标题： 基于异构和非代表性数据的数据驱动可解释光度红移 显示英文标题

标题： Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data

摘要： 我们提出了一种新的方法，用于在深度星系和类星体调查中推断光度红移，该方法基于潜在光谱能量分布（SEDs）的数据驱动模型和光度通带通量作为红移函数的物理模型。这种概念上新颖的方法通过直接从训练数据构建模板SEDs，结合了机器学习和模板拟合方法的优点。这是通过在通量-红移空间中运行的高斯过程实现的，编码了红移的物理特性和星系SEDs在光度通带上的投影。这种方法减轻了获取代表性训练数据或构建详细星系SED模型的需要；它只需要知道光度通带和校准或参数化的未知数。训练数据可以由光谱和深场多通带光度数据的组合构成，这些数据不需要与感兴趣的靶向调查完全空间重叠，甚至不需要使用相同的光度通带。我们在COSMOS区域的$i$-幅度选择的光谱确认星系上展示了该方法。该模型在最深的波段（来自SUBARU和HST）上进行训练，并仅使用较浅的SDSS光学波段得出光度红移。我们证明，尽管训练集和目标集具有非常不同的红移分布、噪声特性，甚至光度波段，我们仍能获得准确的红移点估计和概率分布。我们的模型还可以用来预测缺失的光度通量，或模拟具有现实通量和红移的星系群体。这种方法开启了一个新纪元，在这个纪元中，大型光度调查的光度红移将使用一个灵活但物理的数据模型来推导，该模型在所有可用调查（光谱和光度）上进行训练。 显示英文摘要

摘要： We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine-learning and template-fitting methods by building template SEDs directly from the training data. This is made computationally tractable with Gaussian Processes operating in flux--redshift space, encoding the physics of redshift and the projection of galaxy SEDs onto photometric band passes. This method alleviates the need of acquiring representative training data or constructing detailed galaxy SED models; it requires only that the photometric band passes and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the $i$-magnitude-selected, spectroscopically-confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes, or to simulate populations of galaxies with realistic fluxes and redshifts, for example. This method opens a new era in which photometric redshifts for large photometric surveys are derived using a flexible yet physical model of the data trained on all available surveys (spectroscopic and photometric).