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Record W4415026730 · doi:10.1038/s41467-025-64087-6

Week-long-lifetime microwave spectral holes in an erbium-doped scheelite crystal at millikelvin temperature

2025· article· en· W4415026730 on OpenAlexaff
Zhiren Wang, Sen Lin, Marianne Le Dantec, Miloš Rančić, Philippe Goldner, Sylvain Bertaina, T. Chanelière, Ren‐Bao Liu, D. Estève, D. Vion, Emmanuel Flurin, Patrice Bertet

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversité de Sherbrooke
FundersIntelligence Advanced Research Projects ActivityAgence Nationale de la RechercheEuropean Commission
KeywordsMicrowaveSpectral hole burningCrystal (programming language)ScheeliteSpin (aerodynamics)Spectral lineDopingModulation (music)

Abstract

fetched live from OpenAlex

Rare-earth-ions (REI) doped crystals have remarkable optical and spin properties characterized by narrow homogeneous linewidths, which can be studied despite the large inhomogeneous broadening of the ensemble line through spectral hole burning (SHB). Here, we report SHB spectroscopic measurements in a scheelite crystal of CaWO4 by pumping the spin transition of a paramagnetic REI (Er3+) at microwave frequency and millikelvin temperatures, with nuclear spin states of neighboring 183W atoms serving as the auxiliary levels. The repeated application of pairs of microwave pulses generates a periodic modulation of the Er3+ density profile, which we observe spectrally and in the time-domain as an accumulated echo. The lifetime of the holes and accumulated echoes rises steeply as the sample temperature is decreased, exceeding a week at 10mK. Our results demonstrate that millikelvin temperatures can be beneficial for signal processing applications requiring long spectral hole lifetimes. The authors report spectral hole burning with a record hole lifetime in a Er3+ doped scheelite crystal CaWO4 at mK temperatures. This can benefit applications in classical and quantum information processing.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.906

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 science0.0010.000
Research integrity0.0000.002
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 head0.010
GPT teacher head0.294
Teacher spread0.284 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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