arXiv subjects

A. Moore

Publications and source records attributed to A. Moore.

5 recordsLinked to original sources

arXiv2026

Simultaneous TRACERS and THEMIS Observations of Reversed Cusp Ion Dispersions and Dual-Lobe Reconnection

We present observations from two consecutive TRACERS-2 orbits through the northern low-altitude cusp. During the first crossing, TRACERS-2 observed reversed cusp ion dispersion and sunward convection, consistent with magnetopause reconnection tailward of the cusp during this northward IMF interval. Simultaneous THEMIS-D observations at the equatorial magnetopause show heated magnetosheath plasma captured on closed field lines, with similar particle spectra as in in the low-altitude cusp, indicating that reconnection indeed occurred tailward of the cusp and in both hemispheres. When TRACERS-2 traversed the northern cusp again, 95 minutes later, the IMF was dominated by a negative BX component. Despite the different IMF conditions, TRACERS-2 recorded nearly the same cusp signatures as before, i.e., reversed ion dispersion and sunward convection. The observations indicate that tailward-of-cusp reconnection can occur for both northward and BX-dominated IMF and that these distinct IMF geometries can produce remarkably similar plasma and field signatures in the low-altitude cusp.

physics.space-ph↗

arXiv2021

Domain Decomposition in space-time of 4D-VAR Data Assimilation problem: a case study on the ROMS software

Domain Decomposition of 4D-VAR Data Assimilation (DD-4DVAR) is made up of decomposition of the spate-time domain, solution of reduced forecast model and minimization of local 4D-VAR functionals. Relying on the existing software implementation of ROMS software, we describe main components of DD-4D VAR DA method, highlighting the topics that we will should address both on the mathematical problem underlying ROMS and the MPI-based code implementation of the ROMS-IS4DVAR formulation.

cs.CE↗

arXiv2000

SDSS-RASS: Next Generation of Cluster-Finding Algorithms

We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical databases like the Sloan Digital Sky Survey (SDSS). Specifically, this paper presents four new advances: (1) A new semi-parametric algorithm - nicknamed ``C4'' - for jointly finding clusters of galaxies in the SDSS and ROSAT All-Sky Survey databases; (2) The introduction of the False Discovery Rate into Astronomy; (3) The role of kernel shape in optimizing cluster detection; (4) A new determination of the X-ray Cluster Luminosity Function which has bearing on the existence of a ``deficit'' of high redshift, high luminosity clusters. This research is part of our ``Computational AstroStatistics'' collaboration (see Nichol et al. 2000) and the algorithms and techniques discussed herein will form part of the ``Virtual Observatory'' analysis toolkit.

astro-ph↗

arXiv1999

Focussing Image Slicers: Refractive and Reflective

A number of design options for image slicers for NGST and Gemini are being investigated. These image slicers are all of the focussing type and both refractive and reflective solutions are being explored. One such device, an image slicer that focuses 10 slices on a spectrograph slit is now in operation at the McMath Solar telescope. It consists of three lenslet arrays, and additionally acts as a focal reducer and provides correction for astigmatism of the telescope. A combined refractive and reflective slicer designed for use on NGST delivers near-diffraction limited images for up to 40 slices.

astro-ph↗

arXiv1998

Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets

This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its leaves. We show how the ADtree can be used to accelerate Bayes net structure finding algorithms, rule learning algorithms, and feature selection algorithms, and we provide a number of empirical results comparing ADtree methods against traditional direct counting approaches. We also discuss the possible uses of ADtrees in other machine learning methods, and discuss the merits of ADtrees in comparison with alternative representations such as kd-trees, R-trees and Frequent Sets.

cs.AI↗