论文标题
欧几里得准备。 xxi。在欧几里德深度调查中寻找$ z> 6 $星系的中间红移污染物
Euclid preparation. XXI. Intermediate-redshift contaminants in the search for $z>6$ galaxies within the Euclid Deep Survey
论文作者
论文摘要
(删节)预计欧几里得任务将在三个深地域发现数千个Z> 6个星系,共同覆盖约40 deg2区域。但是,欧几里得频段数量有限,辅助数据的可用性可能会使Z> 6个星系具有挑战性。在这项工作中,我们评估了欧几里德深度调查中Z> 6个星系期望的中间红移星系(Z = 1-5.8)的污染程度。这项研究基于从Ultravista Ultra-Deep调查中选择的〜0.7 deg2区域的Z = 1-8的〜176,000个真实星系,约为96,000个模拟星系,具有25.3 $ \ leq $ h <27.0,完全涵盖了在Euclid深入调查中探讨的范围。我们从基准,28波段光度法和拟合光谱能量分布(SED)模拟欧几里得和辅助光度法到这些模拟数据的各种组合。我们的研究表明,仅使用欧几里得数据鉴定Z> 6将非常有效,对于明亮(微弱)星系,Z> 6恢复91(88)%。对于紫外的明亮样品,单独使用欧几里得观察到的明显z> 6星系中Z = 1-5.8污染物的百分比为18%,通过包括超深鲁宾(Spitzer)光度法将其降低至4(13)%。相反,对于微弱的模拟样本,仅在39%的情况下,仅使用欧几里得的污染分数要高得多,并且在包括超深鲁宾数据时将其降至7%。对于类似于紫外线的明亮星系,我们发现欧几里得(I-Y)> 2.8和(Y-J)<1.4颜色标准可以将污染物与真实的Z> 6星系区分开,尽管这些污染物仅适用于54%的污染物,因为许多污染物的颜色不受约束(I-Y)。在最乐观的情况下,这些切割将污染分数减少到1%,同时保留了81%的基准Z> 6样本。对于微弱的模拟样本,颜色切割是不可行的。
(Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift galaxies (z=1-5.8) expected for z>6 galaxies within the Euclid Deep Survey. This study is based on ~176,000 real galaxies at z=1-8 in a ~0.7 deg2 area selected from the UltraVISTA ultra-deep survey, and ~96,000 mock galaxies with 25.3$\leq$H<27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from the fiducial, 28-band photometry, and fit spectral energy distributions (SEDs) to various combinations of these simulated data. Our study demonstrates that identifying z>6 with Euclid data alone will be very effective, with a z>6 recovery of 91(88)% for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z=1-5.8 contaminants amongst apparent z>6 galaxies as observed with Euclid alone is 18%, which is reduced to 4(13)% by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimized to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (I-Y)>2.8 and (Y-J)<1.4 colour criteria can separate contaminants from true z>6 galaxies, although these are applicable to only 54% of the contaminants, as many have unconstrained (I-Y) colours. In the most optimistic scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z>6 sample. For the faint mock sample, colour cuts are infeasible.