A superfluid He3 detector for direct dark matter search

MACHe3 (MAtrix of Cells of superfluid He3) is a project of a new detector for direct Dark Matter Search. The idea is to use superfluid He3 as a sensitive medium. The existing device, the superfluid He3 cell, will be briefly introduced. Then a description of the MACHe3 project will be presented, in particular the background rejection and the neutralino event rate that may be achieved with such a device.Comment: 6 pages, 3 figures, Proceedings of the 3rd International Workshop on the Identification of Dark Matter (York, UK, 09/18/2000-09/22/2000

A project of a new detector for direct Dark Matter search: MACHe3

MACHe3 (MAtrix of Cells of superfluid He3) is a project of a new detector for direct Dark Matter (DM) search. A cell of superfluid He3 has been developed and the idea of using a large number of such cells in a high granularity detector is proposed.This contribution presents, after a brief description of the superfluid He3 cell, the simulation of the response of different matrix configurations allowing to define an optimum design as a function of the number of cells and the volume of each cell. The exclusion plot and the predicted interaction cross-section for the neutralino as a photino are presented.Comment: 8 pages, 7 figures, Proceedings of Dark Matter 2000 (Marina Del Rey, Los Angeles, USA, 02/23/2000-02/25/2000

Influence of the track geometry variability on the train behavior

International audienceThis paper is devoted to the development of a stochastic modeling of the track geometry and its identiication with experimental measurements. This modeleing, which has to integrate the statistical and spatial variabilities and dependencies , is a keyu issue when using simulation for conception, maintenance or certification purposes

Statistical inverse problems for non-Gaussian vector valued random fields with a set of experimental realizations

International audienceThe railway track irregularities, which is a four dimensions vector-valued random field, are the main source of excitation of the train. At first, using a revisited Karhunen-Loève expansion, the considered random field is approximated by its truncated projection on a particularly well adapted orthogonal basis. Then, the distribution of the random vector that gathers the projection coefficients of the random field on this spatial basis is characterized using a polynomial chaos expansion. The dimension of this random vector being very high (around five hundred), advanced identification techniques are introduced to allow performing relevant convergence analysis and identification. Based on the stochastic modeling of the non- Gaussian non-stationary vector-valued track geometry random field, realistic track geometries, which are representative of the experimental measurements and representative of the whole railway network, can be generated. These tracks can then be introduced as an input of any railway software to characterize the stochastic behavior of any normalized train

Karhunen-Loève based sensitivity analysis

International audienceThe identification of the most dangerous combinations of excitations that a non-linear mechanical system can be confronted to is not an easy task. Indeed, in such cases, the link between the maximal values of the inputs and of the outputs is not direct, as the system can be more sensitive to a problematic succession of excitations of low amplitudes than to high amplitudes for each kind of excitations. This work presents therefore an innovative method to identify the combined shapes of excitations that are the most correlated to problematic responses of the studied mechanical system

Modeling the track geometry variability

International audienceAt its building, the theoretical new railway line is supposed to be made of perfect straight lines and curves. This track geometry is however gradually damaged and regularly subjected to maintenance operations. The analysis of these track irregularities is a key issue as the dynamic behaviour of the trains is mainly induced by the track geometry. In this context, this work is devoted to the development of a stochastic modeling of the track geometry and its identification with experimental measurements. Based on a spatial and statistical decomposition, this model allows the spatial and statistical variability and dependency of the track geometry to be taken into account. Moreover, it allows the generation of realistic track geometries that are representative of a whole railway network. These tracks can be used in any deterministic railway dynamic software to characterize the dynamic behavior of the train

Type-II/III DCT/DST algorithms with reduced number of arithmetic operations

We present algorithms for the discrete cosine transform (DCT) and discrete sine transform (DST), of types II and III, that achieve a lower count of real multiplications and additions than previously published algorithms, without sacrificing numerical accuracy. Asymptotically, the operation count is reduced from ~ 2N log_2 N to ~ (17/9) N log_2 N for a power-of-two transform size N. Furthermore, we show that a further N multiplications may be saved by a certain rescaling of the inputs or outputs, generalizing a well-known technique for N=8 by Arai et al. These results are derived by considering the DCT to be a special case of a DFT of length 4N, with certain symmetries, and then pruning redundant operations from a recent improved fast Fourier transform algorithm (based on a recursive rescaling of the conjugate-pair split radix algorithm). The improved algorithms for DCT-III, DST-II, and DST-III follow immediately from the improved count for the DCT-II.Comment: 9 page

Type-IV DCT, DST, and MDCT algorithms with reduced numbers of arithmetic operations

We present algorithms for the type-IV discrete cosine transform (DCT-IV) and discrete sine transform (DST-IV), as well as for the modified discrete cosine transform (MDCT) and its inverse, that achieve a lower count of real multiplications and additions than previously published algorithms, without sacrificing numerical accuracy. Asymptotically, the operation count is reduced from ~2NlogN to ~(17/9)NlogN for a power-of-two transform size N, and the exact count is strictly lowered for all N > 4. These results are derived by considering the DCT to be a special case of a DFT of length 8N, with certain symmetries, and then pruning redundant operations from a recent improved fast Fourier transform algorithm (based on a recursive rescaling of the conjugate-pair split radix algorithm). The improved algorithms for DST-IV and MDCT follow immediately from the improved count for the DCT-IV.Comment: 11 page