论文标题
欧几里得准备:X.欧几里德光度降距挑战
Euclid preparation: X. The Euclid photometric-redshift challenge
论文作者
论文摘要
即将进行宇宙学的大型光度测量需要精确,准确的光度红移(Photo-Z)测量,以实现其主要科学目标的成功。但是,迄今为止,尚无使用这些调查将提供的宽带光度法以所需的精度生产照片 - $ z $ s。评估当前方法的优势和劣势是最终发展以应对这一挑战的方法的关键步骤。我们报告了一组通用数据的13个光度红移代码单值红移估计值和红移概率分布(PDZ)的性能,尤其是欧几里得任务将探测的0.2--2.6红移范围。我们使用从宇宙场的三个光度调查中绘制的模拟欧几里得数据设计挑战。数据分为两个样本:为参与者提供光度法和红移的一个校准样本;和仅包含光度法的验证样品,以确保对方法进行盲测。邀请参与者为验证样本中的每个源提供红移单个价值估算和PDZ,以及一个拒绝标志,表明他们认为不适合用于宇宙学分析的来源。通过一系列信息指标评估每种方法的性能,使用交叉匹配的光谱和高度准确的光度红移作为地面真理。我们表明,参与者设定的拒绝标准有效地消除了较强的异常值,这些来源从光谱 - 红外(Spec-Z)中偏离了超过0.15(1+Z)。我们还表明,尽管所有方法都能够提供可靠的单个价值估计值,但几种机器学习方法无法设法产生有用的PDZ。 [简略]
Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-$z$s at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2--2.6 redshift range that the Euclid mission will probe. We design a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data are divided into two samples: one calibration sample for which photometry and redshifts are provided to the participants; and the validation sample, containing only the photometry, to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates sources they consider unfit for use in cosmological analyses. The performance of each method is assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, sources for which the photo-z deviates by more than 0.15(1+z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. [abridged]