Machine Learning for Accurate Energy Analysis of Double Beta Decay Events
Bibliographic record
Abstract
Point-contact germanium detectors are used to detect and analyze particles and their decay processes. This research focuses on the search for a phenomenon known as neutrinoless double beta decay, where two neutrons within a nucleus transform into two protons, emitting electrons in the process. Unlike standard double beta decay, no neutrinos are emitted, suggesting that the neutrino may act as its own antiparticle, effectively canceling itself out. Detecting this process would provide crucial insights into the nature of neutrino mass. During such an event, the detector registers a spike in energy due to the emitted electrons, producing a step-like pulse in the output channels. The energy of the event is determined by the difference in charge between the ‘top’ and ‘bottom’ steps of the pulse. However, accurately determining the energy is complicated by the presence of a resistor in the detector, which causes the top step to decay exponentially back to a baseline in preparation for the next event. This decay makes it challenging to determine the true energy of the pulse. My work this summer focused on developing machine learning models to reconstruct the step-like nature of pulses from their decayed versions recorded by the detector. This involved constructing deep neural networks trained on inputs (decayed pulses) and corresponding targets (the original step pulses) to learn to accurately reconstruct the target from the input. Initially, the networks were trained on simulated detector data. Once proficient in reconstruction, electronic noise was added to the input pulses to simulate real-world conditions, under which the models were trained to remove both electronic noise and decay. The models were trained to handle pulses with varying decay constants to improve their generalization ability. These neural networks can now be applied to real detector data for accurate energy analysis of double beta decay events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".