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

2021· article· en· W3159694649 on OpenAlexafffund
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, Miguel Cárdenas‐Montes, 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. Cicalò, L. Cifarelli, D. Cintas, F. Coccetti, V. Cocco, M. Colocci, E. Conde Vilda, L. Consiglio, S. Copello, J. Corning, G. Covone, P. Czudak, Mario 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, 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, M. A. 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, Chunkai Guo, B. R. Hackett, A. L. Hallin, M. Harańczyk, Stephen Hill, Shin Horikawa, F. Hubaut, T. Hugues, E. Hungerford, An. Ianni, V. Ippolito, C.C. James, C. Jillings, P. Kachru, A. Kemp, C. Kendziora, G. Keppel, A. V. Khomyakov, 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, N. Lami, S. Langrock, M. Leyton, X. Li, L. Lidey, F. Lippi, M. Lissia, N. Maccioni, I. N. Machulin, A. Marasciulli, A. Margotti, S. M. Mari, J. Maricic, M. Marinelli, M. Martínez, Alma D. Rojas, A. Martini, Michele Mascia, M. Masetto, A. Masoni, A. Mazzi, A. B. McDonald, J. B. McLaughlin, A. Messina, P. D. Meyers, A. Meregaglia, R. Milincic, Riccardo Miola, A. Moggi, A. Moharana, S. Moioli, J. Monroe, Stefano Morisi, M. Morrocchi, Elena Mozhevitina, Tomasz Mróz, V. N. 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. Pantic, 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, 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, E. García, W. Sands, S. Sanfilippo, V. A. Sangiorgio, V. Santacroce, D. Santone, R. Santorelli, Alessia 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, C. Sunny, V. Strickland, M. Stringer, S. Sulis, Y. Suvorov, A. M. Szelc, József-Zsolt Szücs-Balázs, R. Tartaglia, 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, Y. Wang, S. Westerdale, R. Wheadon, L. Williams, M. Wójcik, M. Wójcik, Xiang Xiao, C. Yang, A. Zani, F. Zenobio, A. Zichichi, G. Zuzel, M. P. Zykova

Bibliographic record

VenueThe European Physical Journal C · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsLaurentian UniversityUniversity of AlbertaSnolabQueen's UniversityCarleton UniversityTRIUMF
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 RechercheNarodowe Centrum NaukiEuropean Regional Development FundRegione Autonoma della SardegnaU.S. Department of EnergyFundacja na rzecz Nauki PolskiejEuropean CommissionNational Science FoundationRoyal SocietyIstituto Nazionale di Fisica NucleareCERNFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsDistillationDark matterPhysicsAnalytical Chemistry (journal)ChemistryAstrophysicsChromatography

Abstract

fetched live from OpenAlex

Abstract Aria is a plant hosting a $${350}\,\hbox {m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>350</mml:mn><mml:mspace/><mml:mtext>m</mml:mtext></mml:mrow></mml:math> cryogenic isotopic distillation column, the tallest ever built, which is being installed in a mine shaft at Carbosulcis S.p.A., Nuraxi-Figus (SU), Italy. Aria is one of the pillars of the argon dark-matter search experimental program, lead by the Global Argon Dark Matter Collaboration. It was designed to reduce the isotopic abundance of $${^{39}\hbox {Ar}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mrow/><mml:mn>39</mml:mn></mml:msup><mml:mtext>Ar</mml:mtext></mml:mrow></mml:math> in argon extracted from underground sources, called Underground Argon (UAr), which is used for dark-matter searches. Indeed, $${^{39}\hbox {Ar}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mrow/><mml:mn>39</mml:mn></mml:msup><mml:mtext>Ar</mml:mtext></mml:mrow></mml:math> is a $$\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>β</mml:mi></mml:math> -emitter of cosmogenic origin, whose activity poses background and pile-up concerns in the detectors. In this paper, we discuss the requirements, design, construction, tests, and projected performance of the plant for the isotopic cryogenic distillation of argon. We also present the successful results of the isotopic cryogenic 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.795

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations31
Published2021
Admission routes2
Has abstractyes

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Same venueThe European Physical Journal CSame topicDark Matter and Cosmic PhenomenaFrench-language works237,207