We seek to improve the accuracy of joint galaxy photometric redshift estimation and spectral energy distribution (SED) fitting. By simulating different sources of uncorrected systematic errors, we demonstrate that if the uncertainties on the photometric redshifts are estimated correctly, so are those on the other SED fitting parameters, such as stellar mass, stellar age, and dust reddening. Furthermore, we find that if the redshift uncertainties are over(under)-estimated, the uncertainties in SED parameters tend to be over(under)-estimated by similar amounts. These results hold even in the presence of severe systematics and provide, for the first time, a mechanism to validate the uncertainties on these parameters via comparison with spectroscopic redshifts. We propose a new technique (annealing) to re-calibrate the joint uncertainties in the photo-z and SED fitting parameters without compromising the performance of the SED fitting + photo-z estimation. This procedure provides a consistent estimation of the multidimensional probability distribution function in SED fitting + z parameter space, including all correlations. While the performance of joint SED fitting and photo-z estimation might be hindered by template incompleteness, we demonstrate that the latter is "flagged" by a large fraction of outliers in redshift, and that significant improvements can be achieved by using flexible stellar populations synthesis models and more realistic star formation histories. In all cases, we find that the median stellar age is better recovered than the time elapsed from the onset of star formation [abridged].
Performance of Photometric Template Fitting for Ultra-High Redshift Galaxies
Cosmic Dawn Center (DAWN)（宇宙黎明中心）；Niels Bohr Institute, University of Copenhagen（哥本哈根大学尼尔斯·玻尔研究所）；Department of Physics and Astronomy, University of Missouri（密苏里大学物理与天文学系）；DARK Cosmology Center, University of Copenhagen（哥本哈根大学暗宇宙学中心）；California Institute of Technology（加州理工学院）
Context. Computational techniques are essential for mining large databases produced in modern surveys with value-added products. Aims. This paper presents a machine learning procedure to carry out simultaneously galaxy morphological classification and photometric redshift estimates. Currently, only spectral energy distribution (SED) fitting has been used to obtain these results all at once. Methods. We used the ancillary data gathered in the OTELO catalog and designed a non-sequential neural network that accepts optical and near-infrared photometry as input. The network transfers the results o…
BayeSN $\times$ Dovekie: Joint Photometric Cross-calibration and SED Modelling of Type Ia Supernovae
Institute of Astronomy and Kavli Institute for Cosmology, University of Cambridge（剑桥大学天体物理研究所与卡弗里宇宙学研究所）；School of Physics and Astronomy, University of Southampton（南安普顿大学物理与天文学院）
We present ASTRODEEP-GS43, a new multiwavelength photometric catalogue of the GOODS-South field, which builds and improves upon the previously released CANDELS catalogue. We provide photometric fluxes and corresponding uncertainties in 43 optical and infrared bands (25 wide and 18 medium filters), as well as photometric redshifts and physical properties of the 34930 CANDELS $H$-detected objects, plus an additional sample of 178 $H$-dropout sources, of which 173 are $Ks$-detected and 5 IRAC-detected. We keep the CANDELS photometry in 7 bands (CTIO $U$, Hubble Space Telescope WFC3 and ISAAC-$K$)…
We present a novel method to estimate galaxy physical properties from spectral energy distributions (SEDs), alternate to template fitting techniques and based on self-organizing maps (SOM) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has been previously tested with hydrodynamical simulations in Davidzon et al. (2019) while here is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high quality, panchromatic data set, which we elect to be the "COSMOS2020" galaxy catalog. After the training and calibration s…
Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer
University of Manchester（曼彻斯特大学）；Kavli Institute for Cosmology（卡弗里宇宙学研究所）；Institute of Astronomy（天文学研究所）；University of Portsmouth（朴茨茅斯大学）；Institute of Cosmology and Gravitation（宇宙学与引力研究所）；University of Sussex（萨塞克斯大学）；Instituto de Astrofísica de Canarias（加那利天体物理研究所）；Universidad de La Laguna（拉各鲁纳大学）
Probability density estimation of photometric redshifts based on machine learning
测光红移（photo-z's）提供了另一种方法来估计大样本星系的距离，因此对各种宇宙学问题至关重要。在多年来提出的各种方法中，能够内插通过光谱数据获得的知识的监督机器学习（ML）方法已被证明是非常有效的。METAPHOR（Machine-learning Estimation Tool for Accurate PHOtometric Redshifts）是一种新的方法，旨在提供ML方法预测测光红移误差分布的可靠概率密度函数（PDF）。该方法被实现为模块化工作流程，其用于photo-z估计的内部引擎利用MLPQNA神经网络（具有准牛顿学习规则的多层感知器），并且可以轻松地替换选择用于预测photo-z的特定机器学习模型。在对该软件进行简短描述后，我们对公共星系数据（斯隆数字巡天-数据发布9）的结果进行了总结，并与基于光谱能量分布（SED）模板拟合的完全不同的方法进行了比较。
In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting e…
