An application of adaptive fault-tolerant control to nano-spacecraft

Since nano-spacecraft are small, low cost and do not undergo the same rigor of testing as conventional spacecraft, they have a greater risk of failure. In this paper we address the problem of attitude control of a nano-spacecraft that experiences different types of faults. Based on the traditional quaternion feedback control method, an adaptive fault-tolerant control method is developed, which can ensure that the control system still operates when the actuator fault happens. This paper derives the fault-tolerant control logic under both actuator gain fault mode and actuator deviation fault mode. Taking the parameters of the UKube-1 in the simulation model, a comparison between a traditional spacecraft control method and the adaptive fault-tolerant control method in the presence of a fault is undertaken. It is shown that the proposed controller copes with faults and is able to complete an effective attitude control manoeuver in the presence of a fault

Narrow Trans-TeV Higgs Bosons and H→hhH\to hh Decays: Two LHC Search Paths for a Hidden Sector Higgs Boson

We consider the addition of a condensing singlet scalar field to the Standard Model. Such a scenario may be motivated by any number of theoretical ideas, including the common result in string-inspired model building of singlet scalar fields charged under some hidden sector gauge symmetry. For concreteness, we specify an example model of this type, and consider the relevant constraints on Higgs physics, such as triviality, perturbative unitarity and precision electroweak analysis. We then show that there are two unique features of the phenomenology that present opportunities for discovery at the Large Hadron Collider (LHC). First, it is possible to identify and discover a narrow trans-TeV Higgs boson in this scenario -- a mass scale that is well above the scale at which it is meaningful to discuss a SM Higgs boson. Second, the decays of the heavier scalar state into the lighter Higgs bosons can proceed at a high rate and may be the first discovery mode in the Higgs sector.Comment: 21 pages, 5 figure

Cosmic Archaeology with Gravitational Waves from Cosmic Strings

Cosmic strings are generic cosmological predictions of many extensions of the Standard Model of particle physics, such as a $U(1)^\prime$ symmetry breaking phase transition in the early universe or remnants of superstring theory. Unlike other topological defects, cosmic strings can reach a scaling regime that maintains a small fixed fraction of the total energy density of the universe from a very early epoch until today. If present, they will oscillate and generate gravitational waves with a frequency spectrum that imprints the dominant sources of total cosmic energy density throughout the history of the universe. We demonstrate that current and future gravitational wave detectors, such as LIGO and LISA, could be capable of measuring the frequency spectrum of gravitational waves from cosmic strings and discerning the energy composition of the universe at times well before primordial nucleosynthesis and the cosmic microwave background where standard cosmology has yet to be tested. This work establishes a benchmark case that gravitational waves may provide an unprecedented, powerful tool for probing the evolutionary history of the very early universe.Comment: 6 pages, 3 figure

Gaussianizing the non-Gaussian lensing convergence field I: the performance of the Gaussianization

Motivated by recent works of Neyrinck et al. 2009 and Scherrer et al. 2010, we proposed a Gaussianization transform to Gaussianize the non-Gaussian lensing convergence field $\kappa$. It performs a local monotonic transformation $\kappa\rightarrow y$ pixel by pixel to make the unsmoothed one-point probability distribution function of the new variable $y$ Gaussian. We tested whether the whole $y$ field is Gaussian against N-body simulations. (1) We found that the proposed Gaussianization suppresses the non-Gaussianity by orders of magnitude, in measures of the skewness, the kurtosis, the 5th- and 6th-order cumulants of the $y$ field smoothed over various angular scales relative to that of the corresponding smoothed $\kappa$ field. The residual non-Gaussianities are often consistent with zero within the statistical errors. (2) The Gaussianization significantly suppresses the bispectrum. Furthermore, the residual scatters around zero, depending on the configuration in the Fourier space. (3) The Gaussianization works with even better performance for the 2D fields of the matter density projected over \sim 300 \mpch distance interval centered at $z\in(0,2)$, which can be reconstructed from the weak lensing tomography. (4) We identified imperfectness and complexities of the proposed Gaussianization. We noticed weak residual non-Gaussianity in the $y$ field. We verified the widely used logarithmic transformation as a good approximation to the Gaussianization transformation. However, we also found noticeable deviations.Comment: 13 pages, 15 figures, accepted by PR

Likelihood-informed dimension reduction for nonlinear inverse problems

The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively low-dimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the "likelihood-informed subspace" (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao-Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations

Optimal low-rank approximations of Bayesian linear inverse problems

In the Bayesian approach to inverse problems, data are often informative, relative to the prior, only on a low-dimensional subspace of the parameter space. Significant computational savings can be achieved by using this subspace to characterize and approximate the posterior distribution of the parameters. We first investigate approximation of the posterior covariance matrix as a low-rank update of the prior covariance matrix. We prove optimality of a particular update, based on the leading eigendirections of the matrix pencil defined by the Hessian of the negative log-likelihood and the prior precision, for a broad class of loss functions. This class includes the F\"{o}rstner metric for symmetric positive definite matrices, as well as the Kullback-Leibler divergence and the Hellinger distance between the associated distributions. We also propose two fast approximations of the posterior mean and prove their optimality with respect to a weighted Bayes risk under squared-error loss. These approximations are deployed in an offline-online manner, where a more costly but data-independent offline calculation is followed by fast online evaluations. As a result, these approximations are particularly useful when repeated posterior mean evaluations are required for multiple data sets. We demonstrate our theoretical results with several numerical examples, including high-dimensional X-ray tomography and an inverse heat conduction problem. In both of these examples, the intrinsic low-dimensional structure of the inference problem can be exploited while producing results that are essentially indistinguishable from solutions computed in the full space

Clustering Scatter Plots Using Data Depth Measures.

Clustering is rapidly becoming a powerful data mining technique, and has been broadly applied to many domains such as bioinformatics and text mining. However, the existing methods can only deal with a data matrix of scalars. In this paper, we introduce a hierarchical clustering procedure that can handle a data matrix of scatter plots. To more accurately reflect the nature of data, we introduce a dissimilarity statistic based on "data depth" to measure the discrepancy between two bivariate distributions without oversimplifying the nature of the underlying pattern. We then combine hypothesis testing with hierarchical clustering to simultaneously cluster the rows and columns of the data matrix of scatter plots. We also propose novel painting metrics and construct heat maps to allow visualization of the clusters. We demonstrate the utility and power of our new clustering method through simulation studies and application to a microbe-host-interaction study

Cosmic Strings from Supersymmetric Flat Directions

Flat directions are a generic feature of the scalar potential in supersymmetric gauge field theories. They can arise, for example, from D-terms associated with an extra abelian gauge symmetry. Even when supersymmetry is broken softly, there often remain directions in the scalar field space along which the potential is almost flat. Upon breaking a gauge symmetry along one of these almost flat directions, cosmic strings may form. Relative to the standard cosmic string picture based on the abelian Higgs model, these flat-direction cosmic strings have the extreme Type-I properties of a thin gauge core surrounded by a much wider scalar field profile. We perform a comprehensive study of the microscopic, macroscopic, and observational characteristics of this class of strings. We find many differences from the standard string scenario, including stable higher winding mode strings, the dynamical formation of higher mode strings from lower ones, and a resultant multi-tension scaling string network in the early universe. These strings are only moderately constrained by current observations, and their gravitational wave signatures may be detectable at future gravity wave detectors. Furthermore, there is the interesting but speculative prospect that the decays of cosmic string loops in the early universe could be a source of ultra-high energy cosmic rays or non-thermal dark matter. We also compare the observational signatures of flat-direction cosmic strings with those of ordinary cosmic strings as well as (p,q) cosmic strings motivated by superstring theory.Comment: 58 pages, 16 figures, v2. accepted to PRD, added comments about baryogenesis and boosted decay products from cusp annihilatio