[论文解读] Highly Efficient Selection of High-Redshift Emission-Line Galaxies for future DESI-like surveys with Deep Multi-band Imaging

Yoquelbin Salcedo Hernandez, Jeffrey A. Newman+54 more|arXiv (Cornell University)|Feb 23, 2026

Galaxies: Formation, Evolution, Phenomena被引用 0

一句话总结

作者开发并测试基于深度成像的 ELG 选择方法，以提升 DESI 类调查的高红移 ELG 收获，在 DESI ELG 的红移成功率和表面密度上取得更高表现。

ABSTRACT

Emission-line galaxies (ELGs) are an important tracer of baryon acoustic oscillations (BAO) and large-scale structure (LSS) at $z > 1$. In this work, we investigate the feasibility of using deep wide-area multi-band imaging (e.g., from the Rubin Observatory) to efficiently select high redshift ELGs. Using Hyper Supreme-Cam $grizy$ photometry and COSMOS2020 many-band photometric redshifts, we designed simple color cuts guided by a probabilistic random forest classifier to select galaxies at $z = 1.1$--$1.6$. We then empirically tested and refined these color cuts using two samples of galaxies with deep spectroscopy and broad color coverage obtained with the Dark Energy Spectroscopic Instrument (DESI). Compared to DESI ELGs at $z = 1.1$--$1.6$, we achieve a higher redshift measurement success rate (89% versus 69%), a much higher correct redshift range success rate (84% versus 34%), and a far higher net surface density yield (1372 $\mathrm{deg^{-2}}$ versus 660 $\mathrm{deg^{-2}}$). Combining our sample with current DESI ELGs would increase the net ELG number density by a factor of $\sim3$, moving it out of the shot-noise limited regime and reducing the uncertainties on the BAO scale parameter at $z = 1.1$--$1.6$ by a factor of $\sim 2$.

研究动机与目标

• 通过更密集的高红移 ELG 样本来推动 z>1 的改进 BAO 测量。
• 利用深度多波段成像（类似 HSC/LSST）来设计 ELG 目标选择。
• 量化通过扩大 ELG 样本对 BAO 距离测量的预期改进。

提出的方法

• 在 COSMOS2020 光度数据上训练随机森林分类器，以识别 z=1.1–1.6 的 ELG。
• 从 RF 特征重要性中推导简单的颜色裁剪，以实现可解释的选择。
• 优化裁剪以最大化红移成功率并接近目标表面密度（约 1370 deg^-2）。
• 将优化样本的性能与 DESI ELG 在光谱真值数据和 DESI 观测条件下进行比较。
• 计算指标：红移测量成功率、红移区间成功率、靶密度，以及净表面密度产出。

Figure 1: $1\sigma$ uncertainty on the BAO scale parameter $\alpha$ as a function of the factor by which the ELG number density is increased compared to the expected final DESI sample (solid black), for measures of both the transverse scale ( $\alpha_{\perp}$ ) and the line of sight scale ( $\alpha_

实验结果

研究问题

• RQ1 深度、类似 LSST 的成像并扩展的光度带是否能够高效选择 z=1.1–1.6 的 ELG？
• RQ2 使用优化的颜色裁剪可以达到怎样的红移成功率和红移区间成功率？
• RQ3 新选择法的净 ELG 表面密度与当前 DESI ELG 样本相比如何，对 BAO 测量有何影响？

主要发现

• 基于 RF 的方法对 z=1.1–1.6 ELG 的选择具有高纯度/高完备性，ROC AUC 为 0.960。
• 优化后的颜色裁剪实现 g_fiber < 24.33、i-y -0.16 > r-i、i-y > 0.43、i-z > 0.45。
• 与 DESI ELG 相比，优化样本的红移测量成功率为 89% vs 69%。
• 红移区间成功率为 84% vs DESI ELG 比较的 34%。
• 净表面密度产出为 1372 deg^-2 vs 660 deg^-2（样本扩大约 3 倍）。
• 将新样本与当前 DESI ELGs 结合可将净 ELG 密度提高约 3 倍，并可能在 z=1.1–1.6 处将 BAO 不确定性降低约 2 倍。

Figure 2: Color-color diagrams of the DESI spec-truth sample using $g-r/r-z$ plotted using imaging from either deep (left) or wide (right) depth HSC imaging, with galaxies color-coded according to their spec-z. This sample was obtained using HSC deep photometry. We show the color cuts for the standa