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Record W2170841342 · doi:10.1139/p05-013

A new path to ultracold hydrogen

2005· article· en· W2170841342 on OpenAlexvenueno aff
Robert deCarvalho, Nathan Brahms, Bonna Newman, John M. Doyle, Daniel Kleppner, Tom Greytak

Bibliographic record

VenueCanadian Journal of Physics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsnot available
FundersCenter for Ultracold Atoms, Massachusetts Institute of TechnologyNational Science Foundation
KeywordsHydrogenThermalisationPhysicsDeuteriumAtomic physicsLithium (medication)ScatteringEvaporative coolerHeliumOpticsThermodynamics

Abstract

fetched live from OpenAlex

Ultracold hydrogen offers unique possibilities for precision spectroscopy, studies of atomic interactions, and the creation of quantum fluids containing mixtures of hydrogen and deuterium. Current techniques for trapping and cooling hydrogen have produced large condensates with N ∼ 10 9 atoms, but suffer from a variety of experimental limitations. Among these are the slow evaporative cooling rate due to the small H–H elastic-scattering cross section, the need for a superfluid helium film in the initial thermalization process, a geometry that severely limits detection efficiency, and the inability to trap deuterium. We are constructing a new apparatus based on buffer-gas cooling that will overcome these problems. To accelerate evaporative cooling, the thermalization rate is increased by simultaneously loading lithium and hydrogen into a 4.2 T anti-Helmholtz trap. Lithium accelerates evaporative cooling because the Li–H elastic-scattering cross section is ∼1200 times larger than that of H–H. Hydrogen and lithium will be produced by laser ablation of solid LiH in a 3 He buffer gas held at temperatures of ∼350 mK. Because no wall collisions are needed for initial thermalization, ablating a solid sample of LiD should enable studies of deuterium. The apparatus is in the final stages of construction. We report on results of initial tests and discuss the new scientific opportunities made possible by this approach.PACS No.: 32.80.Pj

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.632

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

Explore more

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