Radio Galaxy Zoo:<scp>Claran</scp>– a deep learning classifier for radio morphologies

Wu, Chen; Wong, O. I.; Rudnick, Lawrence; Shabala, Stanislav S.; Alger, Matthew J.; Banfield, Julie; Ong, Cheng Soon; White, Sarah V.; Garon, Avery F.; Norris, R. P.; Andernach, H.; Tate, Jean; Lukic, Vesna; Tang, Hongming; Schawinski, Kevin; Diakogiannis, Foivos I.

doi:10.1093/mnras/sty2646

Cited by 113 publications

(91 citation statements)

References 34 publications

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“…Williams et al 2019), and to categorise the resulting samples for scientific analysis (e.g. Aniyan & Thorat 2017;Alhassan et al 2018;Wu et al 2019;Lukic 2019;Ma et al 2019). However, sensitive low frequency observations are at the same time revealing a more complex extended source population, including candidate hybrid radio galaxies, restarting and remnant radio galaxies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

Mingo

Croston

Hardcastle

et al. 2019

Monthly Notices of the Royal Astronomical Society

179

223

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Abstract The relative positions of the high and low surface brightness regions of radio-loud active galaxies in the 3CR sample were found by Fanaroff and Riley to be correlated with their luminosity. We revisit this canonical relationship with a sample of 5805 extended radio-loud active galactic nuclei (AGN) from the LOFAR Two-Metre Sky Survey (LoTSS), compiling the most complete data set of radio-galaxy morphological information obtained to date. We demonstrate that, for this sample, radio luminosity does not reliably predict whether a source is edge-brightened (FRII) or centre-brightened (FRI). We highlight a large population of low-luminosity FRIIs, extending three orders of magnitude below the traditional FR break, and demonstrate that their host galaxies are on average systematically fainter than those of high-luminosity FRIIs and of FRIs matched in luminosity. This result supports the jet power/environment paradigm for the FR break: low-power jets may remain undisrupted and form hotspots in lower mass hosts. We also find substantial populations that appear physically distinct from the traditional FR classes, including candidate restarting sources and ‘hybrids’. We identify 459 bent-tailed sources, which we find to have a significantly higher SDSS cluster association fraction (at z < 0.4) than the general radio-galaxy population, similar to the results of previous work. The complexity of the LoTSS faint, extended radio sources not only demonstrates the need for caution in the automated classification and interpretation of extended sources in modern radio surveys, but also reveals the wealth of morphological information such surveys will provide and its value for advancing our physical understanding of radio-loud AGN.

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Section: Introductionmentioning

confidence: 99%

Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

Mingo

Croston

Hardcastle

et al. 2019

Monthly Notices of the Royal Astronomical Society

179

223

View full text Add to dashboard Cite

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“…Em [Wu et al 2018] foi desenvolvido um projeto utilizando redes neurais convolucionais para realizar a classificação de Radio-galáxias via imagens geradas pela combinação ao de sinais de rádio e radiação infravermelha.É um projeto open source, extremamente rápido (<200ms por imagem) e com uma precisão aproximada de 90%.…”

Section: Trabalhos Correlatosunclassified

“…Em [Wu et al 2018] foi desenvolvido um projeto para realizar a classificação de Radio-galáxias via imagens geradas pela combinação de sinais de rádio e radiação infravermelha utilizando redes neurais convolucionais.…”

Section: Trabalhos Correlatosunclassified

Classificação Morfológica de Galáxias Por Meio de Redes Neurais

Silva¹,

Ventura²

2019

Anais Da X Escola Regional De Informática De Mato Grosso (ERI-MT 2019)

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This paper proposes the development of a convolutional neural network for the morphological classification of galaxies through optical images, classifying them into six distinct classes based on the Hubble Tuning Fork model. In order to automate the mass identification and separation of the huge volume of data generated in recent astronomical observatories, deep learning and data augmentation techniques are used to generate increased data variation and consequently improve network accuracy. Our model achieved an average precision of 88%.

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“…In this work, we apply shallow neural networks (NNs) as well as deep convolutional neural networks to study optical stellar spectra in detail and investigate whether using deeper networks of convolutional layers can significantly reduce the error and accuracy achieved in the stellar spectral classification. Deep learning frameworks (Hinton & Salakhutdinov 2006;Bengio 2009;Zeiler & Fergus 2013) have been used in the astronomical domain for various applications like galaxy morphology prediction (Dieleman et al 2015), classification of variable stars based on their light curves (Mahabal et al 2017), estimating atmospheric parameters using stellar spectra (Fabbro et al 2018), detecting bar structures in galaxy images (Abraham et al 2018), classifying galaxy morphologies at radio wavelengths (Wu et al 2019) etc. However, all these problems require a large sample for supervised training of the network.…”

Section: Introductionmentioning

confidence: 99%

Application of convolutional neural networks for stellar spectral classification

Sharma

Kembhavi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters T eff , log g, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using Convolutional Neural Networks. Traditional machine learning (ML) methods with "shallow" architecture (usually up to 2 hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalisation. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Network (CNN) to classify stellar spectra using the Jacoby Atlas, ELODIE and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using convolutional neural networks, we are able to lower the error up to 1.23 spectral sub-classes as compared to that of 2 sub-classes achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS database with SNR>20.

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Radio Galaxy Zoo:Claran– a deep learning classifier for radio morphologies

Cited by 113 publications

References 34 publications

Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

Classificação Morfológica de Galáxias Por Meio de Redes Neurais

Application of convolutional neural networks for stellar spectral classification

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