相关论文: Automatic unsupervised classification of all SDSS/…

We analyze a portion of the SDSS photometric catalog, consisting of approximately 10,000 objects that have been spectroscopically classified into stars, galaxies, QSOs, late-type stars and unknown objects (spectroscopically unclassified…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

We introduce a novel galaxy classification methodology based on the visible spectra of a sample of over 68,000 nearby ($z\leq 0.1$) Sloan Digital Sky Survey lenticular (S0) galaxies. Unlike traditional diagnostic diagrams, which rely on a…

We present a data-driven fuzzy set framework for classifying galaxies into the red sequence, blue cloud, and green-valley populations using multiple observables from the Sloan Digital Sky Survey (SDSS DR18). Unlike traditional methods based…

In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color…

Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grows with surveys such as Euclid and Vera C. Rubin , there is a need for tools to classify and analyze…

We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with…

We consider the techniques to distinguish normal star forming (NSF) galaxies and active galactic nuclei (AGN) hosts using optical spectra. The observational data base is a set of 20000 galaxies extracted from the Sloan Digital Sky Survey,…

The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from…

We apply a novel spectral graph technique, that of locally-biased semi-supervised eigenvectors, to study the diversity of galaxies. This technique permits us to characterize empirically the natural variations in observed spectra data, and…

The availability of highly accurate urban airborne laser scanning (ALS) data will increase rapidly in the future, especially as acquisition costs decrease, for example through the use of drones. Current challenges in data processing are…

A new catalog of isolated galaxies from The Sloan Digital Sky Survey (DR5) is presented. 1520 isolated galaxies were found in 1.4 steradians of sky. The selection criteria in this so called UNAM-KIAS catalog was implemented from a variation…

Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in…

We present a new method to classify galaxies from large surveys like the Sloan Digital Sky Survey using inclination-corrected concentration, inclination-corrected location on the color-magnitude diagram, and apparent axis ratio. Explicitly…

We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…

Many open questions in X-ray astronomy are limited by the relatively small number of objects in uniform optically-identified samples, especially when rare subclasses are considered, or subsets isolated to search for evolution or…

Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2 degree Field survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and…

Autism spectrum disorder (ASD) remains a challenging condition to diagnose effectively and promptly, despite global efforts in public health, clinical screening, and scientific research. Traditional diagnostic methods, primarily reliant on…

In a series of papers based on the FIRST and Sloan Digital Sky Surveys (SDSS), we investigate the local population of star-forming galaxies and Active Galactic Nuclei (AGN) in order to clarify the link between these two types of activity,…

We present a machine learning model to classify Active Galactic Nuclei (AGN) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test…