UNCERTAINTY REASONING IN ASTRONOMY--APPLICATIONS TO CLASSIFICATION AND OPTIMAL TELESCOPE SCHEDULING

Abstract

ABSTRACT This thesis addresses two topics: the "selection of interesting objects from astronomical catalogues" and the "design of optimised observation schedules." Part I of the thesis deals with the question of how to optimally select interesting objects from astronomical databases. To this end we use a trainable statistical pattern recognition system. The chapter "Supervised and unsupervised classification --- The case of IRAS point sources" provides an short introduction to and review of supervised and un\\-supervised classification. The chapter "Mapping the extragalactic sky with IRAS PSC data" describes the selection of galaxy candidates from the IRAS Point Source Catalog. The distributions of the infrared colours are approximated as multivariate Gaussian distributions. The selection is carried out by statistical classifiers based on the maximum likelihood (ML) and maximum a posteriori (MAP) principles. The chapter "Young stellar objects in the IRAS Point Source Catalog" uses essentially the same methods for selecting young stellar object (YSO) candidates from the IRAS Point Source Catalog. In this case the information provided by the infrared colours alone is insufficient for a good discrimination of YSO candidates against other objects. However with the help of additional uncertain information provided by the spatial clustering properties of the objects on the sky, the selection can be significantly improved. The chapter "Statistical inferencing in the design of a sky-survey project" attempts to bridge the wide gap remaining between a selection of interesting candidates from some catalogue and the design of a suitably optimised strategy for follow-up observations. Part II of this thesis deals with the problem of how to create and optimise a long-term schedule for an astronomical telescope. Specifically the case of scheduling the Hubble Space Telescope (HST) observations is considered. It is shown how an artificial neural network with a novel stochastic dynamics can be used to efficiently solve the underlying constraint satisfaction problem (CSP). The chapter "The processing of HST observing programmes" introduces the HST long range planning problem and explains why the scheduling of pointed observations with this astronomical satellite in a low Earth orbit is difficult. The chapter "Connectionism and neural networks" provides some conceptual background on artificial neural networks. It is shown how a simple scheduling problem can be mapped onto the topology of an artificial neural network. Adding a suitable network dynamics turns such a network into an efficient problem solver. The chapter "A discrete stochastic neural network algorithm for constraint satisfaction problems" describes the novel "guarded discrete stochastic" (GDS) network algorithm, a relative of the standard Hopfield-Tank network, which is surprisingly efficient in finding solutions to large constraint satisfaction problems even on serial machines. The chapter "Scheduling with neural networks --- The case of Hubble Space Telescope" finally describes how the GDS network is integrated into Spike, the scheduling system developed at the Space Telescope Science Institute, Baltimore, for creating long range HST observing schedules. The methodology described here is quite general and is applicable to scheduling other ground-based or space-born astronomical observatories.