Related papers: Separating the blue cloud and the red sequence usi…
We present a method to study the photometric properties of galaxies in filaments by stacking the galaxy populations between pairs of galaxy clusters. Using Sloan Digital Sky Survey data, this method can detect the inter-cluster filament…
We present a novel method to estimate accurate redshifts of star-forming galaxies by measuring the flux ratio of the same emission line observed through two adjacent narrow-band filters. We apply this method to our NB912 and new NB921 data…
Although the optical colour-magnitude diagram of galaxies allows one to select red sequence objects, neither can it be used for galaxy classification without additional observational data such as spectra or high-resolution images, nor to…
We propose a new technique, which we call the lens parallax method, to determine simultaneously the redshift distribution of the faint blue galaxies and the mass distributions of foreground clusters of galaxies. The method is based on…
Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels…
We use the Herschel-ATLAS survey to conduct the first large-scale statistical study of the submm properties of optically selected galaxies. Using ~80,000 r-band selected galaxies from 126 deg^2 of the GAMA survey, we stack into submm…
We select 25,000 galaxies from the NEWFIRM Medium Band Survey (NMBS) to study the rest-frame U-V color distribution of galaxies at 0 < z < 2.5. The five unique NIR filters of the NMBS enable the precise measurement of photometric redshifts…
Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to…
We describe a new method of combining optical and infrared photometry to select Luminous Red Galaxies (LRGs) at redshifts $z > 0.6$. We explore this technique using a combination of optical photometry from CFHTLS and HST, infrared…
The galaxies found in optical surveys fall in two distinct regions of a diagram of optical colour versus absolute magnitude: the red sequence and the blue cloud with the green valley in between. We show that the galaxies found in a…
Using the photometric population prediction method {\bf Red Dragon}, we characterize the Red Sequence (RS) and Blue Cloud (BC) of DES galaxies in the COSMOS field. Red Dragon (RD) uses a redshift-evolving, error-corrected Gaussian mixture…
The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is…
Modern astronomical surveys produce millions of light curves of variable sources. These massive data sets challenge the community to create automatic light-curve processing methods for detection, classification, and characterisation of…
We have analyzed the bivariate distribution of galaxies as a function of ultraviolet-optical colors and absolute magnitudes in the local universe. The sample consists of galaxies with redshifts and optical photometry from the Sloan Digital…
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…
A method of obtaining approximate redshifts and spectral types of galaxies using a photometric system of six broad-bandpass filters is developed. The technique utilizes a smallest maximum difference approach rather than a least-squares…
Precision-era optical cluster cosmology calls for a precise definition of the red sequence (RS), consistent across redshift. To this end, we present the Red Dragon algorithm: an error-corrected multivariate Gaussian mixture model (GMM).…
Rapid developments in swarm intelligence optimizers and computer processing abilities make opportunities to design more accurate, stable, and comprehensive methods for color image segmentation. This paper presents a new way for unsupervised…
We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural…
We develop a straightforward and quantitative two-step method for spectroscopically classifying galaxies from the low signal-to-noise (S/N) optical spectra typical of galaxy redshift surveys. First, using \chi^2-fitting of characteristic…