Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)

@article{Schmidt2020EvaluationOP,
  title={Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)},
  author={Samuel J. Schmidt and Alex I. Malz and Alex I. Malz and John Y. H. Soo and Ibrahim A. Almosallam and Massimo Brescia and Stefano Cavuoti and J. Cohen-Tanugi and Andrew J. Connolly and Joseph DeRose and Peter E. Freeman and Melissa L. Graham and Kartheik G. Iyer and Kartheik G. Iyer and Matt J. Jarvis and Matt J. Jarvis and Johannes Kalmbach and Eve Kovacs and Ann B. Lee and Giuseppe Longo and Christopher Brian Morrison and Jeffery A. Newman and Erfan Nourbakhsh and Eric Nuss and Taylor Pospisil and H. Tranin and Risa H. Wechsler and Risa H. Wechsler and Rongpu Zhou and Rongpu Zhou and Rafael Izbicki},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2020},
  volume={499},
  pages={1587-1606}
}
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 twelve photo-z algorithms applied to mock data produced for The Rubin Observatory… 

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