Searching for BSM neutrino interactions in dark matter detectors

Neutrino interactions beyond the Standard Model (BSM) are theoretically well motivated and have an important impact on the future precision measurement of neutrino oscillation. In this work, we study the sensitivity of a multi-ton-scale liquid Xenon dark matter detector equipped with an intense radiative neutrino source to various BSM neutrino-electron interactions. We consider the conventional Non-Standard Interactions (NSIs), other more generalized four-fermion interactions including scalar and tensor forms, and light-boson mediated interactions. The work shows that with realistic experimental setups, one can achieve unprecedented sensitivity to these BSM neutrino-electron interactions.Comment: fig. 7 added, matches the published versio

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption

A Novel Method for the Fault Diagnosis of a Planetary Gearbox based on Residual Sidebands from Modulation Signal Bispectrum Analysis

This paper presents a novel method for the fault diagnosis of planetary gearboxes based on an accurate estimation of residual sidebands using a modulation signal bispectrum (MSB). The residual sideband resulting from the out-phase superposition of vibration waves from asymmetrical multiple meshing sources are much less influenced by gear errors than that of the in-phase sidebands. Therefore, with the accurate estimation by MSB they can produce accurate and consistent diagnosis, which are evaluated by both simulating and experimental studies. However, the commonly used in-phase sidebands have high amplitudes but include gear error effects, consequently leading to poor diagnostic results

Market imperfections, government imperfections and policy mixes : policy innovations in Singapore

10.1007/s11077-013-9186-xPolicy Sciences473305-32

Quantum dynamics of an Ising spin-chain in a random transverse field

We consider an Ising spin-chain in a random transverse magnetic field and compute the zero temperature wave vector and frequency dependent dynamic structure factor numerically by using Jordan-Wigner transformation. Two types of distributions of magnetic fields are introduced. For a rectangular distribution, a dispersing branch is observed, and disorder tends to broaden the dispersion peak and close the excitation gap. For a binary distribution, a non-dispersing branch at almost zero energy is recovered. We discuss the relationship of our work to the neutron scattering measurement in $\mathrm{LiHoF_4}$.Comment: 4 pages and 6 eps figures; minor clarifications were made; the text was shortened to add an additional figur

GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN

Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue OED for nonlinear systems from a Bayesian perspective, with the goal of choosing experiments that are optimal for parameter inference. Our objective in this context is the expected information gain in model parameters, which in general can only be estimated using Monte Carlo methods. Maximizing this objective thus becomes a stochastic optimization problem. This paper develops gradient-based stochastic optimization methods for the design of experiments on a continuous parameter space. Given a Monte Carlo estimator of expected information gain, we use infinitesimal perturbation analysis to derive gradients of this estimator.We are then able to formulate two gradient-based stochastic optimization approaches: (i) Robbins-Monro stochastic approximation, and (ii) sample average approximation combined with a deterministic quasi-Newton method. A polynomial chaos approximation of the forward model accelerates objective and gradient evaluations in both cases.We discuss the implementation of these optimization methods, then conduct an empirical comparison of their performance. To demonstrate design in a nonlinear setting with partial differential equation forward models, we use the problem of sensor placement for source inversion. Numerical results yield useful guidelines on the choice of algorithm and sample sizes, assess the impact of estimator bias, and quantify tradeoffs of computational cost versus solution quality and robustness.United States. Air Force Office of Scientific Research (Computational Mathematics Program)National Science Foundation (U.S.) (Award ECCS-1128147