Deep Realistic Extragalactic Model (DREaM) Galaxy Catalogs: Predictions for a Roman Ultra-deep Field

Deep Realistic Extragalactic Model (DREaM) Galaxy Catalogs: Predictions for a Roman Ultra-deep Field
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深层真实河外模型 (DREAM) 星系目录:对罗马超深场的预测

DOI:
10.3847/1538-4357/ac46fb
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发表时间:
2021
期刊:
The Astrophysical Journal
影响因子:
--
通讯作者:
Risa Wechsler
Risa Wechsler
中科院分区:
--
文献类型:
--
作者:
N. Drakos;B. Villasenor;B. Robertson;R. Hausen;M. Dickinson;H. Ferguson;S. Furlanetto;J. Greene;P. Madau;A. Shapley;D. Stark;Risa Wechsler

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在接下来的十年里,由詹姆斯·韦伯太空望远镜和罗马太空望远镜等望远镜进行的深部星系探测将提供变革性的数据集,这些数据集将极大地增强对再电离纪元(EOR)期间星系形成的理解。在这项工作中,我们提出了用于创建合成星系星表的深现实星系外模型(DREAM)。我们的模型结合了暗物质模拟、次晕丰度匹配和经验模型,并包括星系位置、形态和光谱能量分布。由此产生的综合星表延伸到红移z∼12,以及覆盖天空1°2区域的星系质量log10(M/M⊙)=5。我们使用DREAM探索1°2罗马超深部油田(UDF)的科学回报,并为优化超深部勘测设计提供资源。我们发现,一个罗马UDF到∼30 m AB将可能探测到超过106M的UV<−17星系,其中超过104个处于红移z>7,这为在提高采收率期间约束星系的性质提供了一个无与伦比的数据集。我们的合成目录和模拟图像是公开提供的,为社区提供了一个工具,为即将到来的数据做准备。
In the next decade, deep galaxy surveys from telescopes such as the James Webb Space Telescope and Roman Space Telescope will provide transformational data sets that will greatly enhance the understanding of galaxy formation during the epoch of reionization (EoR). In this work, we present the Deep Realistic Extragalactic Model (DREaM) for creating synthetic galaxy catalogs. Our model combines dark matter simulations, subhalo abundance matching and empirical models, and includes galaxy positions, morphologies, and spectral energy distributions. The resulting synthetic catalog extends to redshifts z ∼ 12, and galaxy masses log10(M/M⊙)=5 covering an area of 1 deg2 on the sky. We use DREaM to explore the science returns of a 1 deg2 Roman ultra-deep field (UDF), and to provide a resource for optimizing ultra-deep survey designs. We find that a Roman UDF to ∼30 m AB will potentially detect more than 106 M UV < − 17 galaxies, with more than 104 at redshifts z > 7, offering an unparalleled data set for constraining galaxy properties during the EoR. Our synthetic catalogs and simulated images are made publicly available to provide the community with a tool to prepare for upcoming data.
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