Quantitative super-resolution solid immersion microscopy via refractive index profile reconstruction
Bibliographic record
Abstract
Solid Immersion (SI) microscopy is a modern imaging modality that overcomes the Abbe diffraction limit and offers novel applications in various branches of visible, infrared, terahertz, and millimeter-wave optics. Despite the widespread use, SI microscopy usually results in qualitative imaging. Indeed, it presents only the raw distributions (in the image plane) of the backscattered field intensity, while unlocking the information about the physical properties of an imaged object, such as its complex refractive index (RI) distribution, requires resolving the inverse problem and remains a daunting task. In this paper, a method for resolving the SI microscopy inverse problem is developed, capable of reconstructing the RI distribution at the object imaging plane with subwavelength spatial resolution, while performing only intensity measurements. The sample RI is retrieved via minimization of the error function that characterizes discrepancy between the experimental data and the predictions of analytical model. This model incorporates all the key features of the electromagnetic-wave interaction with the SI lens and an imaged object, including contributions of the evanescent and ordinary-reflected waves, as well as effects of light polarization and wide beam aperture. The model is verified numerically, using the finite-element frequency-domain method, and experimentally, using the in-house reflection-mode continuous-wave terahertz SI microscope. Spatial distributions of the terahertz RIs of different low-absorbing optical materials and highly absorbing biological objects were studied and compared to a priori known data to demonstrate the potential of the novel SI microscopy modality. Given the linear nature of the Maxwell’s equations, the developed method can be applied for subwavelength-resolution SI microscopy at other spectral ranges.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".