Euclid preparation - X. The Euclid photometric-redshift challenge
A&A, 644 (2020) A31
Published online: 2020-11-25
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All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8
Kenneth J DuncanMonthly Notices of the Royal Astronomical Society 512 (3) 3662 (2022)
https://doi.org/10.1093/mnras/stac608
Euclid preparation
R. Saglia, S. De Nicola, M. Fabricius, et al.Astronomy & Astrophysics 664 A196 (2022)
https://doi.org/10.1051/0004-6361/202243604
Euclid preparation
S. E. van Mierlo, K. I. Caputi, M. Ashby, et al.Astronomy & Astrophysics 666 A200 (2022)
https://doi.org/10.1051/0004-6361/202243950
Identification of Single Spectral Lines in Large Spectroscopic Surveys Using UMLAUT: an Unsupervised Machine-learning Algorithm Based on Unbiased Topology
I. Baronchelli, C. M. Scarlata, L. Rodríguez-Muñoz, M. Bonato, L. Morselli, M. Vaccari, R. Carraro, L. Barrufet, A. Henry, V. Mehta, G. Rodighiero, A. Baruffolo, M. Bagley, A. Battisti, J. Colbert, Y. S. 昱 Dai 戴, M. De Pascale, H. Dickinson, M. Malkan, C. Mancini, M. Rafelski and H. I. TeplitzThe Astrophysical Journal Supplement Series 257 (2) 67 (2021)
https://doi.org/10.3847/1538-4365/ac250c
Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case
Massimo Brescia, Stefano Cavuoti, Oleksandra Razim, et al.Frontiers in Astronomy and Space Sciences 8 (2021)
https://doi.org/10.3389/fspas.2021.658229
Euclidpreparation
A. Pocino, I. Tutusaus, F. J. Castander, et al.Astronomy & Astrophysics 655 A44 (2021)
https://doi.org/10.1051/0004-6361/202141061
Benchmarking and scalability of machine-learning methods for photometric redshift estimation
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam, Tony Hey and Ofer LahavMonthly Notices of the Royal Astronomical Society 505 (4) 4847 (2021)
https://doi.org/10.1093/mnras/stab1513
A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest
S Mucesh, W G Hartley, A Palmese, et al.Monthly Notices of the Royal Astronomical Society 502 (2) 2770 (2021)
https://doi.org/10.1093/mnras/stab164
Improving the reliability of photometric redshift with machine learning
Oleksandra Razim, Stefano Cavuoti, Massimo Brescia, et al.Monthly Notices of the Royal Astronomical Society 507 (4) 5034 (2021)
https://doi.org/10.1093/mnras/stab2334
Probing dark energy with tomographic weak-lensing aperture mass statistics
Nicolas Martinet, Joachim Harnois-Déraps, Eric Jullo and Peter SchneiderAstronomy & Astrophysics 646 A62 (2021)
https://doi.org/10.1051/0004-6361/202039679
Dark Energy Survey Year 3 Results: Galaxy mock catalogs for BAO analysis
I. Ferrero, M. Crocce, I. Tutusaus, et al.Astronomy & Astrophysics 656 A106 (2021)
https://doi.org/10.1051/0004-6361/202141744
Testing extensions to ΛCDM on small scales with forthcoming cosmic shear surveys
Sam G Stafford, Ian G McCarthy, Juliana Kwan, et al.Monthly Notices of the Royal Astronomical Society 508 (2) 2537 (2021)
https://doi.org/10.1093/mnras/stab2787
Dark Energy Survey Year 3 Results: Deep Field optical + near-infrared images and catalogue
W G Hartley, A Choi, A Amon, et al.Monthly Notices of the Royal Astronomical Society 509 (3) 3547 (2021)
https://doi.org/10.1093/mnras/stab3055
Euclid Preparation. XIV. The Complete Calibration of the Color–Redshift Relation (C3R2) Survey: Data Release 3
S. A. Stanford, D. Masters, B. Darvish, D. Stern, J. G. Cohen, P. Capak, N. Hernitschek, I. Davidzon, J. Rhodes, D. B. Sanders, B. Mobasher, F. J. Castander, S. Paltani, N. Aghanim, A. Amara, N. Auricchio, A. Balestra, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, J. Brinchmann, V. Capobianco, C. Carbone, J. Carretero, et al.The Astrophysical Journal Supplement Series 256 (1) 9 (2021)
https://doi.org/10.3847/1538-4365/ac0833