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Record W3129468607 · doi:10.1126/science.abh2634

Approaching the motional ground state of a 10-kg object

2021· article· en· W3129468607 on OpenAlexaff

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of British Columbia
FundersAustralian Research CouncilScience and Technology Facilities CouncilMinistry of Public Security of the People's Republic of ChinaCalifornia Institute of TechnologyMassachusetts Institute of TechnologyMax-Planck-GesellschaftNational Science Foundation
KeywordsQuantumGround stateThermalQuantum stateObject (grammar)Thermal statePhononMotion (physics)Quantum dynamics

Abstract

fetched live from OpenAlex

The motion of a mechanical object, even a human-sized object, should be governed by the rules of quantum mechanics. Coaxing them into a quantum state is, however, difficult because the thermal environment masks any quantum signature of the object's motion. The thermal environment also masks the effects of proposed modifications of quantum mechanics at large mass scales. We prepared the center-of-mass motion of a 10-kilogram mechanical oscillator in a state with an average phonon occupation of 10.8. The reduction in temperature, from room temperature to 77 nanokelvin, is commensurate with an 11 orders-of-magnitude suppression of quantum back-action by feedback and a 13 orders-of-magnitude increase in the mass of an object prepared close to its motional ground state. Our approach will enable the possibility of probing gravity on massive quantum systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.339
Teacher spread0.318 · 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 designSimulation or modeling
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

Citations92
Published2021
Admission routes1
Has abstractyes

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