Optimal softening for force calculations in collisionless N-body simulations

In N-body simulations the force calculated between particles representing a given mass distribution is usually softened, to diminish the effect of graininess. In this paper we study the effect of such a smoothing, with the aim of finding an optimal value of the softening parameter. As already shown by Merritt (1996), for too small a softening the estimates of the forces will be too noisy, while for too large a softening the force estimates are systematically misrepresented. In between there is an optimal softening, for which the forces in the configuration approach best the true forces. The value of this optimal softening depends both on the mass distribution and on the number of particles used to represent it. For higher number of particles the optimal softening is smaller. More concentrated mass distributions necessitate smaller softening, but the softened forces are never as good an approximation of the true forces as for not centrally concentrated configurations. We give good estimates of the optimal softening for homogeneous spheres, Plummer spheres, and Dehnen spheres. We also give a rough estimate of this quantity for other mass distributions, based on the harmonic mean distance to the $k$th neighbour ($k$ = 1, .., 12), the mean being taken over all particles in the configuration. Comparing homogeneous Ferrers ellipsoids of different shapes we show that the axial ratios do not influence the value of the optimal softening. Finally we compare two different types of softening, a spline softening (Hernquist & Katz 1989) and a generalisation of the standard Plummer softening to higher values of the exponent. We find that the spline softening fares roughly as well as the higher powers of the power-law softening and both give a better representation of the forces than the standard Plummer softening.Comment: 16 pages Latex, 19 figures, accepted for publication in MNRAS, corrected typos, minor changes mainly in sec.

Numerical radiative transfer with state-of-the-art iterative methods made easy

This article presents an on-line tool (rttools.irap.omp.eu) and its accompanying software ressources for the numerical solution of basic radiation transfer out of local thermodynamic equilibrium (LTE). State-of-the-art stationary iterative methods such as Accelerated $\Lambda$-Iteration and Gauss-Seidel schemes, using a short characteristics-based formal solver are used. We also comment on typical numerical experiments associated to the basic non-LTE radiation problem. These ressources are intended for the largest use and benefit, in support to more classical radiation transfer lectures usually given at the Master level.Comment: 8 pages, 5 figures, accepted for Eur. J. Phys. - see also (and use!) http://rttools.irap.omp.e

Superconductivity-Induced Anderson Localisation

We have studied the effect of a random superconducting order parameter on the localization of quasi-particles, by numerical finite size scaling of the Bogoliubov-de Gennes tight-binding Hamiltonian. Anderson localization is obtained in d=2 and a mobility edge where the states localize is observed in d=3. The critical behavior and localization exponent are universal within error bars both for real and complex random order parameter. Experimentally these results imply a suppression of the electronic contribution to thermal transport from states above the bulk energy gap.Comment: 4 pages, revtex file, 3 postscript figure

Determination of failure limits for sterilizable solid rocket motor

A structural evaluation to establish probable failure limits and a series of environmental tests involving temperature cycling, sustained acceleration, and vibration were conducted on an 18-inch diameter solid rocket motor. Despite the fact that thermal, acceleration and vibration loads representing a severe overtest of conventional environmental requirements were imposed on the sterilizable motor, no structural failure of the grain or flexible support system was detected. The following significant conclusions are considered justified. It is concluded that: (1) the flexible grain retention system, which permitted heat sterilization at 275 F on the test motor, can readily be adopted to meet the environmental requirements of an operational motor design, and (2) if further substantiation of structural integrity is desired, the motor used is considered acceptable for static firing

Forming disk galaxies in wet major mergers. I. Three fiducial examples

Using three fiducial Nbody+SPH simulations, we follow the merging of two disk galaxies with a hot gaseous halo component each, and examine whether the merger remnant can be a spiral galaxy. The stellar progenitor disks are destroyed by violent relaxation during the merging and most of their stars form a classical bulge, while the remaining form a thick disk and its bar. A new stellar disk forms subsequently and gradually in the remnant from the gas accreted mainly from the halo. It is vertically thin and well extended in its equatorial plane. A bar starts forming before the disk is fully in place, contrary to what is assumed in idealised simulations of isolated bar-forming galaxies. It has morphological features such as ansae and boxy/peanut bulges. Stars of different ages populate different parts of the box/peanut. A disky pseudobulge forms also, so that by the end of the simulation, all three types of bulges coexist. The oldest stars are found in the classical bulge, followed by those of the thick disk, then by those in the thin disk. The youngest stars are in the spiral arms and the disky pseudobulge. The disk surface density profiles are of type II (exponential with downbending), and the circular velocity curves are flat and show that the disks are submaximum in these examples: two clearly so and one near-borderline between maximum and submaximum. On average, only roughly between 10 and 20% of the stellar mass is in the classical bulge of the final models, i.e. much less than in previous simulations.Comment: 17 pages, 8 figures, accepted for publication in ApJ. V2: replaced Figure 4 with correct versio

Demonstration of a sterilizable solid rocket motor system

A solid propellant rocket motor containing 60.9 Kg (134-lb) of propellant was successfully static fired after being subjected to eight heat sterilization cycles (three 54-hour cycles plus five 40-hour cycles) at 125 C (257 F). The test motor, a modified SVM-3 chamber, incorporated a flexible grain retention system of EPR rubber to relieve thermal shrinkage stresses. The propellant used in the motor was ANB-3438, and 84 wt% solids system (18 wt% aluminum) containing 66 wt% stabilized ammonium perchlorate oxidizer and a saturated hydroxylterminated polybutadiene binder. Bonding of the propellant to the EPR insulation (GenGard V-4030) was provided by the use of SD-886, an epoxy urethane restriction

One step multiderivative methods for first order ordinary differential equations

A family of one-step multiderivative methods based on Padé approximants to the exponential function is developed. The methods are extrapolated and analysed for use in PECE mode. Error constants and stability intervals are calculated and the combinations compared with well known linear multi-step combinations and combinations using high accuracy Newton-Cotes quadrature formulas as correctors. w926020

High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to instable and non convergent methods due to inappropriate computational frameworks. We hereby propose a stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results: We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, which combines iterative optimization of logistic regression and sparse PLS to ensure convergence and stability. Our results are confirmed on synthetic and experimental data. In particular we show how crucial convergence and stability can be when cross-validation is involved for calibration purposes. Using gene expression data we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method on the prediction of cell-types based on single-cell expression data. Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3 figures, 10 table