scholarly journals THE POSSIBILITIES OF THE “CLUSTER CARTOGRAPHY” TOOL FOR THE STUDY OF THE INNER STRUCTURES OF GALAXY CLUSTERS

2021 ◽  
Vol 34 ◽  
pp. 35-39
Author(s):  
S. I. Yemelyanov ◽  
E. A. Panko
Keyword(s):  
Galaxy Clusters ◽  
Cluster Center ◽  
Cluster Member ◽  
Morphological Classification ◽  
Galaxy Distribution ◽  
The Galaxy ◽  
Celestial Sphere ◽  
Input Catalogue ◽  
Best Fit

We describe the possibilities of the “Cluster Cartography” tool which was created for detailed study of the 2D distribution of galaxies in the clusters. The main tasks of the “Cluster Cartography” tool were the detailed study of the morphologyof galaxy clusters using the statistically significant numerical criteria as well as to detect their regular peculiarities. The tool allows to create the 2D map with positions of galaxies in the cluster field and show for each cluster member its shape and orientation as a best-fit ellipse using input catalogue data. The size of symbols for galaxies correspond to input data.It may reflect the galaxy image in arcseconds from catalogue in the map 4000×4000arcsec. Another way connects the size of the symbol with the magnitude of the galaxy. Tool is able to build the map in four modes: the symbols are dots; the symbols are circles with diameters reflected the magnitudes of galaxies; the symbols are ellipses with size reflected the magnitudesand both ellipticities and orientation from the input catalogue; the symbols illustrate the shape of galaxies in projection to the celestial sphere. The “Cluster Cartography” algorithms allow to detect the standard cases in galaxy distribution, suchas the degree of concentration to the cluster center and/or to some line on a statistically significant level using the numerical criteria. Also “Cluster Cartography” allows to detect other features, such as crosses, semi-crosses, complex crosses and short compact chains, as well as to export the list of galaxies forming the peculiarities for the futurestudy. The final version of the “Cluster Cartography” allows to realize the modern scheme for detailed morphological classification of galaxy clusters. The “Cluster Cartography” is powerful and perspective tool for study of features of galaxy clusters.

scholarly journals Automated morphological classification of galaxies using wavelet transform

2009 ◽  
Vol 5 (S262) ◽  
pp. 317-318
Author(s):  
Didier Curty ◽  
François C. Cuisinier ◽  
Carlos R. Rabaça
Keyword(s):  
Wavelet Transform ◽  
Radial Distance ◽  
Wavelet Coefficients ◽  
Morphological Classification ◽  
Spatial Frequencies ◽  
Irregular Galaxies ◽  
The Galaxy ◽  
Star Formation Regions

AbstractThe wavelet transform acts to segregate objects in function of their size. We apply this method on images of galaxies to decompose them into coefficients representing only objects of the same size. The total fluxes of the wavelet coefficients describe the cumulative power spectrum of spatial frequencies. Based on this spectrum, we propose a new parameter to quantify the galaxy texture. As expected, it remains small and quite invariant for early-type galaxies, while it covers a large range and takes larger values for late-type galaxies. Combined with a second parameter, our determination of the texture is able to successfully separate galaxy types. By thresholding the wavelet coefficients, we detect luminous lumps. In irregular galaxies, their radial distribution seems to show a double peak. This could be the trace of a privileged radial distance of strong star formation regions.

scholarly journals Galaxy structural analysis with the curvature of the brightness profile

2019 ◽  
Vol 489 (1) ◽  
pp. 1161-1180 ◽  
Author(s):  
Geferson Lucatelli ◽  
Fabricio Ferrari
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Morphological Classification ◽  
Evolution Path ◽  
Starting Point ◽  
The Galaxy ◽  
Curvature Measures ◽  
Formation And Evolution

Abstract In this work, we introduce the curvature of a galaxy brightness profile to identify its structural subcomponents in a non-parametrically fashion. Bulges, bars, discs, lens, rings, and spiral arms are key to understand the formation and evolution path the galaxy undertook. Identifying them is also crucial for morphological classification of galaxies. We measure and analyse in detail the curvature of 14 galaxies with varied morphology. High (low) steepness profiles show high (low) curvature measures. Transitions between components are identified as local peaks oscillations in the values of the curvature. We identify patterns that characterize bulges (pseudo or classic), discs, bars, and rings. This method can be automated to identify galaxy components in large data sets or to provide a reliable starting point for traditional multicomponent modelling of galaxy light distribution.

CLINICAL AND MORPHOLOGICAL CLASSIFICATION OF ACUTE PANCREATITIS AND ITS COMPLICATIONS

2016 ◽  
Author(s):  
S. N. Bogdanov ◽  
◽  
S. Ju. Babaev ◽  
A. V. Strazhnov ◽  
A. B. Stroganov ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Morphological Classification

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

scholarly journals CNN architecture comparison for radio galaxy classification

2021 ◽  
Vol 503 (2) ◽  
pp. 1828-1846
Author(s):  
Burger Becker ◽  
Mattia Vaccari ◽  
Matthew Prescott ◽  
Trienko Grobler
Keyword(s):  
Comparative Study ◽  
Galaxy Evolution ◽  
Radio Galaxy ◽  
Recognition Performance ◽  
Model Complexity ◽  
Great Promise ◽  
Radio Sources ◽  
Morphological Classification ◽  
Computational Performance

ABSTRACT The morphological classification of radio sources is important to gain a full understanding of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citizen scientists, and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is convolutional neural networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. First, a proper analysis of whether overfitting occurs when training CNNs to perform radio galaxy morphological classification using a small curated training set is needed. Secondly, a good comparative study regarding the practical applicability of the CNN architectures in literature is required. Both of these shortcomings are addressed in this paper. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, computational complexity, and mean per class accuracy. As part of this study, we also investigate the effect that receptive field, stride length, and coverage have on recognition performance. For the sake of completeness, we also investigate the recognition performance gains that we can obtain by employing classification ensembles. A ranking system based upon recognition and computational performance is proposed. MCRGNet, Radio Galaxy Zoo, and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.

Morphological classification of crista fenestra of round window corridor during pediatric cochlear implantation

2021 ◽  
pp. 110816
Author(s):  
Saad Elzayat ◽  
Hitham H. Elfarargy ◽  
Islam Soltan ◽  
Mona A. Abdel-Kareem ◽  
Maurizio Barbara ◽  
...  
Keyword(s):  
Cochlear Implantation ◽  
Round Window ◽  
Morphological Classification
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