arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.04291astro-ph.IMastro-ph.COastro-ph.GA
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从宽带测光中学习用于光度红移估计的光谱模板

Learning Spectral Templates for Photometric Redshift Estimation from Broadband Photometry

John Franklin Crenshaw, Andrew J. Connolly

AI总结:

本文提出从约10万个星系测光数据中学习光谱模板,以更高分辨率重建谱线,使光度红移的离群值、偏差和离散度分别降低最多28%、91%和25%。

摘要:

从宽带测光中估计红移通常受限于我们能否准确地将星系颜色映射到潜在的光谱模板。当前技术利用星系的分光光度样本或由光谱合成模型导出的光谱。这两种方法都有其局限性:要么样本量小且往往不能代表星系颜色的多样性,要么模型颜色可能存在偏差(通常随波长变化),从而在导出的红移中引入系统误差。在本文中,我们从约10万个具有已测红移和颜色的星系集合中学习潜在的光谱能量分布。我们表明,对于20个光谱模板的样本,我们能够以显著高于用于测量测光的宽带滤波器的分辨率重建发射线和吸收线。我们发现,与使用标准光谱模板集合的估计相比,我们的训练算法将导出的光度红移中的离群值比例降低了最多28%,偏差降低了最多91%,离散度降低了最多25%。我们讨论了该方法目前的局限性及其在恢复星系潜在性质方面的适用性。我们导出的模板和用于产生这些结果的代码已在一个专门的Github仓库中公开提供:https://github.com/dirac-institute/photoz_template_learning。

英文摘要:

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: https://github.com/dirac-institute/photoz_template_learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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