Related papers: Probabilistic photo-z machine learning models for …
We develop a machine learning-based framework to predict the HI content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental parameters. We train the algorithm on z=0-2 outputs from the…
We employ deep learning (DL) to improve photometric redshifts (photo-$z$s) in the Kilo-Degree Survey Data Release 4 Bright galaxy sample (KiDS-Bright DR4). This dataset, used as a foreground for KiDS lensing and clustering studies, is…
We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the…
We provide a method for estimating the projected density distribution $\bar{n}_2w_p(r_p)$ of photometric objects around spectroscopic objects in a redshift survey. This quantity describes the distribution of Photometric sources with certain…
Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a…
Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based…
We present an empirical method for estimating the underlying redshift distribution N(z) of galaxy photometric samples from photometric observables. The method does not rely on photometric redshift (photo-z) estimates for individual…
[Abridged] We present reliable multiwavelength identifications and high-quality photometric redshifts for the 462 X-ray sources in the ~2 Ms Chandra Deep Field-South. Source identifications are carried out using deep optical-to-radio…
We apply Bayesian statistics with prior probabilities of galaxy surface luminosity (SL) to improve photometric redshifts. We apply the method to a sample of 1266 galaxies with spectroscopic redshifts in the GOODS North and South fields at…
We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a…
In this work, we studied the impact of galaxy morphology on photometric redshift (photo-$z$) probability density functions (PDFs). By including galaxy morphological parameters like the radius, axis-ratio, surface brightness and the S\'ersic…
We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The…
We show how to enhance the redshift accuracy of surveys consisting of tracers with highly uncertain positions along the line of sight. Photometric surveys with redshift uncertainty delta_z ~ 0.03 can yield final redshift uncertainties of…
Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…
We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…
The W-CDF-S and ELAIS-S1 fields will be two of the LSST Deep Drilling fields, but the availability of spectroscopic redshifts within these two fields is still limited on deg^2 scales. To prepare for future science, we use EAZY to estimate…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
We describe the construction of MegaZ-LRG, a photometric redshift catalogue of over one million luminous red galaxies (LRGs) in the redshift range 0.4 < z < 0.7 with limiting magnitude i < 20. The catalogue is selected from the imaging data…
The number of known, bright ($i<18$), high-redshift ($z>2.5$) QSOs in the Southern Hemisphere is considerably lower than the corresponding number in the Northern Hemisphere due to the lack of multi-wavelength surveys at $\delta<0$. Recent…
Accurate photometric redshifts for large samples of galaxies are among the main products of modern multiband digital surveys. Over the last decade, the Sloan Digital Sky Survey (SDSS) has become a sort of benchmark against which to test the…