arXiv (All)
open access
Optimizing Photometric Redshift Training Sets I: Efficient Compression of the Galaxy Color-Redshift Relation with UMAP
Spectroscopic datasets are essential for training and calibrating photometric redshift (photo-$z$) methods. However, spectroscopic redshifts (spec-$z$'s) constitute a biased and sparse sampling of the photometric galaxy population, which creates difficulties for the common grid-based approach for ma
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- CECILIA: Using Flexible Photoionization Models to Accurately Measure Abundances without Auroral Lines#1037171
- JWST Advanced Deep Extragalactic Survey Data Release 5: Photometrically Selected Galaxy Candidates at z > 8#1066040
- Preliminary Target Selection for the DESI Luminous Red Galaxy (LRG) Sample#619929
- A Uniform Census of Variable Stars in M101 Based on HST/ACS Photometry#973450
- JWST Absorption-Line Analysis of UV-Bright Galaxies at $z=7.2-10.6$: Early Chemical Enrichment Traced by C, O, Mg, Al, Si, and Fe#819390
Record · ID 999182
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive