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Record W1597766339 · doi:10.1142/9781783264766_0002

Making an Ultracold Gas

2014· book-chapter· en· W1597766339 on OpenAlexaffabout

Bibliographic record

VenueCold atoms · 2014
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Toronto
Fundersnot available
KeywordsOptical latticeUltracold atomDipoleQuantumAtom opticsDegeneracy (biology)Electric dipole momentUltracold neutronsMagnetic dipole

Abstract

fetched live from OpenAlex

We provide an introduction to the experimental physics of quantum gases. At the low densities of ultracold quantum gases, confinement can be understood from single-particle physics, and interactions can be understood from two-body physics. The structure of atoms provides resonances both in the optical domain and in the radio-frequency domain. Atomic structure data is given for the 27 atomic isotopes that had been brought to quantum degeneracy at the time this chapter was written. We discuss the motivations behind choosing among these species. We review how static and oscillatory fields are treated mathematically. An electric dipole moment can be induced in a neutral atom, and is the basis for optical manipulation as well as short-range interactions. Many atoms have permanent magnetic dipole moments, which can be used for trapping or long-range interactions. The Toronto $^{40}$K/$^{87}$Rb lattice experiment provides an illustration of how these tools are combined to create an ultracold, quantum-degenerate gas.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.006

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 head0.027
GPT teacher head0.258
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 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

Citations2
Published2014
Admission routes2
Has abstractyes

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