相关论文: Classifying Seyfert galaxies with deep learning

We apply classical machine vision and machine deep learning methods to prototype signal classifiers for the search for extraterrestrial intelligence. Our novel approach uses two-dimensional spectrograms of measured and simulated radio…

High resolution spectroscopic observations of Seyfert galaxies with Chandra and XMM-Newton allows us to study the detailed ionization and thermal structures of the X-ray absorbing/emitting material in the circumnuclear environment. The vast…

We present the first result of a programme to search for large flux variations in the X-ray sources of the XMM Serendipitous Survey compared to previous ROSAT observations. An increase in X-ray flux by a factor >10 was discovered from the…

The origin of the unification model for Active Galactic Nuclei (AGN) was the detection of broad hydrogen recombination lines in the optical polarized spectrum of the Seyfert 2 galaxy (Sy2) NGC 1068. Since then, a search for the hidden…

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in conjunction with CNNs, by retraining networks first trained…

Seyfert 1.8/1.9 are sources showing weak broad H-alpha components in their optical spectra. We aim at testing whether Seyfert 1.8/1.9 have similar properties at UV and X-ray wavelengths to Seyfert 2. We use the 15 Seyfert 1.8/1.9 in the…

We discuss the properties of compact nuclear radio components in Seyfert galaxies from the extended 12-micron AGN sample of Rush et al.(1993). Our main results can be summarised as follows. Type 1 and type 2 Seyferts produce compact radio…

We present a study of Seyfert 1.5-2.0 galaxies observed at two epochs with the Hubble Space Telescope (HST) at 1.6 microns. We find that unresolved nuclear emission from 9 of 14 nuclei varies at the level of 10-40% on timescales of 0.7-14…

We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a…

In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years…

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible…

Context. Convolutional neural networks (CNNs) are widely used for automated galaxy morphological classification in large surveys. However, projection effects, image artefacts, and intrinsic degeneracies limit reliable identification of…

We present mosaicked long-slit spectral maps of 18 nearby Active Galactic Nuclei (AGNs), 2 LINERs, and 4 star-forming galaxies. With the resulting data cubes taken using the Kast dual spectrograph on the 3 m Shane telescope of the Lick…

Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science…

We present sub-arcsec near-infrared 1.5 - 2.5 micron moderate resolution long-slit spectra of eight nearby Seyfert galaxies (z<0.01), both parallel to the ionization cone and perpendicular to it. These spectra complement similar data on six…

We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…

Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z>1) galaxies but their formation and role in galaxy evolution remain unclear. High-resolution observations of low-redshift…

We present part of the results from a spectroscopic monitoring programme on a sample of AGNs, relative to Narrow Line Seyfert 1 Galaxies: following the idea that Balmer-line variability can help discriminate among the possible models for…

The excellent soft X-ray sensitivity of the PSPC detector onboard the ROSAT satellite provided the first chance to study precisely the spectral and timing properties of Narrow-Line Seyfert 1 galaxies. ROSAT observations of Narrow-Line…

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in…