Image reconstruction from the Compton scattering of X-ray fan beams in thick/dense objects
Bibliographic record
Abstract
Image reconstruction from Compton scattering measurements results in a nonlinear inverse problem that can produce multiple solutions, particularly in deep/dense objects. In this work, the nonlinearity of the problem is mitigated by a transformation which minimizes the difference of the ratios of measurements with overlapping fields of view. Overlapping eliminates one of the competing factors in the nonlinear problem, and allows solving the inverse problem of image reconstruction beyond the one-mean-free path equivalent depth previously needed to reach a solution in scatter-only imaging. The method also enables the use of wide X-ray fan beam sources, and is numerically applied to exploit the ability of scatter imaging to acquire single-side two-dimensional (2-D) and three-dimensional (3-D) images of relatively dense objects. Examples are presented to demonstrate the method's ability to deal with noisy measurements, at various degrees of overdetermination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".