相关论文: Classifying Seyfert galaxies with deep learning

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained…

We present optical broad band B-I colour maps of a further sample of 10 Seyfert 2 galaxies. In these bands, the contribution from emission lines to the total flux is small, and hence the images predominantly trace the continuum…

The latest observations of line and continuum spectra emitted from the extended narrow line region (ENLR) of the Seyfert 2 galaxy NGC 7212 are analysed using models accounting for photoionization from the active nucleus and shocks. The…

New spectra of NGC 2992 from the Cerro Tololo Inter-American Observatory show that this nearby AGN has changed its type classification to a Seyfert 2 in 2006. It was originally classified as a Seyfert 1.9, and has been previously seen as a…

Preliminary results are presented for a spectroscopic survey of the Groth Survey Strip (GSS), a 40 by 3.5 arcminute region of the sky imaged with HST, for which several hundred galaxy spectra have been obtained as part of the DEEP project…

Indirectly resolving the line-emitting gas regions in distant Active Galactic Nuclei (AGN) requires both high-resolution photometry and spectroscopy (i.e. through reverberation mapping). Emission in AGN originates on widely different…

We present a deep learning model to predict the r-band bulge-to-total light ratio (B/T) of nearby galaxies using their multi-band JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample…

Parallel analysis of the large-scale morphology and local environment of matched active and control galaxy samples plays an important role in studies of the fueling of active galactic nuclei. We carry out a detailed morphological…

We study 12 Seyfert 1 galaxies with a high level of optical polarization. Optical light emerging from the innermost regions is predominantly scattered in a polar region above the central engine directly in our line of sight. These sources…

We present a Hubble Space Telescope (HST) survey of extended [OIII] emission for a sample of 60 Seyfert galaxies (22 Seyfert 1's and 38 Seyfert 2's), selected based on their far infrared properties. The observations for 42 of these galaxies…

The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent…

As the third paper of our serial studies that are aim at examining the AGN-host coevolution by using partially obscured AGNs, we extend the broad-line composite galaxies (composite AGNs) into ROSAT-selected Seyfert 1.8/1.9 galaxies basing…

Using a large sample of 90 Seyfert 2 galaxies (Sy2s) with spectropolarimetric observations, we tested the suggestion that the presence of hidden broad-line regions (HBLRs) in Sy2s is dependent upon the Eddington ratio. The stellar velocity…

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…

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these…

This work studies the optical emission line properties and physical conditions of the narrow line region (NLR) of seven narrow-line Seyfert 1 galaxies (NLS1). Our results show that the flux carried out by the narrow component of H-beta is,…

At high redshift, due to both observational limitations and the variety of galaxy morphologies in the early universe, measuring galaxy structure can be challenging. Non-parametric measurements such as the CAS system have thus become an…

We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…

Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying…

Results obtained from an X-ray spectral survey of nearby Seyfert galaxies using XMM--Newton are reported. The sample was optically selected, well defined, complete in B mag, and distance limited: it consists of the nearest (D<22 Mpc) 27…