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Record W4416012668 · doi:10.1051/0004-6361/202556177

<i>Euclid</i> preparation

2025· article· en· W4416012668 on OpenAlexaff
A Veropalumbo, A. Pugno, M. Moresco, C. Porciani, E Branchini, Michel-Andrès Breton, B. Camacho Quevedo, M. Crocce, S. de la Torre, Vincent Desjacques, Alexander Eggemeier, M. Kärcher, D. Linde, Marco Marinucci, Azadeh Moradinezhad Dizgah, Claudio Moretti, Kevin Pardede, E Sarpa, A. Amara, S Andreon, N Auricchio, C. Baccigalupi, D Bagot, M. Baldi, S. Bardelli, Pietro Battaglia, A. Biviano, M. Brescia, S. Camera, V. Capobianco, Chris Carbone, J. Carretero, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, A. Da Silva, H. Degaudenzi, G. De Lucia, H. Dole, M. Douspis, F. Dubath, X Dupac, S Dusini, M. Farina, R Farinelli, F Faustini, S. Ferriol, F Finelli, P. Fosalba, S. Fotopoulou, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, J. Gracia-Carpio, A Enia, F Grupp, L. Guzzo, W. Holmes, A. Hornstrup, K. Jahnkę, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M Kümmel, M Kunz, H Kurki-Suonio, S. Ligori, V. Lindholm, I Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, G. Meylan, A. Mora, B. Morin, L. Moscardini, E. Munari, C. Neissner, S.-M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S Pires, G. Polenta, M. Poncet, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, G. Testera, D Sapone, B. Sartoris, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. D. Seiffert, Patrice Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, I Tereno, N Tessore, Sune Toft, F. Torradeflot, A. Tsyganov, I Tutusaus, L Valenziano, J. Väliviita, T. Vassallo, Y. Wang, J Weller, E. Zucca, V. Allevato, F. La Franca, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, D. Di Ferdinando, L. Gabarra, J. Martín-Fleitas, S. Matthew, M. Maturi, N. Mauri, Achille Nucita, M. Pöntinen, I Risso, V. Scottez, M Sereno, M Tenti, M Viel, M. Wiesmann, Y. Akrami, M. Archidiacono, F. Atrio‐Barandela, A. Balaguera-Antolínez, Daniele Bertacca, M. Béthermin, L Blot, H. Böhringer, S. Borgani, S Bruton, A Calabrò, F Caro, T. Castro, F Cogato, Simon Conseil, S. Contarini, S. Davini, F. De Paolis, A. Díaz‐Sánchez, J.J Diaz, S. Di Domizio, Paola Dimauro, Y. Fang, A. Finoguenov, A. Franco, K. Ganga, J. García-Bellido, T Gasparetto, V Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, C. Hernández–Monteagudo, H Hildebrandt, J. Hjorth, Shahab Joudaki, Yongshui Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. Kirkpatrick, Sergey Kruk, M. Lattanzi, Maria Lembo, Fabio Lepori, G. Leroy, J. Lesgourgues, L. Leuzzi, A. Loureiro, J. F. Macías–Pérez, G. Maggio, M. Magliocchetti, L. Maurin, M Miluzio, P. Monaco, G. Morgante, K Naidoo, A. Navarro-Alsina, S Nesseris, L. Pagano, F Passalacqua, K. Paterson, L Patrizii, A Pisani, D. Potter, S Quai, M Radovich, P. Reimberg, P.-F Rocci, G. Rodighiero, S Sacquegna, M Sahlén, Aurel Schneider, D Sciotti, E. Sellentin, K Tanidis, Chihiro Tao, G. Testera, Romain Teyssier, Sébastien Tosi, A Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, Filippo Vernizzi, G Verza, P Vielzeuf

Bibliographic record

VenueAstronomy and Astrophysics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersFundação para a Ciência e a TecnologiaNorsk RomsenterAgenția Spațială RomânăNational Astronomical Observatory of JapanAgenzia Spaziale ItalianaMagyar Tudományos AkadémiaMinistero dell’Istruzione, dell’Università e della RicercaEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsGalaxyScalar (mathematics)Spectral densityPopulationCluster analysisAmplitudeSeries (stratigraphy)Consistency (knowledge bases)

Abstract

fetched live from OpenAlex

We investigated the accuracy and range of validity of the perturbative model for the two-point (2PCF) and three-point (3PCF) correlation functions in real space in view of the forthcoming analysis of the Euclid mission spectroscopic sample. We took advantage of clustering measurements from four snapshots of the Flagship I N -body simulations at z = {0.9,1.2,1.5,1.8}, which mimic the expected galaxy population in the ideal case, i.e. in the absence of observational effects such as purity and completeness. For the 3PCF we considered all available triangular configurations given a minimal separation ( r min ). We first assessed the model performance by fixing the cosmological parameters and evaluating the goodness of fit provided by the perturbative bias expansion in the joint analysis of the two statistics, finding an overall agreement with the data down to separations of 20 h −1 Mpc. Subsequently, we built on the state-of-the-art analysis and extended it to include the dependence on three cosmological parameters: the amplitude of scalar perturbations ( A s ), the matter density ( ω cdm ), and the Hubble parameter ( h ). To achieve this goal, we developed an emulator capable of generating fast and robust modelling predictions for the two summary statistics, which thus enables an efficient sampling of the joint likelihood function. We therefore present the first joint full-shape analysis of the real-space 2PCF and 3PCF, testing the consistency and constraining power of the perturbative model across both probes and assessing its performance in a combined likelihood framework. We explored possible systematic uncertainties induced by the perturbative model at small scales, finding an optimal scale cut of r min = 30 h −1 Mpc for the 3PCF when imposing an additional limitation on the nearly isosceles triangular configurations included in the data vector. This work is part of a series of papers in which we validate theoretical models for galaxy clustering measurements in preparation for the Euclid mission.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.150
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1500.070

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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