Tracing the Nature of Dark Energy with Galaxy Distribution

Dynamical Dark Energy (DE) is a viable alternative to the cosmological constant. Yet, constructing tests to discriminate between Lambda and dynamical DE models is difficult because the differences are not large. In this paper we explore tests based on the galaxy mass function, the void probability function (VPF), and the number of galaxy clusters. At high z the number density of clusters shows large differences between DE models, but geometrical factors reduce the differences substantially. We find that detecting a model dependence in the cluster redshift distribution is a hard challenge. We show that the galaxy redshift distribution is potentially a more sensitive characteristics. We do so by populating dark matter halos in Nbody simulations with galaxies using well-tested Halo Occupation Distribution (HOD). We also estimate the Void Probability Function and find that, in samples with the same angular surface density of galaxies in different models, the VPF is almost model independent and cannot be used as a test for DE. Once again, geometry and cosmic evolution compensate each other. By comparing VPF's for samples with fixed galaxy mass limits, we find measurable differences.Comment: 12 pages, 11 figures, dependence on mass-luminosity relation discussed, minor changes to match the accepted version by MNRA

Dark Matter & Dark Energy from a single scalar field: CMB spectrum and matter transfer function

The dual axion model (DAM), yielding bot DM and DE form a PQ-like scalar field solving the strong CP problem, is known to allow a fair fit of CMB data. Recently, however, it was shown that its transfer function exhibits significant anomalies, causing difficulties to fit deep galaxy sample data. Here we show how DAM can be modified to agree with the latter data set. The modification follows the pattern suggested to reconcile any PQ-like approach with gravity. Modified DAM allows precise predictions which can be testable against future CMB and/or deep sample data.Comment: 15 pages, 8 figures, accepted for publication in JCA

Carbon-nanotube geometries as optimal configurations

The fine geometry of carbon nanotubes is investigated from the viewpoint of Molecular Mechanics. Actual nanotube configurations are characterized as being locally minimizing a given configurational energy, including both two- and three-body contributions. By focusing on so-called zigzag and armchair topologies, we prove that the configurational energy is strictly minimized within specific, one-parameter families of periodic configurations. Such optimal configurations are checked to be stable with respect to a large class of small nonperiodic perturbations and do not coincide with classical rolled-up nor polyhedral geometries

Learning for predictions: Real-time reliability assessment of aerospace systems

Prognostics and Health Management (PHM) aim to predict the Remaining Useful Life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the down time of equipment. A major challenge in system prognostics is the availability of accurate physics based representations of the grow rate of faults. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to on-board observations of system’s health. Our approach aims at enabling real-time assessment of systems health and reliability through fast predictions of the Remaining Useful Life that account for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The RUL prediction algorithm relies on a dynamical estimator filter, which allows to deal with nonlinear systems affected by uncertainties with unknown distribution. The proposed method integrates a dynamical model of the fault propagation, accounting for the current and past measured health conditions, the past time history of the operating conditions (such as input command, load, temperature, etc.), and the expected future operating conditions. The model leverages the knowledge collected through the record of past fault measurements, and dynamically adapts the prediction of the damage propagation by learning from the observed time history. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows to refine rapid predictions of the RUL in fractions of seconds by progressively learning from on-board acquisitions