Sensitive detection of electric field-induced second harmonic (E-FISH) signals
Bibliographic record
Abstract
We demonstrate sensitive electric field measurements by coherent homodyne amplification of electric-field induced second harmonic generation (E-FISH) technique. In the process of E-FISH, an applied electric field breaks centrosymmetryof an otherwise homogeneous medium, in turn promoting generation of second harmonic frequency of an incident field. Due to weak third-order hyperpolarizability and requirement of an applied field to break the symmetry, the E-FISH technique has been mainly used to study high fields, also requiring strong optical field and sensitive detection. Here we superimpose the E-FISH signal with an auxilliary beam,also termed a local oscillator (LO), at double of the incident frequency. Coherent superposition of the LO and the E-FISH output allows for a homodyne amplification of the otherwise weak nonlinear signal. We have demonstrated an increase of signal to noise by a factor of 7, which results in a measurement time reduction of a factor of 49. This technique, local oscillator E-FISH, LOE-FISH, has a number of advantages: Detection with intensified detectors is not required. Furthermore, instead of mJ pulsed lasers, we can work with μJ pulsed lasers, which allows measuring at repetition rates of MHz and opens single shot and real time capability. The LOE-FISH technique increases in sensitivity at lower electric field. Our work is a demonstration of the principle. Already with our first results from the demonstration one can see the high potential of LOE-FISH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".