Application of the optimised next neighbour image cleaning method to the VERITAS array
Bibliographic record
Abstract
Imaging atmospheric Cherenkov telescopes, such as the VERITAS array, are subject to the Night Sky Background (NSB) and electronic noise, which contribute to the total signal of pixels in the telescope camera. The contribution of noise photons in event images is reduced with the application of image cleaning methods. Conventionally, high thresholds must be employed to ensure the removal of pixels containing noise signal. On that account, low-energy gamma-ray showers might be suppressed during the cleaning. We present here the application of an optimised next neighbour image cleaning for the VERITAS array. With this technique, differential noise rates are estimated for each individual observation and thus changes in the NSB and afterpulsing are consistently being accounted for. We show that this method increases the overall rate of reconstructed gamma-rays, lowers the energy threshold of the array and allows the reconstruction of low energy (E > 70 GeV) source events which were suppressed by the conventional cleaning method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".