| Full text | |
| Author(s): Show less - |
Schmidt, S. J.
[1]
;
Malz, I, A.
;
Soo, J. Y. H.
[2, 3]
;
Almosallam, I. A.
[4, 5]
;
Brescia, M.
[6]
;
Cavuoti, S.
[6, 7]
;
Cohen-Tanugi, J.
[8]
;
Connolly, A. J.
[9, 10]
;
DeRose, J.
[11, 12, 13, 14, 15, 16]
;
Freeman, P. E.
[17]
;
Graham, M. L.
[9, 10]
;
Iyer, K. G.
[18, 19]
;
Jarvis, M. J.
[20, 21]
;
Kalmbach, J. B.
[9, 10]
;
Kovacs, E.
[22]
;
Lee, A. B.
[17]
;
Longo, G.
[7]
;
Morrison, C. B.
[9, 10]
;
Newman, J. A.
[23, 24]
;
Nourbakhsh, E.
[1]
;
Nuss, E.
[8]
;
Pospisil, T.
[17]
;
Tranin, H.
[8]
;
Wechsler, R. H.
[25, 12, 14]
;
Zhou, R.
[23, 11, 24]
;
Izbicki, R.
[26]
;
Collaboration, LSST Dark Energy Sci
Total Authors: 27
|
| Affiliation: Show less - | [1] Univ Calif Davis, Dept Phys, One Shields Ave, Davis, CA 95616 - USA
[2] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT - England
[3] Univ Sci Malaysia, Sch Phys, Usm 11800, Pulau Pinang - Malaysia
[4] King Abdulaziz City Sci & Technol, Riyadh 11442 - Saudi Arabia
[5] Informat Engn, Parks Rd, Oxford OX1 3PJ - England
[6] INAF Astron Observ Capodimonte, Salita Moiariello 16, I-80131 Naples - Italy
[7] Univ Federico II, Dept Phys E Pancini, Via Cinthia 6, I-80126 Naples - Italy
[8] Univ Montpellier, Lab Univers & Particules Montpellier, CNRS, Montpellier 05 - France
[9] Univ Washington, Dept Astron, Box 351580, Seattle, WA 98195 - USA
[10] Univ Washington, DIRAC Inst, Box 351580, Seattle, WA 98195 - USA
[11] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, 1 Cyclotron Rd, Berkeley, CA 94720 - USA
[12] Stanford Univ, Dept Phys, 382 Via Pueblo Mall, Stanford, CA 94305 - USA
[13] Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 - USA
[14] Stanford Univ, Kavli Inst Particle Astrophys & Cosmol, Stanford, CA 94305 - USA
[15] Univ Calif Berkeley, Dept Phys, Berkeley Ctr Cosmol Phys, Berkeley, CA 94720 - USA
[16] SLAC Natl Accelerator Lab, Dept Particle Phys & Astrophys, Stanford, CA 94305 - USA
[17] Carnegie Mellon Univ, Dept Stat & Data Sci, 5000 Forbes Ave, Pittsburgh, PA 15213 - USA
[18] Rutgers State Univ, Dept Phys & Astron, 136 Frelinghuysen Rd, Piscataway, NJ 08854 - USA
[19] Univ Toronto, Dunlap Inst Astron & Astrophys, 50 St George St, Toronto, ON M5S 3H4 - Canada
[20] Univ Oxford, Dept Phys, Astrophys, Denys Wilkinson Bldg, Keble Rd, Oxford OX1 3RH - England
[21] Univ Western Cape, Dept Phys & Astron, ZA-7535 Bellville - South Africa
[22] Argonne Natl Lab, Lemont, IL 60439 - USA
[23] Univ Pittsburgh, Dept Phys & Astron, Pittsburgh, PA 15260 - USA
[24] Univ Pittsburgh, Pittsburgh Particle Phys Astrophys & Cosmol Ctr P, Pittsburgh, PA 15260 - USA
[25] SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 - USA
[26] Univ Fed Sao Carlos, Dept Stat, BR-13565905 Sao Carlos - Brazil
Total Affiliations: 26
|
| Document type: | Journal article |
| Source: | Monthly Notices of the Royal Astronomical Society; v. 499, n. 2, p. 1587-1606, DEC 2020. |
| Web of Science Citations: | 1 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 19/11321-9 - Neural networks in statistical inference problems |
| Grantee: | Rafael Izbicki |
| Support Opportunities: | Regular Research Grants |