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Borromean supercounterfluids at finite temperatures

2025· article· en· W4406421096 on OpenAlexaff
Alexandru Golic, Igor Timoshuk, Egor Babaev, Boris Svistunov

Bibliographic record

VenuePhysical Review Research · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsRoyal Ottawa Mental Health Centre
FundersVetenskapsrådetStiftelsen Olle Engkvist ByggmästareKnut och Alice Wallenbergs StiftelseNational Science Foundation
KeywordsPhysicsEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

While the properties of standard (single-component) superfluids are well understood, principal differences arise in a special type of multicomponent systems—the so-called Borromean supercounterfluids—in which (i) supertransport is possible only in the counterflow regime and (ii) there are three or more counterflowing components. Borromean supercounterfluids's correlation and topological properties distinguish them from their single- and two-component counterparts. The component-symmetric case characterized by a distinctively different universality class of the supercounterfluid-to-normal phase transition is especially interesting. Using the recently introduced concept of compact-gauge invariance as the guiding principle, we develop the finite-temperature description of Borromean supercounterfluids in terms of an asymptotically exact long-wave effective action. We formulate and study Borromean <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:mi>X</a:mi> <a:mi>Y</a:mi> </a:mrow> </a:math> and loop statistical models, capturing the universal long-range properties and allowing us to perform efficient worm algorithm simulations. Numeric results demonstrate perfect agreement with analytic predictions. Particularly instructive is the two-dimensional case, where the Borromean nature of the system is strongly manifested while allowing for an asymptotically exact analytic description.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.423
Teacher spread0.385 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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