Optimizing Photometric Redshift Training Sets I: Efficient Compression of the Galaxy Color-Redshift Relation with UMAP
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912756112818176 |
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| author | Ashmead, Finian Newman, Jeffrey A. Andrews, Brett H. Bezanson, Rachel Dey, Biprateep Masters, Daniel C. Stanford, S. A. |
| author_facet | Ashmead, Finian Newman, Jeffrey A. Andrews, Brett H. Bezanson, Rachel Dey, Biprateep Masters, Daniel C. Stanford, S. A. |
| contents | 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 mapping color to redshift using self-organizing maps (SOMs). Instead, we utilized the uniform manifold approximation and projection (UMAP) algorithm to compress a Rubin-Roman-like $ugrizyJH$ color space into a thin and densely-sampled manifold. Crucially, the manifold varies continuously and monotonically in redshift and specific star formation rate in roughly orthogonal directions. Using $\sim$110,000 COSMOS2020 many-band photo-$z$'s and $\sim$15,000 spec-$z$'s as representative and non-representative samples, respectively, we trained and tested redshift estimation from a SOM (SOM-$z$) and from nearest neighbors in UMAP space (UMAP-$k$NN-$z$). Compared to SOM-$z$, UMAP-$k$NN-$z$ exhibited smaller photo-$z$ scatter and fraction of outliers for the representative training set. When training with the highly biased spec-$z$ sample, UMAP-$k$NN-$z$ maintained similar performance, but the outlier fraction for SOM-$z$ increased by nearly three times. The physically-meaningful trends across the UMAP manifold allow for accurate redshift regression even in regions of color space sparsely populated by spectroscopic objects, which comprise nearly 25% of the photometric sample. This suggests that representative, spectroscopically-anchored training sets can be produced by interpolating between spectroscopic sources at the UMAP coordinates of photometric objects, maximizing the performance of photo-$z$ algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_09032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Photometric Redshift Training Sets I: Efficient Compression of the Galaxy Color-Redshift Relation with UMAP Ashmead, Finian Newman, Jeffrey A. Andrews, Brett H. Bezanson, Rachel Dey, Biprateep Masters, Daniel C. Stanford, S. A. Astrophysics of Galaxies 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 mapping color to redshift using self-organizing maps (SOMs). Instead, we utilized the uniform manifold approximation and projection (UMAP) algorithm to compress a Rubin-Roman-like $ugrizyJH$ color space into a thin and densely-sampled manifold. Crucially, the manifold varies continuously and monotonically in redshift and specific star formation rate in roughly orthogonal directions. Using $\sim$110,000 COSMOS2020 many-band photo-$z$'s and $\sim$15,000 spec-$z$'s as representative and non-representative samples, respectively, we trained and tested redshift estimation from a SOM (SOM-$z$) and from nearest neighbors in UMAP space (UMAP-$k$NN-$z$). Compared to SOM-$z$, UMAP-$k$NN-$z$ exhibited smaller photo-$z$ scatter and fraction of outliers for the representative training set. When training with the highly biased spec-$z$ sample, UMAP-$k$NN-$z$ maintained similar performance, but the outlier fraction for SOM-$z$ increased by nearly three times. The physically-meaningful trends across the UMAP manifold allow for accurate redshift regression even in regions of color space sparsely populated by spectroscopic objects, which comprise nearly 25% of the photometric sample. This suggests that representative, spectroscopically-anchored training sets can be produced by interpolating between spectroscopic sources at the UMAP coordinates of photometric objects, maximizing the performance of photo-$z$ algorithms. |
| title | Optimizing Photometric Redshift Training Sets I: Efficient Compression of the Galaxy Color-Redshift Relation with UMAP |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2512.09032 |