A blind test of photometric redshifts on ground-based data

A blind test of photometric redshifts on ground-based data

Bibliográfalaš dieđut
Váldodahkkit:	Hildebrandt, H, Wolf, C, Benitez, N
Materiálatiipa:	Journal article
Giella:	English
Almmustuhtton:	2008

Geahča maid

Bayesian photometric redshifts with empirical training sets
Dahkki: Wolf, C
Almmustuhtton: (2009)

CFHTLenS: Improving the quality of photometric redshifts with precision photometry
Dahkki: Hildebrandt, H, et al.
Almmustuhtton: (2011)

CFHTLenS: Improving the quality of photometric redshifts with precision photometry
Dahkki: Hildebrandt, H, et al.
Almmustuhtton: (2012)

Cosmology with photometric redshifts
Dahkki: Gomes, Z
Almmustuhtton: (2019)

Bayesian Photometric Redshifts for Weak Lensing Applications
Dahkki: Edmondson, E, et al.
Almmustuhtton: (2006)

Photometric redshifts for galaxies in the GOODS southern field
Dahkki: Mobasher, B, et al.
Almmustuhtton: (2004)

Photometric redshift estimation using Gaussian processes
Dahkki: Bonfield, D, et al.
Almmustuhtton: (2010)

MAPPING THE GALAXY COLOR–REDSHIFT RELATION: OPTIMAL PHOTOMETRIC REDSHIFT CALIBRATION STRATEGIES FOR COSMOLOGY SURVEYS
Dahkki: Masters, Daniel, et al.
Almmustuhtton: (2016)

Calibrating photometric redshifts with intensity mapping observations
Dahkki: Alonso, D, et al.
Almmustuhtton: (2017)

Measuring the transition to homogeneity with photometric redshift surveys
Dahkki: Alonso, D, et al.
Almmustuhtton: (2014)

Photometric redshifts for the Kilo-Degree Survey Machine-learning analysis with artificial neural networks
Dahkki: Bilicki, M, et al.
Almmustuhtton: (2018)

Euclid preparation: X. The Euclid photometric-redshift challenge
Dahkki: Desprez, G, et al.
Almmustuhtton: (2020)

Improving Photometric Redshift Estimates with Training Sample Augmentation
Dahkki: Irene Moskowitz, et al.
Almmustuhtton: (2024-01-01)

Photometric redshift of X-ray sources in the Chandra Deep Field-South
Dahkki: Zheng, W, et al.
Almmustuhtton: (2004)

Augmenting machine learning photometric redshifts with Gaussian mixture models
Dahkki: Hatfield, PW, et al.
Almmustuhtton: (2020)

UVCANDELS: Catalogs of Photometric Redshifts and Galaxy Physical Properties
Dahkki: Vihang Mehta, et al.
Almmustuhtton: (2024-01-01)

Tracing the sound horizon scale with photometric redshift surveys
Dahkki: Sánchez, E, et al.
Almmustuhtton: (2011)

Photometric selection of high-redshift type Ia supernova candidates
Dahkki: Sullivan, M, et al.
Almmustuhtton: (2006)

Photometric selection of high-redshift type Ia supernova candidates
Dahkki: Sullivan, M, et al.
Almmustuhtton: (2006)

Photometric redshifts for type Ia supernovae in the supernova legacy survey
Dahkki: Palanque-Delabrouille, N, et al.
Almmustuhtton: (2010)

Photometric redshifts for type Ia supernovae in the supernova legacy survey
Dahkki: Palanque-Delabrouille, N, et al.
Almmustuhtton: (2010)

Hybrid photometric redshifts for sources in the COSMOS and XMM-LSS fields
Dahkki: Hatfield, PW, et al.
Almmustuhtton: (2022)

A Sparse Gaussian Process Framework for Photometric Redshift Estimation
Dahkki: Almosallam, I, et al.
Almmustuhtton: (2015)

COSMOS2015 photometric redshifts probe the impact of filaments on galaxy properties
Dahkki: Laigle, C, et al.
Almmustuhtton: (2017)

The FENIKS Survey: Multiwavelength Photometric Catalog in the UDS Field, and Catalogs of Photometric Redshifts and Stellar Population Properties
Dahkki: Kumail Zaidi, et al.
Almmustuhtton: (2024-01-01)

PHOTOMETRY AND PHOTOMETRIC REDSHIFT CATALOGS FOR THE LOCKMAN HOLE DEEP FIELD
Dahkki: Fotopoulou, S, et al.
Almmustuhtton: (2012)

The Herschel Multi-tiered Extragalactic Survey: SPIRE-mm photometric redshifts
Dahkki: Roseboom, I, et al.
Almmustuhtton: (2012)

A Photometric Redshift of z ~ 9.4 for GRB 090429B
Dahkki: Cucchiara, A, et al.
Almmustuhtton: (2011)

Analytical marginalization over photometric redshift uncertainties in cosmic shear analyses
Dahkki: Ruiz-Zapatero, J, et al.
Almmustuhtton: (2023)

A photometric redshift of z = 6.39 +/- 0.12 for GRB 050904.
Dahkki: Haislip, J, et al.
Almmustuhtton: (2006)

Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case
Dahkki: Massimo Brescia, et al.
Almmustuhtton: (2021-06-01)

The 2-degree Field Lensing Survey: photometric redshifts from a large new training sample to r < 19.5
Dahkki: Wolf, C, et al.
Almmustuhtton: (2016)

Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models
Dahkki: Boris Leistedt, et al.
Almmustuhtton: (2023-01-01)

Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning
Dahkki: Chong De Wei, Kenny, et al.
Almmustuhtton: (2019-01-01)

Support Vector Machines for Photometric Redshift Estimation from Broadband Photometry
Dahkki: Dan Wang, et al.
Almmustuhtton: (2007-08-01)

Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method
Dahkki: Chen Zhang, et al.
Almmustuhtton: (2024-01-01)

A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17
Dahkki: Wolf, C, et al.
Almmustuhtton: (2004)

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST
Dahkki: Bingjie Wang, et al.
Almmustuhtton: (2023-01-01)

Accuracy of the Photometric Redshifts of Brightest Cluster Galaxies Identified in the CFHTLS-W1
Dahkki: Sinan Aliş
Almmustuhtton: (2024-12-01)

Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks
Dahkki: Evan Jones, et al.
Almmustuhtton: (2024-01-01)