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Record W4286288926 · doi:10.1051/0004-6361/202141938

<i>Euclid</i>preparation

2022· article· en· W4286288926 on OpenAlexaff
R. Scaramella, J. Amiaux, Y. Mellier, C. Burigana, C. S. Carvalho, Jean‐Charles Cuillandre, A. Da Silva, A. Derosa, J. Dinis, E. Maiorano, M. Maris, I. Tereno, R. Laureijs, Tobias Boenke, Guillermo Buenadicha, X. Dupac, Luis M. Gaspar Venancio, P. Gómez-Álvarez, José Lorenzo Alvarez, G. D. Racca, G. Saavedra-Criado, Joel Schwartz, R. Vavrek, M Schirmer, R. Azzollini, V. F. Cardone, M. Cropper, A. Ealet, B. Garilli, W. Gillard, B. R. Granett, L. Guzzo, Henk Hoekstra, K. Jahnkę, T. Kitching, T. Maciaszek, M. Meneghetti, L. Miller, R. Nakajima, S. M. Niemi, F. Pasian, Will J. Percival, S. Pottinger, M. Sauvage, M. Scodeggio, Stefanie Wachter, A. Zacchei, N. Aghanim, A. Amara, T. Auphan, N. Auricchio, Saeeda Awan, A. Balestra, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, S. Brau-Nogue, M. Brescia, Gian Paolo Candini, V. Capobianco, C. Carbone, R. G. Carlberg, J. Carretero, R. Casas, F. J. Castander, M. Castellano, S. Cavuoti, A Cimatti, R. Clédassou, G. Congedo, Christopher J. Conselice, L. Conversi, Y. Copin, L. Corcione, A. Costille, F. Courbin, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, S. Dusini, S. Farrens, S. Ferriol, P. Fosalba, N. Fourmanoit, M. Frailis, E. Franceschi, P. Franzetti, M. Fumana, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. A. Holmes, F. Hormuth, P. Hudelot, S. Kermiche, A. Kiessling, M. Kilbinger, R. Kohley, B. Kubik, M Kümmel, M. Kunz, H. Kurki‐Suonio, O. Lahav, S. Ligori, P. B. Lilje, I. Lloro, O Mansutti, O. Marggraf, K. Markovič, F. Marulli, R. Massey, S. Maurogordato, M. Melchior, E. Merlin, G. Meylan, J. J. Mohr, M. Moresco, B. Morin, L. Moscardini, E. Munari, R. C. Nichol, S. Paltani, J. A. Peacock, K. Pedersen, V. Pettorino, S. Pires, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rébolo, Jason Rhodes, Hans‐Walter Rix, M. Roncarelli, E. Rossetti, R. P. Saglia, Peter Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stančo, Jean‐Luc Starck, P. Tallada-Crespí, D. Tavagnacco, Andy Taylor, Harry I. Teplitz, R. Toledo-Moreo, F. Torradeflot, M. Trifoglio, E. A. Valentijn, L. Valenziano, G. Verdoes Kleijn, Yun Wang, N. Welikala, J. Weller, Michael E. Wetzstein, G. Zamorani, J. Zoubian, S. Andreon, Marco Baldi, S. Bardelli, A. Boucaud, S. Camera, D. Di Ferdinando, Giulio Fabbian, R. Farinelli, S. Galeotta, J. Graciá‐Carpio, E. Medinaceli, S. Mei, G. Polenta, A. Renzi, E. Romelli, C. Rosset, F. Sureau, M. Tenti, T. Vassallo, E. Zucca, C. Baccigalupi, A. Balaguera-Antolínez, P Battaglia, A. Biviano, S. Borgani, E. Bozzo, R. Cabanac, A. Cappi, Santiago Casas, G. Castignani, C Colodro-Conde, J. Coupon, Jean-Gabriel Cuby, S. de la Torre, S. Desai, H. Dole, Maximilian Fabricius, M. Farina, P.G Ferreira, F. Finelli⋆, P. Flose-Reimberg, S. Fotopoulou, K. Ganga, G. Gozaliasl, I. Hook, E. Keihänen, C.C Kirkpatrick, P. Liebing, V. Lindholm, G Mainetti, M. Martinelli, N. Martinet, M. Maturi, H. J. McCracken, R. B. Metcalf, G. Morgante, J.W Nightingale, Achille Nucita, L. Patrizii, D. Potter, G. Riccio, A. G. Sánchez, D. Sapone, J.A Schewtschenko, M. Schultheis, V Scottez, Romain Teyssier, I. Tutusaus, J. Väliviita, Matteo Viel, Willem-Jan Vriend, L. Whittaker

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of TorontoPerimeter InstituteUniversity of Waterloo
FundersStaatssekretariat für Bildung, Forschung und InnovationFundação para a Ciência e a TecnologiaNorsk RomsenterAgenția Spațială RomânăNational Astronomical Observatory of JapanAgenzia Spaziale ItalianaAcademy of FinlandEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsAstronomy

Abstract

fetched live from OpenAlex

Euclid is a mission of the European Space Agency that is designed to constrain the properties of dark energy and gravity via weak gravitational lensing and galaxy clustering. It will carry out a wide area imaging and spectroscopy survey (the Euclid Wide Survey: EWS) in visible and near-infrared bands, covering approximately 15 000 deg 2 of extragalactic sky in six years. The wide-field telescope and instruments are optimised for pristine point spread function and reduced stray light, producing very crisp images. This paper presents the building of the Euclid reference survey: the sequence of pointings of EWS, deep fields, and calibration fields, as well as spacecraft movements followed by Euclid as it operates in a step-and-stare mode from its orbit around the Lagrange point L2. Each EWS pointing has four dithered frames; we simulated the dither pattern at the pixel level to analyse the effective coverage. We used up-to-date models for the sky background to define the Euclid region-of-interest (RoI). The building of the reference survey is highly constrained from calibration cadences, spacecraft constraints, and background levels; synergies with ground-based coverage were also considered. Via purposely built software, we first generated a schedule for the calibrations and deep fields observations. On a second stage, the RoI was tiled and scheduled with EWS observations, using an algorithm optimised to prioritise the best sky areas, produce a compact coverage, and ensure thermal stability. The result is the optimised reference survey RSD_2021A, which fulfils all constraints and is a good proxy for the final solution. The current EWS covers ≈14 500 deg 2 . The limiting AB magnitudes (5 σ point-like source) achieved in its footprint are estimated to be 26.2 (visible band I E ) and 24.5 (for near infrared bands Y E , J E , H E ); for spectroscopy, the H α line flux limit is 2 × 10 −16 erg −1 cm −2 s −1 at 1600 nm; and for diffuse emission, the surface brightness limits are 29.8 (visible band) and 28.4 (near infrared bands) mag arcsec −2 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.814
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1860.161

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.198
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations477
Published2022
Admission routes1
Has abstractyes

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