Related papers: The SDSS Coadd: A Galaxy Photometric Redshift Cata…
We present a photometric redshift (photo-$z$) estimation technique for galaxies in the P\lowercase{an}-STARRS1 (PS1) $3\pi $ survey. Specifically, we train and test a regression and a classification Random-Forest (RF) models using…
We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes…
Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time (LSST) are all critically dependent on estimates of…
We have applied ClassX, an oblique decision tree classifier optimized for astronomical analysis, to the homogeneous multicolor imaging data base of the Sloan Digital Sky Survey (SDSS), training the software on subsets of SDSS objects whose…
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-$z$'s), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-$z$. We…
Galaxy photometric redshift (photo-$z$) is crucial in cosmological studies, such as weak gravitational lensing and galaxy angular clustering measurements. In this work, we try to extract photo-$z$ information and construct its probability…
In the next decade, the LSST will become a major facility for the astronomical community. However accurately determining the redshifts of the observed galaxies without using spectroscopy is a major challenge. Reconstruction of the redshifts…
We present photometric redshifts for 1 341 559 galaxies from the Physics of the Accelerating Universe Survey (PAUS) over 50.38 ${\rm deg}^{2}$ of sky to $i_{\rm AB}=23$. Redshift estimation is performed using DEEPz, a deep-learning…
We present photometric redshifts and spectral energy distribution (SED) classifications for a sample of 1542 optically identified sources detected with XMM in the COSMOS field. Our template fitting classifies 46 sources as stars and 464 as…
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…
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…
Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. Photometric redshifts can be efficiently…
We present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods -- ANNZ2, BPZ…
Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is…
We present a catalogue of photometric redshifts for galaxies from DESI Legacy Imaging Surveys, which includes $\sim0.18$ billion sources covering 14,000 ${\rm deg}^2$. The photometric redshifts, along with their uncertainties, are estimated…
We present a set of low resolution empirical SED templates for AGNs and galaxies in the wavelength range from 0.03 to 30 microns based on the multi-wavelength photometric observations of the NOAO Deep-Wide Field Survey Bootes field and the…
We present a new approach, kernel regression, to determine photometric redshifts for 399,929 galaxies in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS). In our case, kernel regression is a weighted average of spectral…
In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various…
We use multi-band optical and near-infrared photometric observations of galaxies in the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) to predict photometric redshifts using artificial neural networks. The…
We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the…