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New Approaches To Photometric Redshift Prediction

Metadata Updated: December 7, 2023

Expanding upon the work of Way & Srivastava (2006) we demonstrate how the use of training sets of comparable size continue to make Gaussian Process Regression a competitive approach to that of Neural Networks and other least squares fitting methods. This is possible via new large size matrix inversion techniques developed for Gaussian Processes that do not require that the kernel matrix be sparse. This development, combined with a neural-network kernel function appears to give superior results for this problem. Our best t results for the Sloan Digital Sky Survey Main Galaxy Sample using u,g,r,i,z gives an rms error of 0.0201 while our results for the same in the Luminous Red Galaxy Sample yield 0.0220.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Publisher Dashlink
Identifier DASHLINK_166
Data First Published 2010-09-22
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 502faad8-d51b-425a-b160-7c99788a4eda
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:029
Source Datajson Identifier True
Source Hash 54d837462b560dac2741db53f953a463b2680ad4fb8421ac5e58a0306bdb821a
Source Schema Version 1.1

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