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

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.

[1]  Brian Everitt,et al.  Cluster analysis , 1974 .

[2]  J. V. Peach,et al.  Studies of rich clusters of galaxies – IV. Photometry of the Coma Cluster , 1977 .

[3]  J. Tyson,et al.  Focas: faint object classification and analysis system. , 1981 .

[4]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[5]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[6]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[7]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[8]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[9]  Rose,et al.  Statistical mechanics and phase transitions in clustering. , 1990, Physical review letters.

[10]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[11]  S. Odewahn,et al.  Automated star/galaxy discrimination with neural networks , 1992 .

[12]  C. J. Pritchet,et al.  The CFHT north galactic pole faint galaxy survey , 1992 .

[13]  Thomas Martinetz,et al.  'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[14]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[15]  Juha Karhunen,et al.  Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.

[16]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[17]  The Importance of Wide-Field Imaging , 1994 .

[18]  Suchendra M. Bhandarkar,et al.  A multilayer self-organizing feature map for range image segmentation , 1995, Neural Networks.

[19]  Juha Karhunen,et al.  Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.

[20]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[21]  Alberto Fernández-Soto,et al.  Star-forming galaxies at very high redshifts , 1996, Nature.

[22]  E. Bertin,et al.  SExtractor: Software for source extraction , 1996 .

[23]  M. J. Coe,et al.  Star/galaxy classification using Kohonen self-organizing maps , 1996 .

[24]  David Bazell,et al.  A Comparison of Neural Network Algorithms and Preprocessing Methods for Star-Galaxy Discrimination , 1998 .

[25]  S. M. Fall,et al.  The Hubble Deep Field : proceedings of the Space Telescope Science Institute Symposium, held in Baltimore, Maryland, May 6-9, 1997 , 1998 .

[26]  L. Milano,et al.  Spectral analysis of stellar light curves by means of neural networks , 1999 .

[27]  Roberto Tagliaferri,et al.  Automated labeling for unsupervised neural networks: a hierarchical approach , 1999, IEEE Trans. Neural Networks.