Measurement of the efficiency of the pattern recognition of tracks generated by ionizing radiation in a TIMEPIX detector
Bibliographic record
Abstract
A hybrid silicon pixelated TIMEPIX detector (256 × 256 square pixels with a pitch of 55 μm) operated in Time Over Threshold (TOT) mode was exposed to radioactive sources and protons after Rutherford Backscattering on a thin gold foil of protons beams delivered by the Tandem Accelerator of the Montreal University. Simultaneous exposure of TIMEPIX to radioactive sources and to protons beams on top of the radioactive sources allowed measurements with different mixed radiation fields of protons, alpha-particles, photons and electrons. All measurements were performed in vacuum. The comparison of the experimental activities (determined from the measurement of the number of tracks left in the device by incoming particles) of the radioactive sources with their expected activities allowed the test of the device efficiency for track recognition. The efficiency of track recognition of incident protons of different energies as a function of the incidence angle was measured. The cluster size left by protons in the device was measured as a function of their incident energy at normal and large (75°) incident angles. The operation of TIMEPIX in TOT mode has allowed a 3D mapping of the charge spreading effect in the whole volume of the silicon sensor. The results of the present measurements demonstrate the TIMEPIX capability of differentiating between different types of particles species from mixed radiation fields and measuring their energy deposition. Single track analysis gives a good precision (significantly better than the 55 μm size of one detector pixel) on the coordinates of the impact point of protons with normal incidence interacting in the TIMEPIX silicon layer.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".