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Record W4293166369 · doi:10.1051/0004-6361/201527206

<i>Planck</i>intermediate results

2016· article· en· W4293166369 on OpenAlexaff
P. A. R. Ade, N. Aghanim, M. Arnaud, J. Aumont, C. Baccigalupi, A. J. Banday, R. B. Barreiro, N. Bartolo, E. Battaner, K. Benabed, A. Benoit-Lévy, J.-P. Bernard, M. Bersanelli, P. Bielewicz, A. Bonaldi, L. Bonavera, J. R. Bond, J. Borrill, F. R. Bouchet, F. Boulanger, C. Burigana, R. C. Butler, E. Calabrese, A. Catalano, H. C. Chiang, P. R. Christensen, D. L. Clements, L. P. L. Colombo, F. Couchot, A. Coulais, B. P. Crill, A. Curto, F. Cuttaia, L. Danese, R. D. Davies, Roger J. Davis, P. de Bernardis, A. de Rosa, G. de Zotti, C. Dickinson, J. M. Diego, H. Dole, O. Doré, M. Douspis, A. Ducout, X. Dupac, F. Elsner, T. A. Enßlin, H. K. Eriksen, É. Falgarone, F. Finelli⋆, I. Flores-Cacho, M. Frailis, A. A. Fraisse, E. Franceschi, S. Galeotta, S. Galli, K. Ganga, M. Giard, Y. Giraud–Héraud, E. Gjerløw, J. González-Nuevo, K. M. Górski, A. Gregorio, A. Gruppuso, J. E. Gudmundsson, F. K. Hansen, D. L. Harrison, G. Hélou, C. Hernández-Monteagudo, D. Herranz, S. R. Hildebrandt, E. Hivon, A. Hornstrup, W. Hovest, K. M. Huffenberger, G. Hurier, A. H. Jaffe, T. R. Jaffe, E. Keihänen, R. Keskitalo, T. S. Kisner, R. Kneißl, J. Knoche, M. Kunz, H. Kurki‐Suonio, G. Lagache, J.‐M. Lamarre, A. Lasenby, M. Lattanzi, R. Leonardi, F. Levrier, M. Liguori, P. B. Lilje, M. Linden-Vørnle, M. López-Caniego, P. M. Lubin, J. F. Macías–Pérez, B. Maffei, G. Maggio, D. Maino, N. Mandolesi, A. Mangilli, M. Maris, P. G. Martin, E. Martínez-González, S. Masi, S. Matarrese, A. Melchiorri, A. Mennella, M. Migliaccio, S. Mitra, M.-A. Miville-Deschênes, A. Moneti, L. Montier, G. Morgante, D. Mortlock, D. Munshi, J. A. Murphy, F. Nati, P. Natoli, N. P. H. Nesvadba, F. Noviello, D. Novikov, I. Novikov, C. A. Oxborrow, L. Pagano, F. Pajot, D. Paoletti, B. Partridge, F. Pasian, T. J. Pearson, O. Perdereau, L. Perotto, V. Pettorino, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, G. Polenta, G. W. Pratt, S. Prunet, J.‐L. Puget, J. P. Rachen, M. Reinecke, M. Remazeilles, C. Renault, A. Renzi, I. Ristorcelli, G. Rocha, C. Rosset, M. Rossetti, G. Roudier, J. A. Rubiño-Martín, B. Rusholme, M. Sandri, D. Santos, М. Савелайнен, G. Savini, D. Scott, L. D. Spencer, V. Stolyarov, R. Stompor, R. Sudiwala, R. Sunyaev, A.-S. Suur-Uski, J.-F. Sygnet, J. A. Tauber, L. Terenzi, L. Toffolatti, M. Tomasi, M. Tristram, M. Türler, G. Umana, L. Valenziano, J. Väliviita, F. Van Tent, P. Vielva, F. Villa, L. A. Wade, B. D. Wandelt, I. K. Wehus, N. Welikala, D. Yvon, A. Zacchei, A. Zonca

Bibliographic record

VenueAstronomy and Astrophysics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersFundação para a Ciência e a TecnologiaTekesScience and Technology Facilities CouncilInstitut National de Physique Nucléaire et de Physique des ParticulesMinistério da Ciência, Tecnologia e Ensino SuperiorCentre National de la Recherche ScientifiqueNational Aeronautics and Space AdministrationChina Scholarship CouncilCentre National d’Etudes SpatialesPartnership for Advanced Computing in Europe AISBLMax-Planck-GesellschaftUK Space AgencyScience Foundation Ireland
KeywordsPhysicsAstrophysicsPlanckAstronomy

Abstract

fetched live from OpenAlex

The Planck mission, thanks to its large frequency range and all-sky coverage, has a unique potential for systematically detecting the brightest, and rarest, submillimetre sources on the sky, including distant objects in the high-redshift Universe traced by their dust emission. A novel method, based on a component-separation procedure using a combination of Planck and IRAS data, has been validated and characterized on numerous simulations, and applied to select the most luminous cold submillimetre sources with spectral energy distributions peaking between 353 and 857 GHz at 5′ resolution. A total of 2151 Planck high-z source candidates (the PHZ) have been detected in the cleanest 26% of the sky, with flux density at 545 GHz above 500 mJy. Embedded in the cosmic infrared background close to the confusion limit, these high-z candidates exhibit colder colours than their surroundings, consistent with redshifts z > 2, assuming a dust temperature of Txgal = 35 K and a spectral index of βxgal = 1.5. Exhibiting extremely high luminosities, larger than 1014L⊙, the PHZ objects may be made of multiple galaxies or clumps at high redshift, as suggested by a first statistical analysis based on a comparison with number count models. Furthermore, first follow-up observations obtained from optical to submillimetre wavelengths, which can be found in companion papers, have confirmed that this list consists of two distinct populations. A small fraction (around 3%) of the sources have been identified as strongly gravitationally lensed star-forming galaxies at redshift 2 to 4, while the vast majority of the PHZ sources appear as overdensities of dusty star-forming galaxies, having colours consistent with being at z > 2, and may be considered as proto-cluster candidates. The PHZ provides an original sample, which is complementary to the Planck Sunyaev-Zeldovich Catalogue (PSZ2); by extending the population of virialized massive galaxy clusters detected below z < 1.5 through their SZ signal to a population of sources at z > 1.5, the PHZ may contain the progenitors of today’s clusters. Hence the Planck list of high-redshift source candidates opens a new window on the study of the early stages of structure formation, particularly understanding the intensively star-forming phase at high-z.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.222
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0040.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2220.204

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 head0.187
Teacher spread0.182 · 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 designObservational
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".

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Citations54
Published2016
Admission routes1
Has abstractyes

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