Particle identification using semi-supervised learning in the PICO-60 dark matter detector
Bibliographic record
Abstract
Abstract Astrophysical observations have demonstrated the existence of dark matter over the past decades. Experimental efforts in the search for dark matter are largely focused on the well-motivated weakly interacting massive particles (WIMPs) as a dark matter candidate. Current experiments in direct detection are producing increasingly competitive limits on the cross section of WIMP-nucleon scattering. The main experimental challenge for all direct detection experiments is the presence of background signals. These backgrounds need to be either eliminated by providing sufficient shielding or discriminated from WIMP signals. In this work, semi-supervised learning techniques are developed to discriminate alpha recoils from nuclear recoils induced by WIMPs in the PICO-60 detector. The two semi-supervised learning techniques, gravitational differentiation and iterative cluster nucleation, maximize the effect of the most confidently predicted data samples on subsequent training iterations. Classifications using both techniques can reproduce the traditional acoustic parameter with accuracies over 98%. The best model yields an accuracy of 99.2% and a class-wise standard deviation value of 0.11. These techniques can reliably serve as an intermediate verification tool before the acoustic parameter is constructed in future detectors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".