期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 499, 期 2, 页码 1587-1606出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/staa2799
关键词
methods: statistical; galaxies: distances and redshifts; galaxies: statistics
资金
- United States Department of Energy (DOE) [DE-SC0009999]
- National Science Foundation/Association of Universities for Research in Astronomy NSF/AURA [N56981C]
- Max Planck Society
- Alexander von Humboldt Foundation in the framework of the Max PlanckHumboldt Research Award by the Federal Ministry of Education and Research
- National Science Foundation [AST-1517237, 1258333]
- MyBrainSc Scholarship (Ministry of Education, Malaysia)
- King Abdulaziz City for Science and Technology
- ASI/INAF [2018-23-HH.0]
- DOE [DESC-0011635, DE-AC02-76SF00515]
- DIRAC Institute in the Department of Astronomy at the University of Washington
- NSF [1521786]
- Oxford Hintze Centre for Astrophysical Surveys - Hintze Family Charitable Foundation
- NSF DMS grant [1520786]
- EU
- Department of Energy [DE-AC02-76SF00515]
- U.S. Department of Energy, Office of Science, Office of High Energy Physics [DE-SC0007914]
- FAPESP [2019/11321-9]
- CNPq [306943/2017-4]
- Institut National de Physique Nucleaire et de Physique des Particules in France
- the Science & Technology Facilities Council in the United Kingdom
- Department of Energy, the National Science Foundation
- LSST Corporation in the United States
- Centre National de la Recherche Scientifique
- National Energy Research Scientific Computing Center
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
- UK BIS National E-infrastructure capital grants
- UK particle physics grid
- GridPP Collaboration
- STFC [ST/S000488/1] Funding Source: UKRI
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1521786] Funding Source: National Science Foundation
Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing 12 photo-z algorithms applied to mock data produced for The Rubin Observatory Legacy Survey of Space and Time Dark Energy Science Collaboration. By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. We report systematic biases and overall over/underbreadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performance metrics.
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