Development of a single vacuum ultra-violet photon-sensing solution for nEXO
Bibliographic record
Abstract
Silicon PhotoMultiplier (SiPM) technology represents an unprecedented attempt to create an ideal solid-state photon detector, combining the low-light detection capabilities of the previous device generations with all the benefits of a solid-state sensor. For this reason, large-scale low-background cryogenic experiments, such as the next-generation Enriched Xenon Observatory experiment (nEXO), are migrating to a SiPM-based light detection system. nEXO aims to probe the boundaries of the standard model of particle physics by searching for neutrino-less double beta decay of ¹³⁶Xe. The nEXO experiment follows the same detection concept as the EXO-200 experiment, but uses 5 tonnes of liquid xenon inside a vacuum cryostat that is expected to be located at SNOLAB, the Canadian underground science laboratory. Decays in the xenon produce both light and ionization and it is important to measure both to achieve sufficient energy resolution and thus background rejection. In particular, electrons from the ionization drift in an applied electric field toward anode pads where they are measured. The light flash is simultaneously detected by an array of SiPMs. The technical goal of the proposed thesis is to study different SiPMs characteristics in order to choose the best SiPM technology for the nEXO experiment. This thesis will also introduce new mathematical models to better understand Geiger mode properties of SiPMs in order to optimize them for the next generations of double beta decay and dark matter experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.002 |
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".