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Separating $^{39}$Ar from $^{40}$Ar by cryogenic distillation with Aria for dark matter searches

2021· preprint· en· W3123068396 on OpenAlexfundno aff
P. Agnes, S. Albergo, I. F. M. Albuquerque, T. Alexander, A. Alici, P. Amaudruz, M. Arba, Pasquale Arpaïa, S. Arcelli, M. Ave, Igor Avetissov, Roman Avetisov, O. Azzolini, H.O. Back, Z. Balmforth, V. Barbarian, A. Barrado Olmedo, P. Barrillon, A. Basco, G. Batignani, A. Bondar, W. Bonivento, E. Borisova, B. Bottino, M. G. Boulay, G. Buccino, S. Bussino, J. Busto, A. Buzulutskov, M. Cadeddu, Mariano Cadoni, A. Caminata, E. V. Canesi, N. Canci, G. Cappello, M. Caravati, N. Cargioli, F. Carnesecchi, Paolo Castello, A. Castellani, S. Catalanotti, V. Cataudella, P. Cavalcante, S. Cavuoti, S. Cebrián, J. M. Cela Ruiz, B. Celano, S. Chashin, A. Chepurnov, C. Cical, L. Cifarelli, D. Cintas, F. Coccetti, V. Cocco, M. Colocci, E. Conde Vilda, L. Consiglio, S. Copello, J. Corning, G. Covone, P. Czudak, M. D’Aniello, S. D’Auria, M. Rolo, O. Dadoun, M. K. Daniel, S. Davini, A. De Candia, S. De Cecco, A. De Falco, G. De Filippis, D. De Gruttola, Giorgia De Guido, G. De Rosa, M. Della Valle, G. Dellacasa, S. De Pasquale, A. Derbin, A. Devoto, L. Di Noto, F. Di Eusanio, C. Dionisi, Stefano Morisi, G. Dolganov, Danilo Nicola Dongiovanni, F. Dordei, M. Downing, T. Erjavec, S. Falciano, S. Farenzena, M. Fernández Díaz, Claudiu Filip, G. Fiorillo, A. Franceschi, D. Franco, E. Frolov, N. Funicello, F. Gabriele, C. Galbiati, M. Garbini, P. Garcı́a-Abia, A. Gendotti, C. Ghiano, Raffaele Aaron Giampaolo, C. Giganti, F. M. Giorgi, G. K. Giovanetti, Mihai Gligan, V. Goicoechea Casanueva, A. Gola, R. Graciani Diaz, G. Y. Grigoriev, A. Grobov, M. Gromov, M. Guan, M. Guerzoni, M. Guetti, M. Gulino, Cong Guo, B. R. Hackett, A. L. Hallin, M. Harańczyk, Stephen Hill, Shin Horikawa, F. Hubaut, T. Hugues, E. V. Hungerford, An. Ianni, V. Ippolito, C.C. James, C. Jillings, P. Kachru, A. Kemp, C. Kendziora, G. Keppel, A. V. Khomyakov, S. Kim, I. Kochanek, K. Kondo, G. Korga, A. Kubankin, R. Kugathasan, M. Kuss, M. Kuźniak, M. La Commara, L. La Delfa, D. La Grasta, M. Laí, Nnamonu Lami, S. Langrock, M. Leyton, X. Li, L. Lidey, F. Lippi, M. Lissia, G. Longo, N. Maccioni, I. Machulin, L. Mapelli, A. Marasciulli, A. Margotti, S. M. Mari, J. Maricic, M. Marinelli, M. Mart, Alma D. Rojas, A. Martini, C. J. Martoff, Michele Mascia, M. Masetto, A. Masoni, A. Mazzi, A. B. McDonald, J. Lin, A. Messina, P. D. Meyers, A. Meregaglia, Riccardo Miola, A. Moggi, A. Moharana, Stefania Moioli, J. Monroe, M. Morrocchi, Elena Mozhevitina, Trofimovskiĭ Mr, V. Muratova, A. Murenu, C. Muscas, Ludovico Musenich, R. Nania, T. Napolitano, A. Navrer Agasson, M. Nessi, I. S. Nikulin, J. Nowak, A. Oleinik, V. Oleynikov, L. Pagani, M. Pallavicini, Simonetta Palmas, L. Pandola, E. Paoloni, G. Paternoster, P. A. Pegoraro, Laura A. Pellegrini, C. Pellegrino, K. Pelczar, F. Perotti, V. Pesudo, E. Picciau, F. Pietropaolo, T. Pinna, A. Pocar, P. Podda, D. M. Poehlmann, S. Pordes, S. S. Poudel, P. Pralavorio, D. Price, F. Raffaelli, F. Ragusa, A. Ramirez, M. Razeti, A. Razeto, A. Renshaw, S. Rescia, M. Rescigno, F. Resnati, F. Retière, L. P. Rignanese, C. Ripoli, A. Rivetti, J. Rode, L. Romero, M. Rossi, A. Rubbia, M. Rucaj, G. M. Sabiu, Piero Salatino, O. Samoylov, W. Sands, S. Sanfilippo, V. A. Sangiorgio, V. Santacroce, D. Santone, R. Santorelli, A. Santucci, C. Savarese, E. Scapparone, B. Schlitzer, G. Scioli, Д. А. Семенов, B. Shaw, A. Shchagin, A. Sheshukov, M. Simeone, P. Skensved, M. D. Skorokhvatov, O. Smirnov, B. Smith, A. Sokolov, R. Stefanizzi, A. Steri, S. Stracka, V. Strickland, M. Stringer, S. Sulis, Y. Suvorov, A. M. Szelc, J.Z. Zsücs-Balázs, R. Tartaglia, G. Testera, T.N. Thorpe, A. Tonazzo, S. Torres-Lara, Silvano Tosti, A. Tricomi, Matteo Tuveri, E. Unzhakov, G. Usai, T. Vallivilayil John, S. Vescovi, T. Viant, S. Viel, A. Vishneva, R. B. Vogelaar, M. Wada, H. Wang, Yi Wang, S. Westerdale, R. Wheadon, L. Williams, M. Wójcik, Xiang Xiao, Changgen Yang, A. Zani, F. Zenobio, A. Zichichi, G. Zuzel, M. P. Zykova

Bibliographic record

VenueIris (Roma Tre University) · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersLabex UnivEarthSPacific Northwest National LaboratoryCentro de Investigaciones Energéticas, Medioambientales y TecnológicasScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e TecnológicoBattelleAgence Nationale de la RechercheCERNIstituto Nazionale di Fisica NucleareEuropean Regional Development FundU.S. Department of EnergyFundacja na rzecz Nauki PolskiejEuropean CommissionNational Science FoundationRoyal SocietyFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsArgonDistillationDark matterEnvironmental sciencePhysicsChemistryAstrophysicsChromatographyAtomic physics

Abstract

fetched live from OpenAlex

Aria is a plant hosting a 350 m cryogenic iso-
\ntopic distillation column, the tallest ever built, which is being
\ninstalled in a mine shaft at Carbosulcis S.p.A., Nuraxi-Figus
\n(SU), Italy. Aria is one of the pillars of the argon dark-matter
\nsearch experimental program, lead by the Global Argon Dark
\nMatter Collaboration. It was designed to reduce the isotopic
\nabundance of 39 Ar in argon extracted from underground
\nsources, called Underground Argon (UAr), which is used for
\ndark-matter searches. Indeed, 39 Ar is a β-emitter of cos-
\nmogenic origin, whose activity poses background and pile-
\nup concerns in the detectors. In this paper, we discuss the
\nrequirements, design, construction, tests, and projected per-
\nformance of the plant for the isotopic cryogenic distillation of
\nargon. We also present the successful results of the isotopic
\ncryogenic distillation of nitrogen with a prototype plant.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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 teacher head, not a consensus.

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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

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