Related papers: Optimizing Redshift Distribution Inference through…
The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on…
Recent detection of high-redshift, massive clusters through Sunyaev-Zel'dovich observations has opened up a new way to test cosmological models. It is known that detection of a single supermassive cluster at a very high redshift can rule…
Interloper contamination due to line misidentification is an important issue in the future low-resolution spectroscopic surveys. We realize that the algorithm previously used for photometric redshift self-calibration, with minor…
We use simulations to demonstrate that photometric redshift "errors" can be greatly reduced by using the photometric redshift probability distribution p(z) rather than a one-point estimate such as the most likely redshift. In principle this…
Objective-The main purpose of this paper is to construct a distributed clustering algorithm such that each distributed cluster can perform the data accuracy at their respective cluster head node before data aggregation and transmit the data…
We present a new cluster detection algorithm designed for finding high-redshift clusters using optical/infrared imaging data. The algorithm has two main characteristics. First, it utilises each galaxy's full redshift probability function,…
Accurate photometric redshift (photo-$z$) estimation requires support from multi-band observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in…
The wide-area imaging surveys with the {\it Herschel} Space Observatory at sub-mm wavelengths have now resulted in catalogs of order one hundred thousand dusty, star-burst galaxies. We make a statistical estimate of $N(z)$ using a…
Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating…
We present a neural network classification (NNC) method for photometric redshift estimation that produces well-calibrated redshift probability density functions (PDFs). The method discretizes the redshift space into ordered bins and…
We present the redshift calibration methodology and bias estimates for the cosmic shear analysis of the fifth and final data release (DR5) of the Kilo-Degree Survey (KiDS). KiDS-DR5 includes a greatly expanded compilation of calibrating…
We describe the derivation and validation of redshift distribution estimates and their uncertainties for the galaxies used as weak lensing sources in the Dark Energy Survey (DES) Year 1 cosmological analyses. The Bayesian Photometric…
We discuss a method to build up and study a sample of distant galaxies, with 2<z<5, using the gravitational amplification effect in cluster-lenses for which the mass distribution is well known. The candidates are selected close to the…
We describe a method for measuring the integrated Comptonization (YSZ) of clusters of galaxies from measurements of the Sunyaev-Zel'dovich (SZ) effect in multiple frequency bands and use this method to characterize a sample of galaxy…
Super-sample covariance (SSC) is the dominant source of statistical error on large scale structure (LSS) observables for both current and future galaxy surveys. In this work, we concentrate on the SSC of cluster counts, also known as sample…
Numerical simulations in cosmology require trade-offs between volume, resolution and run-time that limit the volume of the Universe that can be simulated, leading to sample variance in predictions of ensemble-average quantities such as the…
Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dramatically when handling incomplete high-dimensional data…
Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space. This core demand inspires the exploration of contrastive learning and…
We study the clustering of galaxies detected at $i<22.5$ in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using $2.3\times 10^6$ galaxies over a contiguous 116 deg$^2$…
We investigate the use of simple colour cuts applied to the SDSS optical imaging to perform photometric selections of emission line galaxies out to z<1. From colour-cuts using the SDSS g, r and i bands, we obtain mean photometric redshifts…