Measurement of the mass of the τ lepton

A data-driven energy scan in the immediate vicinity of the τ pair production threshold has been performed using the Beijing Spectrometer at the Beijing Electron-Positron Collider. Approximately 5 pb^(-1) of data, distributed over 12 scan points, have been collected. A previous mass value for the τ lepton, obtained using only the eμ final state, has been published. In this paper, the final BES result on the mass measurement is presented. The analysis is based on the combined data from the ee, eμ, eh, μμ, μh, and hh final states, where h denotes a charged π or K. A maximum likelihood fit to the τ pair production cross section data yields the value m_τ=1776.96_(-0.21)-0.17^(+0.18+0.25) MeV

Direct measurement of B(D_s^+ → φX^+)

The absolute inclusive branching fraction of D_s^+→φX^+ has been measured from data collected by the BES detector at a center-of-mass energy of 4.03 GeV, corresponding to an integrated luminosity of 22.3 pb^(-1). At this energy, direct pair production e^+e^-→D_s^+D_s^- has been observed. We have selected D_s candidate events by reconstructing five hadronic decay modes D_s^+→φπ^+, K^(0*)K^+, K^0K^+, f0^(π+) and K^0K^-π^+π^+ and have searched for inclusive φ’s in the recoiling D_s^-. We observed three recoiling φ’s in the 166.4 ± 31.8 D_s candidate events, which leads to the absolute branching fraction B(D_s^+→φX^+)=(17.8(-7.2 -6.3)^(+15.1+0.6)) % and B(D_s-6.3+→φπ-6.3+)=(3.6_(-1.6 -1.3)(^_3.1+0.4) %. [S0556-2821(97)02423-5

Measurement of the mass of the τ lepton

The mass of the τ lepton has been measured at the Beijing Electron-Positron Collider using the Beijing Spectrometer. A search near threshold for e^+e^-→τ^+τ^- was performed. Candidate events were identified by requiring that one τ decay via τ→eνν¯, and the other via τ→μνν¯. The mass value, obtained from a fit to the energy dependence of the τ^+τ^- cross section, is m_τ=1776.9_(-0.5)^(+0.4)±0.2 MeV

Long-time Behavior of a Two-layer Model of Baroclinic Quasi-geostrophic Turbulence

We study a viscous two-layer quasi-geostrophic beta-plane model that is forced by imposition of a spatially uniform vertical shear in the eastward (zonal) component of the layer flows, or equivalently a spatially uniform north-south temperature gradient. We prove that the model is linearly unstable, but that non-linear solutions are bounded in time by a bound which is independent of the initial data and is determined only by the physical parameters of the model. We further prove, using arguments first presented in the study of the Kuramoto-Sivashinsky equation, the existence of an absorbing ball in appropriate function spaces, and in fact the existence of a compact finite-dimensional attractor, and provide upper bounds for the fractal and Hausdorff dimensions of the attractor. Finally, we show the existence of an inertial manifold for the dynamical system generated by the model's solution operator. Our results provide rigorous justification for observations made by Panetta based on long-time numerical integrations of the model equations

Optimizing an MPI weather forecasting model via processor virtualization

Abstract—Weather forecasting models are computationally intensive applications. These models are typically executed in parallel machines and a major obstacle for their scalability is load imbalance. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the application’s source code, have been used to address both sources. However, these techniques are inflexible and hard to use in legacy codes. In this paper, we demonstrate the effectiveness of processor virtualization for dynamically balancing the load in BRAMS, a mesoscale weather forecasting model based on MPI paral-lelization. We use the Charm++ infrastructure, with its over-decomposition and object-migration capabilities, to move sub-domains across processors during execution of the model. Pro-cessor virtualization enables better overlap between computation and communication and improved cache efficiency. Furthermore, by employing an appropriate load balancer, we achieve better processor utilization while requiring minimal changes to the model’s code. I

Landmark Detection in Orbital Images Using Salience Histograms

NASA's planetary missions have collected, and continue to collect, massive volumes of orbital imagery. The volume is such that it is difficult to manually review all of the data and determine its significance. As a result, images are indexed and searchable by location and date but generally not by their content. A new automated method analyzes images and identifies "landmarks," or visually salient features such as gullies, craters, dust devil tracks, and the like. This technique uses a statistical measure of salience derived from information theory, so it is not associated with any specific landmark type. It identifies regions that are unusual or that stand out from their surroundings, so the resulting landmarks are context-sensitive areas that can be used to recognize the same area when it is encountered again. A machine learning classifier is used to identify the type of each discovered landmark. Using a specified window size, an intensity histogram is computed for each such window within the larger image (sliding the window across the image). Next, a salience map is computed that specifies, for each pixel, the salience of the window centered at that pixel. The salience map is thresholded to identify landmark contours (polygons) using the upper quartile of salience values. Descriptive attributes are extracted for each landmark polygon: size, perimeter, mean intensity, standard deviation of intensity, and shape features derived from an ellipse fit