Commissioning and Testing of IceAct Telescopes at the IceCube Neutrino Observatory
Bibliographic record
Abstract
IceAct is an array of imaging air Cherenkov telescopes located at the ice surface above the IceCube Neutrino Observatory. Each telescope features a silicon photomultiplier-based 61-pixel camera and a Fresnel-lens as imaging optic, resulting in a 12-degree field of view. The design is optimized to be operated in harsh environments, particularly at the South Pole. The setup will consist of seven telescopes in a so-called fly’s eye configuration, increasing the field of view to 36°, and an additional telescope 200m apart for stereoscopic observations. Rigorous testing procedures have been performed before deployment to ensure that operation under these conditions is possible, e.g. night sky observations and cold temperature tests. Furthermore, on-site calibrations are used to verify the accuracy and reliability of the installation. We derive the geometric alignment of each IceAct telescope by comparing the directional reconstruction of muons measured with IceCube to the corresponding primary particle direction reconstruction from IceAct. This contribution presents these testing procedures. Additionally, we present the on-site alignment calibration, including a Graph Neural Network reconstruction for the primary particle direction in IceAct, verification on Monte Carlo simulation, and the application to a commissioning dataset.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".