Warm dark matter in the galaxies:theoretical and observational progresses. Highlights and conclusions of the chalonge meudon workshop 2011

Warm Dark Matter (WDM) research is progressing fast, the subject is new and WDM essentially works, naturally reproducing the astronomical observations over all scales: small (galactic) and large (cosmological) scales (LambdaWDM). Evidence that Cold Dark Matter (LambdaCDM) and its proposed tailored cures do not work at small scales is staggering. Fedor Bezrukov, Pier-Stefano Corasaniti, Hector J. de Vega, Stefano Ettori, Frederic Hessmann, Ayuki Kamada, Marco Lombardi, Alexander Merle, Christian Moni Bidin, Angelo Nucciotti on behalf of the MARE collaboration, Sinziana Paduroiu, Henri Plana, Norma Sanchez, Patrick Valageas, Shun Zhou present here their highlights of the Workshop. LambdaWDM simulations with keV particles remarkably reproduce the observations, small and large structures and velocity functions. Cored DM halos and WDM are clearly determined from theory and astronomical observations, they naturally produce the observed structures at all scales. keV sterile neutrinos are the leading candidates, they naturally appear extensions of the standard model of particle physics. Astrophysical constraints including Lyman alpha bounds put its mass in the range 1< m <13 keV. Predictions for EUCLID and PLANCK have been presented. MARE and an adapted KATRIN experiment could detect a keV sterile neutrino. It will be a fantastic discovery to detect dark matter in a beta decay. A formidable WDM work to perform is ahead of us, these highlights point out some relevant research directions to put the effort. Photos of the Workshop are included (Abridged).Comment: 48 pages, 24 figure

Towards the Chalonge 16th Paris Cosmology Colloquium 2012: Highlights and Conclusions of the Chalonge 15th Paris Cosmology Colloquium 2011

The Chalonge 15th Paris Cosmology Colloquium 2011 was held on 20-22 July in the historic Paris Observatory's Perrault building, in the Chalonge School spirit combining real cosmological/astrophysical data and hard theory predictive approach connected to them in the Warm Dark Matter Standard Model of the Universe: News and reviews from Herschel, QUIET, Atacama Cosmology Telescope (ACT), South Pole Telescole (SPT), Planck, PIXIE, the JWST, UFFO, KATRIN and MARE experiments; astrophysics, particle and nuclear physics warm dark matter (DM) searches and galactic observations, related theory and simulations, with the aim of synthesis, progress and clarification. Philippe Andre, Peter Biermann, Pasquale Blasi, Daniel Boyanovsky, Carlo Burigana, Hector de Vega, Joanna Dunkley, Gerry Gilmore, Alexander Kashlinsky, Alan Kogut, Anthony Lasenby, John Mather, Norma Sanchez, Alexei Smirnov, Sylvaine Turck-Chieze present here their highlights of the Colloquium. Ayuki Kamada and Sinziana Paduroiu present here their poster highlights. LambdaWDM (Warm Dark Matter) is progressing impressively over LambdaCDM whose galactic scale crisis and decline are staggering. The International School Daniel Chalonge issued an statement of strong support to the James Webb Space Telescope (JSWT). The Daniel Chalonge Medal 2011 was awarded to John C. Mather, Science PI of the JWST. Summary and conclusions are presented by H. J. de Vega, M. C. Falvella and N. G. Sanchez. Overall, LambdaWDM and keV scale DM particles deserve dedicated astronomical and laboratory experimental searches, theoretical work and simulations. KATRIN experiment in the future could perhaps adapt its set-up to look to keV scale sterile neutrinos. It will be a a fantastic discovery to detect dark matter in a beta decay. Photos of the Colloquium are included. (Abridged)Comment: 65 pages, 21 figure

Effect of Reconfiguration Characteristics on Manufacturing System Capacity Selection

The increasing frequency of new product introductions force today's companies to continuously upgrade their production capacities. The frequent revision of production capacities and the capacity loss during this period increase the importance of ramp up duration in evaluating capacity investments. This thesis aims to explore how a firm should optimally allocate its capacity investments among different manufacturing systems considering the capacity evolution in ramp up period. The proposed models in this thesis address a production facility making products that has a specific life cycle pattern. In this study, the duration of reconfiguration period for reconfigurable manufacturing system (RMS) is modeled as a function of the amount of capacity change. Through a sensitivity analysis, the impact of reconfiguration on the selection of manufacturing systems has been analyzed with respect to different product life cycle patterns. Through a mixed integer programming model, a various ramp up time patterns are taken into account and a more suitable reconfiguration type for a manufacturer in terms of system layout and response range is analyzed. Finally, the response time of a system is considered in the context of a supply chain network to improve the supply chain responsiveness. The appropriate response speed is selected through a decision tree analysis and based on the expected cost of the supply chain. The results show a faster response speed is a better choice as the failure probability of main supply node increases and/or the recovery of the main supply node decreases

Sparse Estimation of High-Dimensional Covariance Matrices.

This thesis develops methodology and asymptotic analysis for sparse estimators of the covariance matrix and the inverse covariance (concentration) matrix in high-dimensional settings. We propose estimators that are invariant to the ordering of the variables and estimators that exploit variable ordering. For the estimators that are invariant to the ordering of the variables, estimation is based on both lasso-type penalized normal likelihood and a new proposed class of generalized thresholding operators which combine thresholding with shrinkage applied to the entries of the sample covariance matrix. For both approaches we obtain explicit convergence rates in matrix norms that show the trade-off between the sparsity of the true model, dimension, and the sample size. In addition, we show that the generalized thresholding approach estimates true zeros as zeros with probability tending to 1, and is sign consistent for non-zero elements. We also derive a fast iterative algorithm for computing the penalized likelihood estimator. To exploit a natural ordering of the variables to estimate the covariance matrix, we propose a new regression interpretation of the Cholesky factor of the covariance matrix, as opposed to the well known regression interpretation of the Cholesky factor of the inverse covariance, which leads to a new class of regularized covariance estimators suitable for high-dimensional problems. We also establish theoretical connections between banding Cholesky factors of the covariance matrix and its inverse and constrained maximum likelihood estimation under the banding constraint. These covariance estimators are compared to other estimators on simulated data and on real data examples from gene microarray experiments and remote sensing. Lastly, we propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. An efficient optimization algorithm and a fast approximation are developed and we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75832/1/ajrothma_1.pd