Galaxy formation during the first billion years of our Universe remains a challenging problem at the forefront of astrophysical cosmology. Although these $z \geq 6$ galaxies are likely responsible for the last major phase change of our Universe, the epoch of reionization (EoR), detailed studies are possible only for relatively rare, bright objects. Characterizing the fainter galaxies which are more representative of the population as a whole is currently done mainly through their non-ionizing UV luminosity function (LF). Observing the faint end of the UV LFs is nevertheless challenging, and current estimates can differ by orders of magnitude.
Here we propose a methodology to combine disparate high-$z$ UV LF data sets in a Bayesian framework: Bayesian Data Averaging (BDA). Using a flexible, physically-motivated galaxy model, we compute the relative evidence of various $z=6$ UV LFs within the magnitude range $-20 \leq M_{\rm UV} \leq -15$ which is common to the data sets. Our model, based primarily on power-law scalings of the halo mass function, naturally penalizes systematically jagged data points as well as mis-estimated errors. We then use the relative evidence to weigh the posteriors obtained from disparate LF observations during the EoR, $6 \leq z \leq 10$. The resulting LFs suggest that the star formation rate density (SFRD) integrated down to a UV magnitude of -17 represent $60.9^{+11.3}_{-9.6}\%$ / $28.2^{+9.3}_{-10.1}\%$ / $5.7^{+4.5}_{-4.7}\%$ of the total SFRD at redshifts 6 / 10 / 15. The BDA framework we introduce enables galaxy models to leverage multiple, analogous observational data sets.
Bayesian luminosity function estimation in multi-depth datasets with selection effects: A case study for $3<z<5$ Lyman $α$ emitters
Universitá degli Studi di Milano-Bicocca（米兰比可卡大学）；INAF – Osservatorio Astronomico di Brera（意大利国家天体物理研究所布雷拉天文台）；INAF – Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；INFN, Sezione di Milano-Bicocca（意大利国家核物理研究所米兰比可卡分部）；Max-Planck-Institut für Astrophysik（马普天体物理研究所）
A hierarchical Bayesian framework for cosmology using Type 1 AGN variability
Institute of Astronomy, University of Cambridge（剑桥大学天文学研究所）；Kavli Institute for Cosmology, University of Cambridge（剑桥大学卡弗里宇宙学研究所）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）
The Radius-Luminosity Relationship for Active Galactic Nuclei with Hierarchical Bayesian Reverberation Mapping
University of Queensland（昆士兰大学）；Macquarie University（麦考瑞大学）；Astrophysics and Space Technologies Research Centre, Macquarie University（麦考瑞大学天体物理与空间技术中心）；The Ohio State University（俄亥俄州立大学）；Kavli Institute for Particle Astrophysics and Cosmology, Stanford University（斯坦福大学卡弗里粒子天体物理学与宇宙学研究所）；Australian National University（澳大利亚国立大学）；Sydney Institute for Astronomy, School of Physics, A28, University of Sydney（悉尼大学A28物理学院悉尼天文研究所）
Galaxies in X-ray Selected Clusters and Groups in Dark Energy Survey Data II: Hierarchical Bayesian Modeling of the Red-Sequence Galaxy Luminosity Function
The XXL Survey: XLIII. The quasar radio loudness dichotomy exposed via radio luminosity functions obtained by combining results from COSMOS and XXL-S X-ray selected quasars
