Event Classification Using Machine Learning for the ARGO Dark Matter Detector
Bibliographic record
Abstract
Physicists are investing significant effort searching for dark matter using advanced detectors. The ARGO detector is being designed to be one of the most advanced and sophisticated such experiments, using liquid argon as the detection medium. ARGO plans to deploy Single Photon Avalanche Diodes (SPAD) on the surface of the vessel, which totals$200 \sim \mathrm{m}^{2}$. The SPAD matrices will be arranged in$\text{mm}^{2}$units, requiring the management of millions of data channels simultaneously. This presents major challenges, including high power consumption, extensive cabling, and the need for expensive high-speed storage, leading to higher costs. To address these challenges, we investigate the use of real-time machine-learning (ML) algorithms designed to identify and classify particle types in order to preserve dark matter signals while rejecting background events. We designed a Convolutional Neural Network (CNN) model to classify events into three particle types: 39Ar beta decays, 40Ar nuclear recoils, and alpha decays. Further work will include more particle identification and confidence measurements, as well as real-time position reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".