a Penning trap which could lead to increased background. We have performed test measurements showing that the filter energy of the pre-spectrometer can be reduced by several keV in order to diminish this trap. These measurements were analyzed with the help of a complex computer simulation, modeling multiple electron reflections both from the detector and the photoelectric electron source used in our test setup.Comment: 22 pages, 12 figure

Initial source and site characterization studies for the U.C. Santa Barbara campus

The University of California Campus-Laboratory Collaboration (CLC) project is an integrated 3 year effort involving Lawrence Livermore National Laboratory (LLNL) and four UC campuses - Los Angeles (UCLA), Riverside (UCR), Santa Barbara (UCSB), and San Diego (UCSD) - plus additional collaborators at San Diego State University (SDSU), at Los Alamos National Laboratory and in industry. The primary purpose of the project is to estimate potential ground motions from large earthquakes and to predict site-specific ground motions for one critical structure on each campus. This project thus combines the disciplines of geology, seismology, geodesy, soil dynamics, and earthquake engineering into a fully integrated approach. Once completed, the CLC project will provide a template to evaluate other buildings at each of the four UC campuses, as well as provide a methodology for evaluating seismic hazards at other critical sites in California, including other UC locations at risk from large earthquakes. Another important objective of the CLC project is the education of students and other professional in the application of this integrated, multidisciplinary, state-of-the-art approach to the assessment of earthquake hazard. For each campus targeted by the CLC project, the seismic hazard study will consist of four phases: Phase I - Initial source and site characterization, Phase II - Drilling, logging, seismic monitoring, and laboratory dynamic soil testing, Phase III - Modeling of predicted site-specific earthquake ground motions, and Phase IV - Calculations of 3D building response. This report cover Phase I for the UCSB campus and incudes results up through March 1997

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem