Related papers: Optimal multihump filter for photometric redshifts

Recent extensive, multi-color deep surveys of galaxies open a possibility to get observational estimation of sizes for the largest structures in the Universe. Photometric redshift accuracy (about 0.03(1+z)) allows directly study clustering…

Filament finders are limited, among other things, by the abundance of spectroscopic redshift data. As there are proportionally more photometric redshift data than spectroscopic, we aim to use photometric data to improve and expand the areas…

We present a method to simulate color, 3-dimensional images taken with a space-based observatory by building off of the established shapelets pipeline. The simulated galaxies exhibit complex morphologies, which are realistically correlated…

The Cluster Lensing And Supernovae survey with Hubble (CLASH) is an Hubble Space Telescope (HST) Multi-Cycle Treasury program observing 25 massive galaxy clusters. CLASH observations are carried out in 16 bands from UV to NIR to derive…

Measuring distances of cosmological sources such as galaxies, stars and quasars plays an increasingly critical role in modern cosmology. Obtaining the optical spectrum and consequently calculating the redshift as a distance indicator could…

We investigate the accuracy of 4000\AA/Balmer-break based redshifts by combining Hubble Space Telescope ({\it HST}) grism data with photometry. The grism spectra are from the Probing Evolution And Reionization Spectroscopically (PEARS)…

Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to…

Deep near-infrared imaging surveys allow us to select and study distant galaxies in the rest-frame optical, and have transformed our understanding of the early Universe. As the vast majority of K- or IRAC-selected galaxies is too faint for…

The combination of deep exposures and high resolution offered by telescopes in space allows the detection of lensing over a wide range of source redshifts and lens masses. As an example, we model a lens candidate found in the southern…

We present redshifts for 115 emission line objects in the Hubble Ultra Deep Field (HUDF) identified through the GRism ACS Program for Extragalactic Science (GRAPES) project using the slitless grism spectroscopy mode of the ACS Camera on the…

Photometric redshifts (photo-$z$'s) will be crucial for studies of galaxy evolution, large-scale structure, and transients with the Nancy Grace Roman Space Telescope. Deep learning methods leverage pixel-level information from ground-based…

We present positions, magnitudes, sizes and morphological classifications for 111 galaxies discovered in the Hubble Space Telescope NICMOS Camera 1 and Camera 2 parallel fields. We combine the magnitudes measured in the JHK-analog filters…

Photometric galaxy surveys are an essential tool to further our understanding of the large-scale structure of the universe, its matter and energy content and its evolution. These surveys necessitate the determination of the galaxy redshifts…

Photometric redshifts (photo-$z$'s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Rubin Observatory, and the Nancy Grace Roman Space…

Given multiband photometric data from the SDSS DR6, we estimate galaxy redshifts. We employ a Random Forest trained on color features and spectroscopic redshifts from 80,000 randomly chosen primary galaxies yielding a mapping from color to…

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper…

Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation…

Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between…

We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multi-wavelength datasets, over the NOAO Deep Wide Field Survey Bo\"otes and COSMOS fields,…

We investigate the expected accuracy of redshifts that can be obtained using low-resolution spectroscopic (medium-band) data from the 7-Dimensional Sky Survey (7DS). By leveraging 40 densely sampled filters with widths of full width at half…