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Novel Methods for Predicting Photometric Redshifts

Metadata Updated: December 7, 2023

We calculate photometric redshifts from the Sloan Digital Sky Survey Main Galaxy Sample, The Galaxy Evolution Explorer All Sky Survey, and The Two Micron All Sky Survey using two new training-set methods. We utilize the broad-band photometry from the three surveys alongside Sloan Digital Sky Survey measures of photometric quality and galaxy morphology. Our first training-set method draws from the theory of ensemble learning while the second employs Gaussian process regression both of which allow for the estimation of redshift along with a measure of uncertainty in the estimation. The Gaussian process models the data very effectively with small training samples of approximately 1000 points or less. These two methods are compared to a well known Artificial Neural Network training-set method and to simple linear and quadratic regression. We also demonstrate the need to provide confidence bands on the error estimation made by both classes of models. Our results indicate that variations due to the optimization procedure used for almost all neural networks, combined with the variations due to the data sample, can produce models with variations in accuracy that span an order of magnitude. A key contribution of this paper is to quantify the variability in the quality of results as a function of model and training sample. We show how simply choosing the ``best" model given a data set and model class can produce misleading results. We also investigate supplemental information provided by the Sloan Digital Sky Survey photometric pipeline related to photometric quality and galaxy morphology tracers. We show that, using these additional quality and morphology indicators rather than only the Sloan Digital Sky Survey broad-band u,g,r,i,z imaging data commonly used, one can improve redshift accuracy by 10s of percent. Near Infrared LaTeX broad-band photometry provided from the Two Micron All Sky Survey and near-ultraviolet and far-ultraviolet broad-band data from The Galaxy Evolution Explorer All Sky Survey are also investigated where they overlap with the Sloan Digital Sky Survey. Our results show that robust photometric redshift errors as low as 0.02 RMS can regularly be obtained. We believe these can be expanded to other photometric surveys where sufficient redshift calibration objects exist.

Access & Use Information

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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Dates

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
Maintainer
Identifier DASHLINK_150
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 https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id dfcd7d78-4f3c-40fa-9c5e-7acefde2498a
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://c3.nasa.gov/dashlink/resources/150/
Program Code 026:029
Source Datajson Identifier True
Source Hash 8ddb6e3b443db4c6a82c55ddc20e832539f971897a3937a028377daa051bd450
Source Schema Version 1.1

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