Euclid：利用梯度提升树识别Euclid深场中最红的高红移星系
Euclid: Identifying the reddest high-redshift galaxies in the Euclid Deep Fields with gradient-boosted trees
AI总结：
本研究评估Euclid结合辅助数据识别高红移尘埃大质量星系的能力，利用梯度提升树预测红移和光谱类型，在模拟深场中准确识别$3\lesssim z\lesssim 7$的种群，红移RMS为0.55，OLF为8.5%。
摘要：
尘埃、遥远且大质量（$M_*\gtrsim 10^{11}\\，\rm M_\odot$）的星系通常表现出显著的恒星形成活动，根据ALMA观测，它们在$z\approx3$--$5$时对宇宙恒星形成速率密度的贡献约为$25\\%$，在$z\sim7$时高达$30\\%$。然而，它们在经典光学巡天中难以探测，而当前的近红外巡天仅能在非常小的天区内探测到它们。由于这些天体具有较低的空间密度，为了获得统计上相关的结果，需要进行深且宽的巡天。Euclid将有可能提供所需的信息，但由于其波段内缺乏这些距离上的光谱特征，目前尚不清楚是否能够识别和表征这些天体。本工作的目标是评估Euclid结合辅助光学和近红外数据，基于宽带测光识别这些遥远、尘埃且大质量星系的能力。我们使用梯度提升算法来预测高$z$天体的红移和光谱类型。为进行此分析，我们利用了基于SPRITZ软件模拟的测光观测。发现梯度提升算法在$z>2$时对Euclid深场巡天模拟星表中的天体红移和光谱类型预测均准确。特别地，我们研究了HIERO类似体（即$H-[4.5]>2.25$的源），结合Euclid和Spitzer在深场深度下的数据。我们发现，在$3\lesssim z\lesssim 7$范围内的尘埃种群被很好地识别，红移RMS和OLF分别仅为$0.55$和$8.5\\%$（$H_E\leq26$）。我们的研究结果表明，借助Euclid，我们将对大质量尘埃星系在宇宙恒星形成速率随时间演变中的作用获得有意义的见解。
英文摘要：
Dusty, distant, massive ($M_*\gtrsim 10^{11}\,\rm M_\odot$) galaxies are usually found to show a remarkable star-formation activity, contributing on the order of $25\%$ of the cosmic star-formation rate density at $z\approx3$--$5$, and up to $30\%$ at $z\sim7$ from ALMA observations. Nonetheless, they are elusive in classical optical surveys, and current near-infrared surveys are able to detect them only in very small sky areas. Since these objects have low space densities, deep and wide surveys are necessary to obtain statistically relevant results about them. Euclid will be potentially capable of delivering the required information, but, given the lack of spectroscopic features at these distances within its bands, it is still unclear if it will be possible to identify and characterize these objects. The goal of this work is to assess the capability of Euclid, together with ancillary optical and near-infrared data, to identify these distant, dusty and massive galaxies, based on broadband photometry. We used a gradient-boosting algorithm to predict both the redshift and spectral type of objects at high $z$. To perform such an analysis we make use of simulated photometric observations derived using the SPRITZ software. The gradient-boosting algorithm was found to be accurate in predicting both the redshift and spectral type of objects within the Euclid Deep Survey simulated catalog at $z>2$. In particular, we study the analog of HIEROs (i.e. sources with $H-[4.5]>2.25$), combining Euclid and Spitzer data at the depth of the Deep Fields. We found that the dusty population at $3\lesssim z\lesssim 7$ is well identified, with a redshift RMS and OLF of only $0.55$ and $8.5\%$ ($H_E\leq26$), respectively. Our findings suggest that with Euclid we will obtain meaningful insights into the role of massive and dusty galaxies in the cosmic star-formation rate over time.
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。
