Related papers: Photometric redshift estimation based on data mini…
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…
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…
In Lima et al. 2008 we presented a new method for estimating the redshift distribution, N(z), of a photometric galaxy sample, using photometric observables and weighted sampling from a spectroscopic subsample of the data. In this paper, we…
Current and future weak lensing surveys will rely on photometrically estimated redshifts of very large numbers of galaxies. In this paper, we address several different aspects of the demanding photo-z performance that will be required for…
In this paper we study the accuracy of photometric redshifts computed through a standard SED fitting procedure, where SEDs are obtained from broad-band photometry. We present our public code hyperz, which is presently available on the web.…
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…
We present a new method for obtaining photometric redshifts (photo-z) for sources observed by multiple photometric surveys using a combination (conflation) of the redshift probability distributions (PDZs) obtained independently from each…
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…
We present a robust method to estimate the redshift of galaxies using Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of 2313724…
The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on…
Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses. In this work, we investigate two major forms of…
Large imaging surveys will rely on photometric redshifts (photo-z's), which are typically estimated through machine learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training…
Accurate photometric redshift (photo-$z$) estimation is a key challenge in cosmology, as uncertainties in photo-$z$ directly limit the scientific return of large-scale structure and weak lensing studies, especially in upcoming Stage IV…
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…
The Chinese Space Station Optical Survey (CSS-OS) is a major science project of the Space Application System of the China Manned Space Program. This survey is planned to perform both photometric imaging and slitless spectroscopic…
We present a novel method capable of creating optimal eigenspectra from multicolor redshift surveys for photometric redshift estimation. Our iterative training algorithm modifies the templates to represent the photometric measurements…
In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts.…
(Abridged) In this paper we present photometric redshift estimates for a sample of X-ray selected sources detected in the wide field (~2 deg^2), bright [f_{X} (0.5-8 keV)~10^{-14} cgs] XMM-Newton/2dF survey. Unlike deeper X-ray samples…
We present a method for mapping variations between probability distribution functions and apply this method within the context of measuring galaxy redshift distributions from imaging survey data. This method, which we name PITPZ for the…
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…