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Record W4396661440 · doi:10.1364/oe.523623

Pulse interaction induced systematic errors in dual comb spectroscopy

2024· article· en· W4396661440 on OpenAlexfundno aff
Mathieu Walsh, Esther Baumann, Nathan Malarich, Scott C. Egbert, Ryan K. Cole, Gregory B. Rieker, Nathan R. Newbury, Ian Coddington, Kevin C. Cossel, Jérôme Genest

Bibliographic record

VenueOptics Express · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsSpectroscopyPhysicsTransmittancePhase (matter)Spectral lineModulation (music)Materials scienceComputational physicsAcoustics

Abstract

fetched live from OpenAlex

Systematic errors are observed in dual comb spectroscopy when pulses from the two sources travel in a common fiber before interrogating the sample of interest. When sounding a molecular gas, these errors distort both the line shapes and retrieved concentrations. Simulations of dual comb interferograms based on a generalized nonlinear Schrodinger equation highlight two processes for these systematic errors. Self-phase modulation changes the spectral content of the field interrogating the molecular response but affects the recorded spectral baseline and absorption features differently, leading to line intensity errors. Cross-phase modulation modifies the relative inter-pulse delay, thus introducing interferogram sampling errors and creating a characteristic asymmetric distortion on spectral lines. Simulations capture the shape and amplitude of experimental errors which are around 0.1% on spectral transmittance residuals for 10 mW of total average power in 10 meters of common fiber, scaling up to above 0.6% for 20 mW and 60 m.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.578

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)0.0000.000

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 head0.018
GPT teacher head0.292
Teacher spread0.275 · 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 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

Citations9
Published2024
Admission routes1
Has abstractyes

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