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Using the 'k-means' cluster analysis algorithm, we carry out an unsupervised classification of all galaxy spectra in the seventh and final Sloan Digital Sky Survey data release (SDSS/DR7). Except for the shift to restframe wavelengths, and…
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…
The detection and characterization of filamentary structures in the cosmic web allows cosmologists to constrain parameters that dictates the evolution of the Universe. While many filament estimators have been proposed, they generally lack…
In Astronomy, a huge amount of image data is generated daily by photometric surveys, which scan the sky to collect data from stars, galaxies and other celestial objects. In this paper, we propose a technique to leverage unlabeled…
Encoder-Decoder networks such as U-Nets have been applied successfully in a wide range of computer vision tasks, especially for image segmentation of different flavours across different fields. Nevertheless, most applications lack of a…
We describe the application of data mining algorithms to research problems in astronomy. We posit that data mining has always been fundamental to astronomical research, since data mining is the basis of evidence-based discovery, including…
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,…
We present recent results from the LCDM (Laboratory for Cosmological Data Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the…
The advent of deep, wide, accurate, digital photometric surveys exemplified by the Sloan Digital Sky Survey (SDSS) has had a profound impact on studies of the Milky Way. In the past decade, we have transitioned from a scarcity to an…
Wide field images taken in several photometric bands allow simultaneous measurement of redshifts for thousands of galaxies. A variety of algorithms to make this measurement have appeared in the last few years, the majority of which can be…
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…
The goal of the Sloan Digital Sky Survey is ``to map in detail one-quarter of the entire sky, determining the positions and absolute brightnesses of more than 100 million celestial objects''. The survey will be performed by taking…
We summarize the plans for and the current status of the Sloan Digital Sky Survey, a digital imaging and spectroscopic survey of $\pi$ steradians in the northern Galactic cap. The CCD photometric survey will produce images in five bands to…
Semantic image segmentation is one of the most important tasks in medical image analysis. Most state-of-the-art deep learning methods require a large number of accurately annotated examples for model training. However, accurate annotation…
We present the results of a MOPED analysis of ~3 x 10^5 galaxy spectra from the Sloan Digital Sky Survey Data Release Three (SDSS DR3), with a number of improvements in data, modelling and analysis compared with our previous analysis of…
We describe the target selection and resulting properties of a spectroscopic sample of luminous, red galaxies (LRG) from the imaging data of the Sloan Digital Sky Survey (SDSS). These galaxies are selected on the basis of color and…
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…
Weak lensing is emerging as a powerful observational tool to constrain cosmological models, but is at present limited by an incomplete understanding of many sources of systematic error. Many of these errors are multiplicative and depend on…
Understanding the ages of stars is crucial for unraveling the formation history and evolution of our Galaxy. Traditional methods for estimating stellar ages from spectroscopic data often struggle with providing appropriate uncertainty…
Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited…