Related papers: Optical Wavelength Guided Self-Supervised Feature …
Building a comprehensive catalog of galaxy clusters is a fundamental task for the studies on the structure formation and galaxy evolution. In this paper, we present COSMIC (Cluster Optical Search using Machine Intelligence in Catalogs), an…
We present a metric to quantify systematic labeling bias in galaxy morphology data sets stemming from the quality of the labeled data. This labeling bias is independent from labeling errors and requires knowledge about the intrinsic…
In this contribution the ongoing effort to build a statistical sample of clusters of galaxies over a wide range of redshifts to study the evolution of clusters and member galaxies is reviewed. The starting point for this project has been…
The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
This paper, the third in a series, investigates the scaling relations between optical richness, weak-lensing mass, and Compton $Y$ for a sample of galaxy clusters selected purely by the effect of their gravitational potential on the shapes…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
Cosmological constraints from current and upcoming galaxy cluster surveys are limited by the accuracy of cluster mass calibration. In particular, optically identified galaxy clusters are prone to selection effects that can bias the weak…
Fine-grained image classification involves identifying different subcategories of a class which possess very subtle discriminatory features. Fine-grained datasets usually provide bounding box annotations along with class labels to aid the…
We present weak lensing mass estimates for a sample of 458 galaxy clusters from the redMaPPer Sloan Digital Sky Survey DR8 catalogue using Hyper Suprime-Cam weak lensing data. We develop a method to quickly estimate cluster masses from weak…
We describe ten strong lensing galaxy clusters of redshift 0.26-0.56 that were found in the Sloan Digital Sky Survey. We present measurements of richness, mass and velocity dispersion for the clusters. We find that in order to use the…
We have measured the surface density of galaxies toward 14 X-ray-selected cluster candidates at redshifts greater than z=0.46, and we show that they are associated with rich galaxy concentrations. We find that the clusters range between…
We present cosmology results obtained from a blind joint analysis of the abundance, projected clustering, and weak lensing of galaxy clusters measured from the Sloan Digital Sky Survey (SDSS) redMaPPer cluster catalog and the Hyper-Suprime…
We describe an objective and automated method for detecting clusters of galaxies from optical imaging data. This method is a variant of the so-called `matched-filter' technique pioneered by Postman et al. (1996). With simultaneous use of…
We develop a new method of combining cluster observables (number counts and cluster-cluster correlation functions) and stacked weak lensing signals of background galaxy shapes, both of which are available in a wide-field optical imaging…
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of complex data is…
The task of large-scale retrieval-based image localization is to estimate the geographical location of a query image by recognizing its nearest reference images from a city-scale dataset. However, the general public benchmarks only provide…
Weak gravitational lensing signals of optically identified clusters are impacted by a selection bias -- halo triaxiality and large-scale structure along the line of sight simultaneously boost the lensing signal and richness (the inferred…
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…