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

We have constructed a set of supercluster catalogues for the galaxies from the SDSS survey main and luminous red galaxy (LRG) flux-limited samples. To delineate superclusters, we calculated luminosity density fields using the B3-spline…

We perform a detailed study of the resolved properties of emission-line galaxies at kpc-scale to investigate how small-scale and global properties of galaxies are related. 119 galaxies with high-resolution Keck/DEIMOS spectra are selected…

Bright gravitationally lensed galaxies provide our most detailed view of galaxies at high redshift. Yet as a result of the small number of ultra-bright z~2 lensed systems with confirmed redshifts, most detailed spectroscopic studies have…

Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…

The Swift AGN and Cluster Survey (SACS) uses 125 deg^2 of Swift XRT serendipitous fields with variable depths surrounding gamma-ray bursts to provide a medium depth (4e-15 erg/s/cm^2) and area survey filling the gap between deep, narrow…

We use the combined photometric SDSS + GALEX database to look for populations of luminous blue star-forming galaxies. These were initially identified from such a sample at redshifts near 0.4, using SDSS spectra. We make use of the colour…

We present the observed fraction of galaxies with an Active Galactic Nucleus (AGN) as a function of environment in the Early Data Release of the Sloan Digital Sky Survey (SDSS). Using 4921 galaxies between 0.05 <= z <= 0.095, and brighter…

We study the properties of SDSS galaxies with and without AGN detection as a function of the local and global environment measured via the local density, the mass of the galaxy host group (parameterised by the group luminosity) and distance…

We present the first results of the KMOS Lens-Amplified Spectroscopic Survey (KLASS), a new ESO Very Large Telescope (VLT) large program, doing multi-object integral field spectroscopy of galaxies gravitationally lensed behind seven galaxy…

Machine-learning (ML) algorithms will play a crucial role in studying the large datasets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with…

We measure the angular 2-point correlation functions of galaxies in a volume limited, photometrically selected galaxy sample from the fifth data release of the Sloan Digital Sky Survey. We split the sample both by luminosity and galaxy type…

The Sloan Digital Sky Survey (SDSS) surveyed 14,555 square degrees, and delivered over a trillion pixels of imaging data. We present a study of galaxy clustering using 900,000 luminous galaxies with photometric redshifts, spanning between…

The Sloan Digital Sky Survey (SDSS) is making a multi-colour, three dimensional map of the nearby Universe. The survey is in two parts. The first part is imaging one quarter of the sky in five colours from the near ultraviolet to the near…

The classification of the optical spectra of active galactic nuclei (AGN) into different types is well founded on AGN physics, but it involves some degree of human oversight and cannot be reliably scaled to large data sets. Machine learning…

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute…

Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe…

We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor Error…

This paper introduces a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18). By strategically segmenting the sky into sectors aligned with SDSS observational…

We have selected a sample of 88 nearby (z<0.1) galaxy clusters from the SDSS-DR4 with redshift information for the cluster members. We have derived global properties for each cluster, such as their mean recessional velocity, velocity…

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…