相关论文: Morphological classification of compact and extend…

Morphologically classifying radio sources in continuum images with the SKA has the potential to address some of the key questions in cosmology and galaxy evolution. In particular, we may use different classes of radio sources as independent…

Using the HST WFPC2 we perform deep I-band imaging of 9 radio-selected (limit 14 microJanskys at 8.5 GHz) faint galaxies from Roche, Lowenthal and Koo (2002). Two are also observed in V. Six of the galaxies have known redshifs of 0.4<z<1.0.…

We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy…

We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a…

Fanaroff-Riley class I (FRI) radio galaxies show centre-brightened emission from disrupted lower power jets, while traditionally more luminous class II (FRIIs), are edge-brightened, with relativistic jets terminating in hotspots. Population…

We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in…

The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…

We introduce a novel machine learning dataset tailored for the classification of bent radio active galactic nuclei (AGN) in astronomical observations. Bent radio AGN, distinguished by their curved jet structures, provide critical insights…

We train Artificial Neural Networks to classify galaxies based solely on the morphology of the galaxy images as they appear on blue survey plates. The images are reduced and morphological features such as bulge size and the number of arms…

We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some…

There is an obvious need for automated classification of galaxies, as the number of observed galaxies increases very fast. We examine several approaches to this problem, utilising {\em Artificial Neural Networks} (ANNs). We quote results…

Radio-loud active galaxies (RLAGN) can exhibit various morphologies. The Fanaroff-Riley (FR) classifications, which are defined by the locations of peaks in surface brightness, have been applied to many catalogues of RLAGN. The FR…

Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…

Among astrophysical sources in the Advanced LIGO and Advanced Virgo detectors' frequency band are rotating non-axisymmetric neutron stars emitting long-lasting, almost-monochromatic gravitational waves. Searches for these continuous…

Extragalactic radio continuum surveys play an increasingly more important role in galaxy evolution and cosmology studies. While radio galaxies and radio quasars dominate at the bright end, star-forming galaxies (SFGs) and radio-quiet Active…

We present a quantitative method to classify galaxies, based on multi-wavelength data and elaborated from the properties of nearby galaxies. Our objective is to define an evolutionary method that can be used for low and high redshift…

The Combined NVSS-FIRST Galaxy (CoNFIG) survey was defined by selecting all sources with S_1.4GHz > 1.3Jy from the NRAO VLA Sky Survey (NVSS) in the north field of the Faint Images of the Radio Sky at Twenty-cm (FIRST) survey. We carried…

Convolutional Neural Networks (CNNs) perform very well in image classification and object detection in recent years, but even the most advanced models have limited rotation invariance. Known solutions include the enhancement of training…

In this paper the cosmic evolution of the space density of Fanaroff & Riley Class I (FRI) radio sources is investigated out to z ~ 1, in order to understand the origin of the differences between these and the more powerful FRIIs. High…