Enhancing photometric redshift catalogs through color-space analysis: Application to KiDS-bright galaxies,Astronomy & Astrophysics

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Enhancing photometric redshift catalogs through color-space analysis: Application to KiDS-bright galaxies
Astronomy & Astrophysics ( IF 5.4 ) Pub Date : 2024-12-16 , DOI: 10.1051/0004-6361/202452424
Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing, Angus H. Wright, Andrej Dvornik, Christos Georgiou, Catherine Heymans, Hendrik Hildebrandt, Shahab Joudaki, Konrad Kuijken, Constance Mahony, Szymon Jan Nakoneczny, Mario Radovich, Jan Luca van den Busch, Ziang Yan, Mijin Yoon

Aims. We present a method for refining photometric redshift galaxy catalogs based on a comparison of their color-space matching with overlapping spectroscopic calibration data. We focus on cases where photometric redshifts (photo-z) are estimated empirically. Identifying galaxies that are poorly represented in spectroscopic data is crucial, as their photo-z may be unreliable due to extrapolation beyond the training sample.Methods. Our approach uses a self-organizing map (SOM) to project a multidimensional parameter space of magnitudes and colors onto a 2D manifold, allowing us to analyze the resulting patterns as a function of various galaxy properties. Using SOM, we compared the Kilo-Degree Survey’s bright galaxy sample (KiDS-Bright), limited to r < 20 mag, with various spectroscopic samples, including the Galaxy And Mass Assembly (GAMA).Results. Our analysis reveals that GAMA tends to underrepresent KiDS-Bright at its faintest (r ≳ 19.5) and highest-redshift (z ≳ 0.4) ranges; however, no strong trends are seen in terms of color or stellar mass. By incorporating additional spectroscopic data from the SDSS, 2dF, and early DESI, we identified SOM cells where the photo-z values are estimated suboptimally. We derived a set of SOM-based criteria to refine the photometric sample and improve photo-z statistics. For the KiDS-Bright sample, this improvement is modest, namely, it excludes the least represented 20% of the sample reduces photo-z scatter by less than 10%.Conclusions. We conclude that GAMA, used for KiDS-Bright photo-z training, is sufficiently representative for reliable redshift estimation across most of the color space. Future spectroscopic data from surveys such as DESI should be better suited for exploiting the full improvement potential of our method.

中文翻译：

通过色彩空间分析增强光度红移星系：在 KiDS 明亮星系中的应用

目标。我们提出了一种基于颜色空间匹配与重叠光谱校准数据的比较来优化光度红移星系星系目录的方法。我们专注于根据经验估计光度红移（photo-z）的情况。识别光谱数据中表现不佳的星系至关重要，因为由于超出训练样本的外推，它们的 photo-z 可能不可靠。方法。我们的方法使用自组织映射（SOM）将星等和颜色的多维参数空间投影到 2D 流形上，使我们能够将生成的模式作为各种星系特性的函数进行分析。使用 SOM，我们将千度巡天明亮的星系样本（KiDS-Bright）与各种光谱样本（包括星系和质量体组装（GAMA））进行了比较，该样本仅限于 r < 20 等。结果。我们的分析表明，GAMA 往往在最微弱（r ≳ 19.5）和最高红移（z ≳ 0.4）范围内低估了 KiDS-Bright;然而，在颜色或恒星质量方面没有看到强烈的趋势。通过结合来自 SDSS 、 2dF 和早期 DESI 的额外光谱数据，我们确定了 photo-z 值估计不理想的 SOM 单元。我们得出了一组基于 SOM 的标准来优化光度样品并改进 photo-z 统计数据。对于 KiDS-Bright 样本，这种改进是适度的，即它排除了代表性最少的 20% 的样本，减少了不到 10% 的 photo-z 散射。结论。我们得出的结论是，用于 KiDS-Bright photo-z 训练的 GAMA 足以代表大多数颜色空间的可靠红移估计。来自 DESI 等调查的未来光谱数据应该更适合开发我们方法的全部改进潜力。