Bibliographic record
Abstract
DEAP-3600 (Dark matter Experiment using Argon Pulseshape discrimination) is a world-class experiment in the direct detection of dark matter. Dark matter is thought to compose about 27% of the universe, but remains unknown and undetected. The DEAP-3600 Experiment observes light pulses from particle collisions and is located 2 km underground at SNOLAB, the world’s deepest cleanest lab. SNOLAB provides a low-background environment to search for extremely rare physical interactions. In addition to the search for dark matter, DEAP-3600 is attempting the first observation of charged-current solar neutrino interactions in a dark matter detector. Solar neutrinos are elementary particles produced in the fusion reactions which power the Sun. While they are abundant (60 billion neutrinos pass through your thumbnail every second), neutrinos are also weakly interacting. Thus, the detection of charged-current neutrino interactions is an event search, where a handful of events are counted in years of data. To identify these neutrino interactions, the background events must be well understood. Various data cuts are applied to select well-behaved physics events produced by neutrino interactions. If a background event is counted as a neutrino event, the results of the cut-and-count analysis could be affected significantly. I have analyzed the effect of the chosen cut values on the final count of events and explored a specific background population using data cuts. This work has been a critical step in the data analysis for the search of charged-current solar neutrino interactions in the DEAP-3600 detector. The precise detection of this signal could broaden the capabilities of future multi-tonne scale experiments, such as measuring the neutrino spectrum of supernovae and in turn monitoring for supernovae events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".