相关论文: Spiral-Elliptical automated galaxy morphology clas…
We present a systematic observational study of the spin alignment between galaxy groups and their central galaxies using a large spectroscopic sample from the Sloan Digital Sky Survey. Unlike previous studies based on morphology or major…
Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented…
Modern cosmological simulations model the universe with increasing sophistication and at higher spatial and temporal resolutions. These enhancements permit detailed comparisons between the simulation outputs and real observational data.…
Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative AI method capable of predicting optical galaxy…
We present a catalog of detailed visual classifications for 14034 galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 4 (DR4). Our sample includes nearly all spectroscopically-targeted galaxies in the redshift range 0.01<z<0.1 down…
In hierarchical models of galaxy formation, ellipticals form from the merging of disk galaxies drawn together by gravity as their surrounding dark halos coalesce. Using semi-analytic techniques, we are able to follow the merging, star…
We determine the underlying shapes of spiral and elliptical galaxies in the Sloan Digital Sky Survey Data Release 6 from the observed distribution of projected galaxy shapes, taking into account the effects of dust extinction and reddening.…
The increasing importance of digital sky surveys collecting many millions of galaxy images has reinforced the need for robust methods that can perform morphological analysis of large galaxy image databases. Citizen science initiatives such…
This chapter provides an overview of past and present techniques for optical detection of galaxy clusters. It follows the progression of cluster detection techniques through time, allowing readers to understand the development of the field…
We present a theoretical study of galaxy spin correlation statistics, with detailed technical derivations. We also find an expression for the spin-density cross-correlation, and apply that to the Tully galaxy catalog. The observational…
We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration $C_1$, Asymmetry $A_3$, and…
We present an automated morphological classification in 4 types (E,S0,Sab,Scd) of ~700.000 galaxies from the SDSS DR7 spectroscopic sample based on support vector machines. The main new property of the classification is that we associate to…
We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing…
We present radial tracks for four early-type galaxies with embedded intermediate-scale discs in a modified spin-ellipticity diagram. Here, each galaxy's spin and ellipticity profiles are shown as a radial track, as opposed to a single,…
Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen…
We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS…
Morphology is a powerful indicator of a galaxy's dynamical and merger history. It is strongly correlated with many physical parameters, including mass, star formation history and the distribution of mass. The Galaxy Zoo project collected…
Based on the Sloan Digital Sky Survey Data Release 5 Galaxy Sample, we explore photometric morphology classification and redshift estimation of galaxies using photometric data and known spectroscopic redshifts. An unsupervised method,…
Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these…
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…