Related papers: Photometric Redshifts in JWST Deep Fields: A Pixel…
In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to…
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…
We present measurements of the redshift-dependent clustering of a DESI-like luminous red galaxy (LRG) sample selected from the Legacy Survey imaging dataset, and use the halo occupation distribution (HOD) framework to fit the clustering…
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…
Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness.…
We study the performance of the hybrid template-machine-learning photometric redshift (photo-$z$) algorithm Delight, which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey…
Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance-redshift relation. While obtaining a…
We present a method to refine photometric redshift galaxy catalogs by comparing their color-space matching with overlapping spectroscopic calibration data. We focus on cases where photometric redshifts (photo-$z$) are estimated empirically.…
The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-$z$ universe. Current and upcoming JWST imaging will potentially detect galaxies out to $z \sim 20$, creating a new urgency in the quest…
Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift…
The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the Universe, e.g. weak lensing and galaxy clustering. In…
We use the deepest and the most comprehensive photometric data currently available for GOODS-South galaxies to measure their photometric redshifts. The photometry includes VLT/VIMOS (U-band), HST/ACS (F435W, F606W, F775W, and F850LP bands),…
JWST has revolutionized the field of extragalactic astronomy with its sensitive and high-resolution infrared view of the distant universe. Adding to the new legacy of JWST observations, we present the first NIRCam imaging data release from…
Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot…
The effective redshift distribution $n(z)$ of galaxies is a critical component in the study of weak gravitational lensing. Here, we introduce a new method for determining $n(z)$ for weak lensing surveys based on high-quality redshifts and…
We present NIRCam and NIRISS modules for DOLPHOT, a widely-used crowded field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and…
The Near Infrared Imager and Slitless Spectrograph (NIRISS) on the James Webb Space Telescope (JWST) is a versatile instrument for collecting imaging and wide-field slitless spectroscopy (WFSS) data for surveys of galaxy clusters,…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
The aim of this paper is to investigate ways to optimize the accuracy of photometric redshifts for a SNAP like mission. We focus on how the accuracy of the photometric redshifts depends on the magnitude limit and signal-to-noise ratio,…
We use the mock catalog of galaxies, constructed based on the COSMOS galaxy catalog including information on photometric redshifts (photo-z) and SED types of galaxies, in order to study how to define a galaxy subsample suitable for weak…