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Carrier-Suppressed Multiple-Single-Sideband Laser Source for Atom Cooling and Interferometry

2021· article· en· W3206475704 on OpenAlexaff
Simon Templier, J. Hauden, P. Cheiney, Fabien Napolitano, H. Porte, Philippe Bouyer, B. Barrett, Baptiste Battelier

Bibliographic record

VenuePhysical Review Applied · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of New Brunswick
FundersAgence Nationale de la RechercheEuropean Space Agency
KeywordsPhysicsSidebandInterferometryAtom interferometerLaserAstronomical interferometerGravimeterOpticsRadio frequencyTelecommunicationsMicrowave

Abstract

fetched live from OpenAlex

We present an electro-optic modulation technique that enables a single laser diode to realize a cold-atom source and a quantum inertial sensor based on matter-wave interferometry. Using carrier-suppressed dual-single-sideband modulation, an in-phase and quadrature modulator generates two optical sidebands from separate radio-frequency (rf) signals. These sidebands are controlled independently in frequency, phase, and power using standard rf components. Our laser source exhibits improved rejection of parasitic sidebands compared to those based on phase modulators, which generate large systematic shifts in atom interferometers. We measure the influence of residual laser lines on an atom-interferometric gravimeter and show agreement with a theoretical model. We estimate a reduction of the systematic shift by 2 orders of magnitude compared to previous architectures and reach a long-term sensitivity of $15\phantom{\rule{0.2em}{0ex}}\mathrm{n}g$ on the gravitational acceleration with an interrogation time of only $T=20\phantom{\rule{0.2em}{0ex}}\mathrm{ms}$. Finally, we characterize the performance of our integrated laser system and show that it is suitable for mobile sensing applications, including gravity surveys and inertial navigation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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