Related papers: Unsupervised classification of SDSS galaxy spectra
We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy…
The size-mass galaxy distribution is a key diagnostic for galaxy evolution. Massive compact galaxies are potential surviving relics of a high-redshift phase of star formation. Some of these could be nearly unresolved in SDSS imaging and…
Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with…
Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the era of large-scale sky surveys is that the bright galaxies that are selected for spectroscopic observation…
A sample of nearly 9000 early-type galaxies, in the redshift range 0.01 < z < 0.3, was selected from the Sloan Digital Sky Survey using morphological and spectral criteria. This paper describes how the sample was selected, presents examples…
We present a sample of 383 X-ray selected galaxy groups and clusters with spectroscopic redshift measurements (up to z ~ 0.79) from the 2XMMi/SDSS Galaxy Cluster Survey. The X-ray cluster candidates were selected as serendipitously detected…
Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific…
Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived…
The K20 survey is a near infrared-selected, deep (Ks < 20) redshift survey targeting galaxies in two independent regions of the sky, the CDFS and the q0055-2659 field. The total Ks-selected sample includes 545 objects. Optical spectra for…
With the advent of digital astronomy, new benefits and new problems have been presented to the modern day astronomer. While data can be captured in a more efficient and accurate manor using digital means, the efficiency of data retrieval…
In order to study the galaxy population of galaxy clusters with photometric data one must be able to accurately discriminate between cluster members and non-members. The redMaPPer cluster finding algorithm treats this problem…
Measuring the physical properties of galaxies such as redshift frequently requires the use of Spectral Energy Distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space.…
The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include citizen science campaigns adopted by the Sloan Digital Sky Survey (SDSS). SDSS…
We study the spectral classification of emission-line galaxies as star-forming galaxies or Active Galactic Nuclei (AGNs). From the Sloan Digital Sky Survey (SDSS) high quality data, we define an improved classification to be used for high…
The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral…
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…
This paper presents the identification of galaxy clusters from the photometric redshift catalog based on three imaging surveys of SCUSS, SDSS, and unWISE. By applying a fast clustering algorithm, we obtain a total of 19,610 clusters in the…
Galaxies and clusters embedded in the large-scale structure of the Universe are observed to align in preferential directions. Galaxy alignment has been established as a potential probe for cosmological information, but the application of…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
A new method for classification of galaxy spectra is presented, based on a recently introduced information theoretical principle, the `Information Bottleneck'. For any desired number of classes, galaxies are classified such that the…