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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 350m 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 39Ar in argon extracted from underground sources, called Underground Argon (UAr), which is used for dark-matter searches. Indeed, 39Ar is a -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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 source (direct Gemma or distilled Codex), not a consensus.

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

Same venueIris (Roma Tre University)Same topicDark Matter and Cosmic PhenomenaFrench-language works237,207