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Record W4416857423 · doi:10.1364/boe.579250

Bessel beam side lobe suppression via non-degenerate two-photon excitation

2025· article· en· W4416857423 on OpenAlexfundno aff
Stephen Tucker, Ezra Guralnik, Shy Shoham

Bibliographic record

VenueBiomedical Optics Express · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
FundersYork University
KeywordsBessel beamAxiconSide lobeBessel functionPoint spread functionImage qualityExcitationFemtosecondMain lobeBeam (structure)

Abstract

fetched live from OpenAlex

Bessel beams are commonly used in two-photon microscopy to extend the depth of field and thereby achieve functional volumetric imaging of the living brain. In practice, this approach suffers from background signals and limited lateral resolution due to the Bessel beam's strong side lobes. We introduce and demonstrate a new approach to side lobe suppression based on non-degenerate two-photon excitation, in which dual wavelength illumination produces an imaging point-spread function that is the product of the two coaxial Bessel beams. This technique can reduce the main side lobe intensity of a Bessel beam by 50% or more. We illustrate the approach conceptually with an analytical paraxial model and use detailed physical simulation to show that the approach is effective in the presence of the symmetry-breaking aberrations that amplify side lobes in high NA systems. We experimentally demonstrated the technique using a refractive axicon and the pump and tunable beams of a femtosecond laser. This work establishes non-degenerate two-photon excitation as a practical and broadly applicable strategy for improving point spread-function quality in high-resolution volumetric microscopy.

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: none
Teacher disagreement score0.563
Threshold uncertainty score0.999

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 head0.006
GPT teacher head0.293
Teacher spread0.287 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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