Text · dataset · 2025
New Approaches To Photometric Redshift Prediction
Listed in NASA Data Portal
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.
Description
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.
Links
Get the data
- photometric_redshiift_with_GPR.pdf c3.nasa.gov/dashlink/static/media/publication/photometric_redshiift_with_G… ↗
download · from data nasa gov
Where it is published
- NASA Data Portal dataset page data.nasa.gov/dataset/new-approaches-to-photometric-redshift-prediction ↗
landing page · from data nasa gov
Catalogue records · 1
- CKAN API data.nasa.gov/api/3/action/package_show?id=f64dae47-7079-46f7-b03e-365784229… ↗
metadata API · from data nasa gov
Provenance · 1 source records, 6 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NASA Data Portal | f64dae47-7079-46f7-b03e-365784229644 | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| created_date | source · data nasa gov | connector:data_nasa_gov@1.0.0 | |
| description | source · data nasa gov | connector:data_nasa_gov@1.0.0 | /notes |
| license_text | source · data nasa gov | connector:data_nasa_gov@1.0.0 | |
| publication_date | source · data nasa gov | connector:data_nasa_gov@1.0.0 | |
| title | source · data nasa gov | connector:data_nasa_gov@1.0.0 | /title |
| updated_date | source · data nasa gov | connector:data_nasa_gov@1.0.0 |