Morphological Classification of galaxies by Artificial Neural Networks

Storrie-Lombardi, Michael C.; Lahav, O.; Sodré, L.; Storrie-Lombardi, L. J.

doi:10.1093/mnras/259.1.8p

Cited by 159 publications

(122 citation statements)

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“…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.…”

Section: Neural Network and Parameter Estimationmentioning

confidence: 99%

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

Bazarghan

Safari

Innes³

et al. 2008

A&A

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Context. Nanoflares are small impulsive bursts of energy that blend with and possibly make up much of the solar background emission. Determining their frequency and energy input is central to understanding the heating of the solar corona. One method is to extrapolate the energy frequency distribution of larger individually observed flares to lower energies. Only if the power law exponent is greater than 2 is it considered possible that nanoflares contribute significantly to the energy input. Aims. Time sequences of ultraviolet line radiances observed in the corona of an active region are modelled with the aim of determining the power law exponent of the nanoflare energy distribution. Methods. A simple nanoflare model based on three key parameters (the flare rate, the flare duration, and the power law exponent of the flare energy frequency distribution) is used to simulate emission line radiances from the ions Fe XIX, Ca XIII, and Si iii, observed by SUMER in the corona of an active region as it rotates around the east limb of the Sun. Light curve pattern recognition by an Artificial Neural Network (ANN) scheme is used to determine the values.Results. The power law exponents, α ≈ 2.8, 2.8, and 2.6 are obtained for Fe XIX, Ca XIII, and Si iii respectively.Conclusions. The light curve simulations imply a power law exponent greater than the critical value of 2 for all ion species. This implies that if the energy of flare-like events is extrapolated to low energies, nanoflares could provide a significant contribution to the heating of active region coronae.

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Section: Neural Network and Parameter Estimationmentioning

confidence: 99%

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

Bazarghan

Safari

Innes³

et al. 2008

A&A

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“…Using a training sample, the discrimination power of some parameters is determined. Then, a combination of tests is carried out allowing the separation of the object sample into several classes; -The neural network method (see: Storrie-Lombardi et al 1992;Odewahn et al 1992;Lahav 1994;Bertin & Arnouts 1996a,b). The network is a set of inputs (parameters) connected to outputs (classes of objects) through weighted, non linear links (neurones).…”

Section: Send Offprint Requests To: G Paturelmentioning

confidence: 99%

An image database

Paturel

Fang

Petit

et al. 2000

Astron. Astrophys. Suppl. Ser.

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Abstract. This paper presents a method for extracting a catalogue of galaxy candidates from the Digitized Sky Survey (DSS). The method is based on a functional analysis applied on each individual plate. The standard deviation of pixel optical densities versus the inverse of surface area leads to a diagram in which extended and star-like objects are well separated. This diagram is used for a preliminary recognition. Then, a filtering process is applied using a Neural Network method associated with a training sample built with well identified objects. The main catalogue gives coordinates, total magnitude, isophotal diameter, axis ratio, position angle for 2 772 061 galaxy candidates. The method favors the detection of normal galaxies. This creates a bias against compact high surface brightness galaxies.

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“…Andreon & Davoust (1997) have classified galaxies in clusters based on the isophotal analysis of galaxy images. Storrie-Lombardi et al (1992) have employed artificial neural networks for galaxy classification by assigning Bayesian probabilities to the corresponding morphological type of the galaxy. Neural network classifiers were developed by Odewahn (1995) for automated classification of galaxies using photometric parameters such as mean surface brightness, color, concentration index and surface brightness.…”

Section: Introductionmentioning

confidence: 99%

Galaxy classification using fractal signature

Lekshmi

Revathy

Nayar

2003

A&A

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Abstract. Fractal geometry is becoming increasingly important in the study of image characteristics. For recognition of regions and objects in natural scenes, there is always a need for features that are invariant and they provide a good set of descriptive values for the region. There are many fractal features that can be generated from an image. In this paper, fractal signatures of nearby galaxies are studied with the aim of classifying them. The fractal signature over a range of scales proved to be an efficient feature set with good discriminating power. Classifiers were designed using nearest neighbour method and neural network technique. Using the nearest distance approach, classification rate was found to be 92%. By the neural network method it has been found to increase to 95%.

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Morphological Classification of galaxies by Artificial Neural Networks

Cited by 159 publications

References 0 publications

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

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

An image database

Galaxy classification using fractal signature

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