The MANIFEST pre-concept design

Lawrence, Jon; Ben-Ami, Sagi; Braulio, A.; Colless, Matthew; Contos, Adam R.; DePoy, D. L.; Faes, D. M.; Gillingham, Peter; Goodwin, Michael; Houston, E. A.; Kuêhn, K.; Lacombe, Celestina S.; Lorente, Nuria P. F.; Mali, Slavko; Marshall, J. L.; McGregor, Helen; Millan-Gabet, R.; Nichani, Vijay; Hartmann, Vitor N.; O'Brien, Ellie; Oliveira, Claudia L. Mendes de; Ortolan, H.; Pires, Priscila; Ribeiro, Rafael; Saunders, Will; Schmidt, Luke M.; Souza, Aline; Szentgyorgyi, Andrew; Waller, Lew; Zafar, T.; Zheng, Rong

doi:10.1117/12.2563238

Cited by 2 publications

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“…These underlying distributions correspond to the prior p(z, t) used within a Bayesian photometric redshift method; by inferring p(z, t) from the data itself, uncertainties in the prior can be propagated into the final redshift PDFs for each object. Leistedt et al (2019) extended this method to also allow the set of rest-frame SED templates used to be modified via hierarchical inference. A different approach is taken by Sánchez & Bernstein (2019), Alarcon et al (2020), who instead infer a prior probability distribution for the density of galaxies in the observed high-dimensional color space, rather than in the space of intrinsic properties, via a hierarchical inference process.…”

Section: Bayesian Hierarchical Methodsmentioning

confidence: 99%

“…Newman et al 2019 found that the most efficient currentlyavailable options, the DESI instrument at the Mayall Telescope (DESI Collaboration et al 2016b) and the DEIMOS spectrograph at Keck Observatory (Faber et al 2003), would take more than 1800 dark nights to conduct this survey. In contrast, the upcoming Subaru/PFS (Tamura et al 2016) would take roughly 400 dark nights, while the proposed Maunakea Spectroscopic Explorer (Hill et al 2018), ESO SpecTel project (Ellis & Dawson 2019), or the MANIFEST fiber-feed for the GMACS instrument on the Giant Magellan Telescope (Lawrence et al 2020) could potentially complete this fiducial survey in less than 200.…”

Section: Potential Developments In Spectroscopic Trainingmentioning

confidence: 99%

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Photometric Redshifts for Next-Generation Surveys

Newman

Gruen

2022

Annu. Rev. Astron. Astrophys.

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Photometric redshifts are essential in studies of both galaxy evolution and cosmology, as they enable analyses of objects too numerous or faint for spectroscopy. The Rubin Observatory, Euclid, and Roman Space Telescope will soon provide a new generation of imaging surveys with unprecedented area coverage, wavelength range, and depth. To take full advantage of these data sets, further progress in photometric redshift methods is needed. In this review, we focus on the greatest common challenges and prospects for improvement in applications of photometric redshifts to the next generation of surveys: ▪ Gains in performance (i.e., the precision of redshift estimates for individual galaxies) could greatly enhance studies of galaxy evolution and some probes of cosmology. ▪ Improvements in characterization (i.e., the accurate recovery of redshift distributions of galaxies in the presence of uncertainty on individual redshifts) are urgently needed for cosmological measurements with next-generation surveys. To achieve both of these goals, improvements in the scope and treatment of the samples of spectroscopic redshifts that make high-fidelity photometric redshifts possible will also be needed. For the full potential of the next generation of surveys to be reached, the characterization of redshift distributions must improve by roughly an order of magnitude compared with the current state of the art, requiring progress on a wide variety of fronts. We conclude by presenting a speculative evaluation of how photometric redshift methods and the collection of the necessary spectroscopic samples may improve by the time near-future surveys are completed. Expected final online publication date for the Annual Review of Astronomy and Astrophysics Volume 60 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Bayesian Hierarchical Methodsmentioning

confidence: 99%