相关论文: Classifying Stars, Galaxies and AGN in CLAUDS+HSC-…

We analysed the optical and infrared properties of X-ray sources in the Great Observatories Origins Deep Survey (GOODS), a deep multiwavelength survey covering 0.1 square degrees in two fields. The HST ACS data are well explained by a…

We present results of recurrence analysis of 46 active galactic nuclei (AGN) using light curves from the 157-month catalog of the Swift Burst Alert Telescope (BAT) in the 14-150 keV band. We generate recurrence plots and compute recurrence…

Under the unified model for active galactic nuclei (AGNs), narrow-line (Type 2) AGNs are, in fact, broad-line (Type 1) AGNs but each with a heavily obscured accretion disk. We would therefore expect the optical continuum emission from Type…

Through an optical campaign performed at 5 telescopes located in the northern and the southern hemispheres, plus archival data from two on line sky surveys, we have obtained optical spectroscopy for 17 counterparts of suspected or poorly…

Optical rest-frame spectroscopic diagnostics are usually employed to distinguish between star formation and AGN-powered emission. However, this method is biased against dusty sources, hampering a complete census of the AGN population across…

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…

Extended stellar haloes are a natural by-product of the hierarchical formation of massive galaxies. If merging is a non-negligible factor in the growth of our Galaxy, evidence of such events should be encoded in its stellar halo. Reliable…

Gradient boosting methods based on Structured Categorical Decision Trees (SCDT) have been demonstrated to outperform numerical and one-hot-encodings on problems where the categorical variable has a known underlying structure. However, the…

In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently…

We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the…

(Abridged) Many classes of active galactic nuclei (AGN) have been defined entirely throughout optical wavelengths while the X-ray spectra have been very useful to investigate their inner regions. However, optical and X-ray results show many…

We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an…

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…

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…

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural…

(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…

The aim of this paper is to study the stellar population of galaxies hosting an active galactic nucleus (AGN). We studied a sub-sample of hard X-ray emitting AGNs from the INTEGRAL and Swift catalogs which were previously identified and…

We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…

In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…

We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable…