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Determining photometric redshifts to high accuracy is paramount to measure distances in wide-field cosmological experiments. With only photometric information at hand, photo-zs are prone to systematic uncertainties in the intervening…

宇宙学与河外天体物理 · 物理学 2021-06-16 Z. Ansari , A. Agnello , C. Gall

Galaxies whose images overlap in the focal plane of a telescope, commonly referred to as blends, are often located at different redshifts. Blending introduces a challenge to weak-lensing cosmology probes since such blends are subject to…

宇宙学与河外天体物理 · 物理学 2026-02-18 Zekang Zhang , Daniel Gruen , Luca Tortorelli , Shun-Sheng Li , Jamie McCullough

We release photometric redshifts, reaching $\sim$0.7, for $\sim$14M galaxies at $r\leq 20$ in the 11,500 deg$^2$ of the SDSS north and south galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on…

宇宙学与河外天体物理 · 物理学 2023-10-16 M. Treyer , R. Ait-Ouahmed , J. Pasquet , S. Arnouts , E. Bertin , D. Fouchez

Filament finders are limited, among other things, by the abundance of spectroscopic redshift data. As there are proportionally more photometric redshift data than spectroscopic, we aim to use photometric data to improve and expand the areas…

宇宙学与河外天体物理 · 物理学 2023-02-15 Moorits Mihkel Muru , Elmo Tempel

We use N-body/photometric galaxy simulations to examine the impact of sample variance of spectroscopic redshift samples on the accuracy of photometric redshift (photo-z) determination and calibration of photo-z errors. We estimate the…

宇宙学与河外天体物理 · 物理学 2014-12-02 Carlos E. Cunha , Dragan Huterer , Michael T. Busha , Risa H. Wechsler

Wide, deep photometric surveys require robust photometric redshift estimates (photo-z's) for studies of large-scale structure. These estimates depend critically on accurate photometry. We describe the improvements to the photometric…

宇宙学与河外天体物理 · 物理学 2015-06-12 Samuel J. Schmidt , Paul Thorman

We examine the utility of very high redshift Type Ia supernovae for cosmology and systematic uncertainty control. Next generation space surveys such as the Supernova/Acceleration Probe (SNAP) will obtain thousands of supernovae at z>1.7,…

天体物理学 · 物理学 2009-11-11 Greg Aldering , Alex G. Kim , Marek Kowalski , Eric V. Linder , Saul Perlmutter

By means of CCD photometry in three bands (Gunn g, r, i) we investigate the existence of 12 candidate clusters extracted via a model independent peak finding algorithm (\cite{memsait}) from DPOSS data. The derived color-magnitude diagrams…

天体物理学 · 物理学 2009-11-07 E. Puddu , S. Andreon , G. Longo , V. Strazzullo , M. Paolillo , R. Gal

We perform a rigorous cosmology analysis on simulated type Ia supernovae (SN~Ia) and evaluate the improvement from including photometric host-galaxy redshifts compared to using only the "zspec" subset with spectroscopic redshifts from the…

宇宙学与河外天体物理 · 物理学 2023-03-08 Ayan Mitra , Richard Kessler , Surhud More , Renee Hlozek , The LSST Dark Energy Science Collaboration

A method for using the sub-millimeter band to determine photometric redshifts of luminous high-z dusty galaxies is presented. It is based on the observation that local ultra-luminous IR galaxies show an average spectral energy distribution…

天体物理学 · 物理学 2007-05-23 Tommy Wiklind

We present a method, PhotoWeb, for estimating photometric redshifts of individual galaxies, and their equivalent distance, with megaparsec and even sub-megaparsec accuracy using the Cosmic Web as a constraint over photo-z estimates.…

宇宙学与河外天体物理 · 物理学 2015-09-30 Miguel A. Aragon-Calvo , Rien van de Weygaert , Bernard J. T. Jones , Bahram Mobasher

Forthcoming astronomical surveys are expected to detect new sources in such large numbers that measuring their spectroscopic redshift measurements will be not be practical. Thus, there is much interest in using machine learning to yield the…

宇宙学与河外天体物理 · 物理学 2022-03-14 S. J. Curran

We investigate how well the redshift distributions of galaxies sorted into photometric redshift bins can be determined from the galaxy angular two-point correlation functions. We find that the uncertainty in the reconstructed redshift…

天体物理学 · 物理学 2008-11-26 M. Schneider , L. Knox , H. Zhan , A. Connolly

We use multi-band optical and near-infrared photometric observations of galaxies in the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) to predict photometric redshifts using artificial neural networks. The…

星系天体物理 · 物理学 2020-01-15 Derek Wilson , Hooshang Nayyeri , Asantha Cooray , Boris Häußler

We describe our application of broad-band photometric redshift techniques to faint galaxies in the Hubble Deep Field. To magnitudes AB(8140) < 26, the accuracy of the photometric redshifts is a few tenths and the reliability of the…

天体物理学 · 物理学 2007-05-23 Kenneth M. Lanzetta , Alberto Fernandez-Soto , Amos Yahil

We present Mantis Shrimp, a multi-survey deep learning model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. Machine learning is now an established approach for…

天体物理仪器与方法 · 物理学 2025-08-01 Andrew Engel , Nell Byler , Adam Tsou , Gautham Narayan , Emmanuel Bonilla , Ian Smith

I present a new approach at deriving far-infrared photometric redshifts for galaxies based on their reprocessed emission from dust at rest-frame far-infrared through millimeter wavelengths. Far-infrared photometric redshifts ("FIR-$z$")…

星系天体物理 · 物理学 2020-09-09 Caitlin M. Casey

We describe a new method of combining optical and infrared photometry to select Luminous Red Galaxies (LRGs) at redshifts $z > 0.6$. We explore this technique using a combination of optical photometry from CFHTLS and HST, infrared…

星系天体物理 · 物理学 2015-04-27 Abhishek Prakash , Timothy C. Licquia , Jeffrey A. Newman , Sandhya M. Rao

Galaxy photometric redshift (photo-$z$) is crucial in cosmological studies, such as weak gravitational lensing and galaxy angular clustering measurements. In this work, we try to extract photo-$z$ information and construct its probability…

宇宙学与河外天体物理 · 物理学 2022-11-16 Xingchen Zhou , Yan Gong , Xian-Min Meng , Xuelei Chen , Zhu Chen , Wei Du , Liping Fu , Zhijian Luo
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