Wide field imaging - I. Applications of neural networks to object detection and star/galaxy classification

Abstract

Astronomical wide‐field imaging performed with new large‐format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of ‘what an object is’ (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features we use a NN to select the most significant features among the large number of measured ones, and then we use these selected features to perform the classification task. In order to optimize the performance of the system, we implemented and tested several different models of NN. The comparison of the NExt performance with that of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.

A nanoflare model for active region radiance: application of artificial neural networks

“…ANNs have become a popular tool in almost every field of science. In recent years, ANNs have been widely used in astronomy for applications such as star/galaxy discrimination, (Andreon et al 2000;Cortiglioni et al 2001), morphological classification of galaxies, (Storrie-Lombardi et al 1992;Ball et al 2004), and spectral classification of stars (von Hippel et al 1994;Bazarghan & Gupta 2008;Bazarghan 2008). We employ probabilistic neural networks (PNNs Specht 1988, 1990.…”

Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classification

“…Miller & Coe 1996), artificial neural networks (e.g. Odewahn et al 1992Odewahn et al , 1993Bertin & Arnouts 1996;Andreon et al 2000;Philip et al 2002), or fuzzy set reasoning (Mähönen & Frantti 2000). SEXTRACTOR uses a trained neural network for star/galaxy classification.…”

A difference boosting neural network for automated star-galaxy classification

“…Among other packages proposed recently for stargalaxy classification in wide field images is NExtractor (NExt) by Andreon et al (2000). NExt claims to be the first of its kind that uses a neural network both for extracting the principal components in the feature space, as well as for classification.…”

“…The performance of the network was evaluated over twenty five parameters that were expected to be characteristic to the class label of the objects, and it was found that six of these parameters, namely, the harmonic and Kron radius, two gradients of the PSF, the second total moment and a ratio that involves the measures of intensity and area of the observed object were sufficient to produce optimum classification. A comparison of NExt performance with that of SExtractor by Andreon et al (2000) showed that NExt has a classification accuracy that is as good as or better than SExtractor. The NExt code is not publicly available at the present time (Andreon, personal communication) and a comparison with DBNN is not possible.…”