Related papers: Photometric redshift estimation via deep learning
The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet those…
Diffusion Probabilistic Methods are employed for state-of-the-art image generation. In this work, we present a method for extending such models for performing image segmentation. The method learns end-to-end, without relying on a…
Large-scale surveys make huge amounts of photometric data available. Because of the sheer amount of objects, spectral data cannot be obtained for all of them. Therefore it is important to devise techniques for reliably estimating physical…
The Early Data Release from the Sloan Digital Sky survey provides one of the largest multicolor photometric catalogs currently available to the astronomical community. In this paper we present the first application of photometric redshifts…
Photometric redshift (photo-z) estimates are playing an increasingly important role in extragalactic astronomy and cosmology. Crucial to many photo-z applications is the accurate quantification of photometric redshift errors and their…
We introduce a framework for the enhanced estimation of photometric redshifts using Self-Organising Maps (SOMs). Our method projects galaxy Spectral Energy Distributions (SEDs) onto a two-dimensional map, identifying regions that are…
In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic…
We present a neural network based approach to the determination of photometric redshift. The method was tested on the Sloan Digital Sky Survey Early Data Release (SDSS-EDR) reaching an accuracy comparable and, in some cases, better than SED…
Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable performances. However, the existing works only consider context dependencies in one…
The Wide Field Survey Telescope (WFST) is a dedicated time-domain multi-band ($u$, $g$, $r$, $i$, and $z$) photometric survey facility under construction. In this paper, we present a preliminary study that assesses the quality of…
In the present study, we use the DES Y3 catalog of LRG to incorporate the realistic galaxies' redshift Probability Distribution Function(PDF) into the correlation function cosmological model. We used four different photo-z estimators ANNz2,…
Photometric redshifts are estimated on the basis of template scenarios with the help of the code ZPEG, an extension of the galaxy evolution model PEGASE.2 and available on the PEGASE web site. The spectral energy distribution (SED)…
In this paper we present photometric redshifts for 2.7 million galaxies in the XMM-LSS and COSMOS fields, both with rich optical and near-infrared data from VISTA and HyperSuprimeCam. Both template fitting (using galaxy and Active Galactic…
Measuring the physical properties of galaxies such as redshift frequently requires the use of Spectral Energy Distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space.…
We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large numbers of galaxy spectral templates into a corrresponding collection of "fuzzy archetypes"…
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…
Data limitation is a significant challenge in applying deep learning to medical images. Recently, the diffusion probabilistic model (DPM) has shown the potential to generate high-quality images by converting Gaussian random noise into…
Powerful current and future cosmological constraints using high precision measurements of the large-scale structure of galaxies and its weak gravitational lensing effects rely on accurate characterization of the redshift distributions of…
Robust Bayesian inference using density power divergence (DPD) has emerged as a promising approach for handling outliers in statistical estimation. Although the DPD-based posterior offers theoretical guarantees of robustness, its practical…
A new method is developed for estimating photometric redshifts, using realistic template SEDs, extending over four decades in wavelength (i.e from 0.05 $\mu m$ to 1 mm). The SEDs are constructed for four different spectral types of galaxies…