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Record W4224085681 · doi:10.21203/rs.3.rs-1522413/v1

Quantum coherence of a circularly accelerated atom in a spacetime with a reflecting boundary

2022· preprint· en· W4224085681 on OpenAlexfundno aff
Wanhe Zhang, Xiaobao Liu, Tingli Yang, Kun Zhu

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicQuantum Electrodynamics and Casimir Effect
Canadian institutionsnot available
FundersLiupanshui Normal UniversityDepartment of Education of Guizhou ProvincePetroleum Technology Research CentreNational Natural Science Foundation of China
KeywordsPhysicsCoherence (philosophical gambling strategy)Scalar fieldQuantumQuantum mechanicsQuantum field theory in curved spacetimeBoundary value problemSpacetimeScalar (mathematics)Boundary (topology)Massless particleQuantum fluctuationClassical mechanicsQuantum electrodynamicsGeometryQuantum gravityMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract We investigate, in the paradigm of open quantum systems, the dynamics of quantum coherence of a circularly accelerated atom coupled to a bath of vacuum fluctuating massless scalar field in a spacetime with a reflecting boundary. The master equation that governs the system evolution is derived. It is found that the quantum coherence diminishes to zero with increasing centripetal acceleration and evolution time in the case without a boundary. However, the presence of a boundary will modify the quantum fluctuations of the scalar field, which results in the enhancement of quantum coherence near the boundary compared with that for the unbounded case. Particularly, when the atom is very close to the boundary, although the atom still interacts with the environment, it behaves as if it were a closed system as a consequence of the presence of the boundary, and the quantum coherence can be shielded from the effect of the vacuum fluctuating scalar field.PACS numbers: 03.65.Yz, 03.67.Mn, 03.65.Ta

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.401
Teacher spread0.340 · 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 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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