Development of double-foil soft X-ray array imaging (DSXAI) diagnostic on HL-2A tokamak
Bibliographic record
Abstract
Abstract A 100-channel double-foil soft X-ray array imaging (DSXAI) diagnostic system has been developed for the HL-2A tokamak to obtain tomographic bremsstrahlung emissivity and electron temperature (T e). This system employs a double-foil technique to determine T e by comparing the soft X-ray (SXR) emissivities from the same plasma location through two beryllium (Be) foils of differing thickness. The DSXAI system comprises five photocameras mounted at two different poloidal cross-sections, separated toroidally by 15°, allowing for three distinct poloidal viewing angles. Each photocamera features 20 channels, offering a temporal resolution of approximately 4 μs and a spatial resolution of about 8 cm, with no channel overlap. Each photocamera contains two identical optical systems, each defined by an aperture slit and a photodiode array. The double-foil configuration is realized by placing these two optical systems, each with a different Be foil, in close proximity. Initial experimental results demonstrate that the DSXAI diagnostic system performs well, successfully reconstructing 2-dimensional (2D) tomographic SXR emissivity and T e on the HL-2A tokamak. This study provides valuable insights for the future implementation of similar diagnostic systems on fusion reactors like ITER.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".