Further+Reading

=**Pattern Recognition Problems**=

[1] Some Pattern Recognition Challenges in Data-Intensive Astronomy

We review some of the recent developments and challenges posed by the data analysis in modern digital sky surveys, which are representative of the information-rich astronomy in the context of Virtual Observatory. Illustrative examples include the problems of an automated star-galaxy classification in complex and heterogeneous panoramic imaging data sets, and an automated, iterative, dynamical classification of transient events detected in synoptic sky surveys. These problems offer good opportunities for productive collaborations between astronomers and applied computer scientists and statisticians, and are representative of the kind of challenges now present in all data-intensive fields. We discuss briefly some emergent types of scalable scientific data analysis systems with a broad applicability.

=**Use of Neural Networks**=

[2] Application of Neural Networks to the Study of Stellar Model Solutions

Artificial neural networks (ANN) have different applications in Astronomy, including data reduction and data mining. In this work we propose the use ANNs in the identification of stellar model solutions. We illustrate this method, by applying an ANN to the 0.8M ⊙ star CG Cyg B. Our ANN was trained using 60,000 different 0.8M ⊙ stellar models. With this approach we identify the models which reproduce CG Cyg B’s position in the HR diagram. We observe a correlation between the model’s initial metal and helium abundance which, in most cases, does not agree with a helium to metal enrichment ratio Y/Z=2. Moreover, we identify a correlation between the model’s initial helium/metal abundance and both its age and mixing-length parameter. Additionally, every model found has a mixing-length parameter below 1.3. This means that CG Cyg B’s mixing-length parameter is clearly smaller than the solar one. From this study we conclude that ANNs are well suited to deal with the degeneracy of model solutions of solar type stars.

[3] The Stellar Parameterization using Artificial Neural Network

An update on recent methods for automated stellar parametrization is given. We present preliminary results of the ongoing program for rapid parametrization of field stars using medium resolution spectra obtained using Vainu Bappu Telescope at VBO, Kavalur, India. We have used Artificial Neural Network for estimating temperature, gravity, metallicity and absolute magnitude of the field stars. The network for each parameter is trained independently using a large number of calibrating stars. The trained network is used for estimating atmospheric parameters of unexplored field stars.

[4] Automatic Spectral Classification of Stellar Spectra with Low Signal-to-Noise Ratio Using Artificial Neural Networks

**Context.** As part of a project aimed at deriving extinction-distances for thirty-five planetary nebulae, spectra of a few thousand stars were analyzed to determine their spectral type and luminosity class. **Aims.** We present here the automatic spectral classification process used to classify stellar spectra. This system can be used to classify any other stellar spectra with similar or higher signal-to-noise ratios. **Methods.** Spectral classification was performed using a system of artificial neural networks that were trained with a set of line-strength indices selected among the spectral lines most sensitive to temperature and the best luminosity tracers. The training and validation processes of the neural networks are discussed and the results of additional validation probes, designed to ensure the accuracy of the spectral classification, are presented. **Results.**Our system permits the classification of stellar spectra of signal-to-noise ratio (S/N) significantly lower than it is generally considered to be needed. For S/N >= 20, a precision generally better than two spectral subtypes is obtained. At S/N < 20, classification is still possible but has a lower precision. Its potential to identify peculiar sources, such as emission-line stars, is also recognized.

[5] Selection of Radio Pulsar Candidates Using Artificial Neural Networks

Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.

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