Related papers: Augmenting photometric redshift estimates using sp…
Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
Cross-correlations between the galaxy number density in a lensing source sample and that in an overlapping spectroscopic sample can in principle be used to calibrate the lensing source redshift distribution. In this paper, we study in…
The next generation of large scale imaging surveys (such as those conducted with the Large Synoptic Survey Telescope and Euclid) will require accurate photometric redshifts in order to optimally extract cosmological information. Gaussian…
Context: In astronomy, new approaches to process and analyze the exponentially increasing amount of data are inevitable. While classical approaches (e.g. template fitting) are fine for objects of well-known classes, alternative techniques…
Measuring the evolution in the clustering of galaxies over a large redshift range is a challenging problem. For a two-dimensional galaxy catalog, however, we can measure the galaxy-galaxy angular correlation function which provides…
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed, based either on template models fitting or on empirical…
Determining the redshift distribution $n(z)$ of galaxy samples is essential for several cosmological probes including weak lensing. For imaging surveys, this is usually done using photometric redshifts estimated on an object-by-object…
Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the era of large-scale sky surveys is that the bright galaxies that are selected for spectroscopic observation…
We show how the cosmological constant can be estimated from redshift surveys at different redshifts, using maximum-likelihood techniques. The apparent redshift-space clustering on large scales (\simgt 20 \himpc) are affected in the radial…
We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep…
Accurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and…
Large direct-imaging surveys usually use a template-fitting technique to estimate photometric redshifts for galaxies, which are then applied to derive important galaxy properties such as luminosities and stellar masses. These estimates can…
We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1…
In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural…
A leading way to constrain physical theories from cosmological observations is to test their predictions for the angular clustering statistics of matter tracers, a technique that is set to become ever more central with the next generation…
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,…
Determining the radial positions of galaxies up to a high accuracy depends on the correct identification of salient features in their spectra. Classical techniques for spectroscopic redshift estimation make use of template matching with…
Accurate weak lensing mass estimates of clusters are needed in order to calibrate mass proxies for the cosmological exploitation of galaxy cluster surveys. Such measurements require accurate knowledge of the redshift distribution of the…
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…