Statistical methods in astronomy based on artificial neural network techniques

Abstract

Large amounts of astronomical data are becoming common in modern observational projects. Adequate analysis tools need to be developed if one wants to work effectively with the data. In order to extract physical quantities from the data, we have been investigating the application of a computing technique, Artificial Neural Networks (ANNs) to astronomy, and several original methods have resulted. We have developed the following tools: (1) The NNC (Neural Network Classifier, Serra-Ricart et al. 1991, 1994a). We propose a method to classify faint objects from digital astronomical images based on a layered feedforward neural network which has been trained by the backpropagation procedure. (2) The NNA (Neural Network Analysis, Serra-Ricart et al. 1993). We present a new method also based on artificial neural networks techniques, for displaying an n-dimensional distribution in a projected space of 1, 2 or 3 dimensions. As with Principal Component Analysis, the NNA offers powerful ways of extracting information on the data structure and is useful to, a) reduce the number of input variables to its inherent dimensionality (dimension reduction task), and b) identify different groups of objects (clustering task). (3) The NNI (Neural Network Interpolation, Serra-Ricart et al. 1994b). We propose a method for interpolating multidimensional unbinned data, which could also be sparse, using neural networks algorithms.