Related papers: Using neural networks to estimate redshift distrib…

Accurately characterizing the redshift distributions of galaxies is essential for analysing deep photometric surveys and testing cosmological models. We present a technique to simultaneously infer redshift distributions and individual…

Quasar photometric redshifts are essential for studying cosmology and large-scale structures. However, their complex spectral energy distributions cause significant redshift-color degeneracy, limiting the accuracy of traditional methods. To…

The number of galaxies at a given flux as a function of the redshift, $z$, is derived when the $z$-distance relation is non-standard. In order to compare different models, the same formalism is also applied to the standard cosmology. The…

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial…

We present an improved photometric redshift estimator code, CuBAN$z$, that is publicly available at https://goo.gl/fpk90V}{https://goo.gl/fpk90V. It uses the back propagation neural network along with clustering of the training set, which…

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because…

The accurate estimation of photometric redshifts is crucial to many upcoming galaxy surveys, for example the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Almost all Rubin extragalactic and cosmological science requires…

The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets.…

In this work, we explore the potential of multi-domain multi-branch convolutional neural networks (CNNs) for identifying comparatively rare giant radio galaxies from large volumes of survey data, such as those expected for new-generation…

We present the first measurements of the Probability Distribution Function (PDF) of galaxy fluctuations in the VIMOS-VLT Deep Survey (VVDS) cone, covering 0.4x0.4 deg between 0.4<z<1.5. The second moment of the PDF, i.e. the rms…

We apply a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by…

Cosmology and galaxy evolution studies with LSST, \Euclid, and {\it Roman}, will require accurate redshifts for the detected galaxies. In this study, we present improved photometric redshift estimates for galaxies using a template library…

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…

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 extend the real-space mapping method developed in Shi et at. (2016) so that it can be applied to flux-limited galaxy samples. We use an ensemble of mock catalogs to demonstrate the reliability of this extension, showing that it allows…

Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric redshift uncertainties can result in large systematic errors.…

Data-driven approaches play a crucial role in space computing, and our paper focuses on analyzing data to learn more about celestial objects. Photometric redshift, a measure of the shift of light towards the red part of the spectrum, helps…

Obtaining accurate distributions of galaxy redshifts is a critical aspect of weak lensing cosmology experiments. One of the methods used to estimate and validate redshift distributions is apply weights to a spectroscopic sample so that…

We perform high resolution ray-tracing simulations to investigate probability distribution functions (PDFs) of lensing convergence, shear, and magnification on distant sources up to the redshift of $z_s=20$. We pay particular attention to…

We examine the impact of using photometric redshifts for studying the evolution of both the global galaxy luminosity function (LF) and that for different galaxy types. To this end we compare LFs obtained using photometric redshifts from the…