Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived from spectral synthesis models. Both of these approaches have their limitations, either the sample sizes are small and often not representative of the diversity of galaxy colors or the model colors can be biased (often as a function of wavelength) which introduces systematics in the derived redshifts. In this paper we learn the underlying spectral energy distributions from an ensemble of $\sim$100K galaxies with measured redshifts and colors. We show that we are able to reconstruct emission and absorption lines at a significantly higher resolution than the broadband filters used to measure the photometry for a sample of 20 spectral templates. We find that our training algorithm reduces the fraction of outliers in the derived photometric redshifts by up to 28%, bias up to 91%, and scatter up to 25%, when compared to estimates using a standard set of spectral templates. We discuss the current limitations of this approach and its applicability for recovering the underlying properties of galaxies. Our derived templates and the code used to produce these results are publicly available in a dedicated Github repository: this https URL.

中文翻译：

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从宽带光度学中学习光度红移估计的光谱模板

从宽带光度计估计红移通常受到我们将星系的颜色映射到基础光谱模板的准确程度的限制。当前的技术利用星系的分光光度测量样本或从光谱合成模型导出的光谱。这两种方法都有其局限性，要么样本量小，通常不能代表星系颜色的多样性，要么模型颜色可能存在偏差（通常作为波长的函数），这在派生的红移中引入了系统性。在本文中，我们从具有测量红移和颜色的 $\sim$100K 星系的集合中学习潜在的光谱能量分布。我们表明，与用于测量 20 个光谱模板样本的光度测量的宽带滤波器相比，我们能够以明显更高的分辨率重建发射和吸收线。我们发现，与使用标准光谱模板集的估计相比，我们的训练算法将派生的光度红移中的异常值比例减少了 28%，偏差高达 91%，散射高达 25%。我们讨论了这种方法的当前局限性及其在恢复星系基本特性方面的适用性。我们的派生模板和用于生成这些结果的代码在专用的 Github 存储库中公开提供：此 https URL。与使用一组标准光谱模板的估计相比，偏差高达 91%，散射高达 25%。我们讨论了这种方法的当前局限性及其在恢复星系基本特性方面的适用性。我们的派生模板和用于生成这些结果的代码在专用的 Github 存储库中公开提供：此 https URL。与使用一组标准光谱模板的估计相比，偏差高达 91%，散射高达 25%。我们讨论了这种方法的当前局限性及其在恢复星系基本特性方面的适用性。我们的派生模板和用于生成这些结果的代码在专用的 Github 存储库中公开提供：此 https URL。