A Discrete Adapted Hierarchical Basis Solver For Radial Basis Function Interpolation

In this paper we develop a discrete Hierarchical Basis (HB) to efficiently solve the Radial Basis Function (RBF) interpolation problem with variable polynomial order. The HB forms an orthogonal set and is adapted to the kernel seed function and the placement of the interpolation nodes. Moreover, this basis is orthogonal to a set of polynomials up to a given order defined on the interpolating nodes. We are thus able to decouple the RBF interpolation problem for any order of the polynomial interpolation and solve it in two steps: (1) The polynomial orthogonal RBF interpolation problem is efficiently solved in the transformed HB basis with a GMRES iteration and a diagonal, or block SSOR preconditioner. (2) The residual is then projected onto an orthonormal polynomial basis. We apply our approach on several test cases to study its effectiveness, including an application to the Best Linear Unbiased Estimator regression problem

The counterrotating core and the black hole mass of IC1459

The E3 giant elliptical galaxy IC1459 is the prototypical galaxy with a fast counterrotating stellar core. We obtained one HST/STIS long-slit spectrum along the major axis of this galaxy and CTIO spectra along five position angles. We present self-consistent three-integral axisymmetric models of the stellar kinematics, obtained with Schwarzschild's numerical orbit superposition method. We study the dynamics of the kinematically decoupled core (KDC) in IC1459 and we find it consists of stars that are well-separated from the rest of the galaxy in phase space. The stars in the KDC counterrotate in a disk on orbits that are close to circular. We estimate that the KDC mass is ~0.5% of the total galaxy mass or ~3*10^9 Msun. We estimate the central black hole mass M_BH of IC1459 independently from both its stellar and its gaseous kinematics. Some complications probably explain why we find rather discrepant BH masses with the different methods. The stellar kinematics suggest that M_BH = (2.6 +/- 1.1)*10^9 Msun (3 sigma error). The gas kinematics suggests that M_BH ~ 3.5*10^8 Msun if the gas is assumed to rotate at the circular velocity in a thin disk. If the observed velocity dispersion of the gas is assumed to be gravitational, then M_BH could be as high as ~1.0*10^9 Msun. These different estimates bracket the value M_BH = (1.1 +/- 0.3)*10^9 Msun predicted by the M_BH-sigma relation. It will be an important goal for future studies to assess the reliability of black hole mass determinations with either technique. This is essential if one wants to interpret the correlation between the BH mass and other global galaxy parameters (e.g. velocity dispersion) and in particular the scatter in these correlations (believed to be only ~0.3 dex). [Abridged]Comment: 51 pages, LaTeX with 19 PostScript figures. Revised version, with three new figures and data tables. To appear in The Astrophysical Journal, 578, 2002 October 2

Searching for Exoplanets Using Artificial Intelligence

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.Comment: Accepted, 16 Pages, 14 Figures, https://github.com/pearsonkyle/Exoplanet-Artificial-Intelligenc

System- and Data-Driven Methods and Algorithms

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques