Database support of detector operation and data analysis in the DEAP-3600 Dark Matter experiment
Bibliographic record
Abstract
The DEAP-3600 detector searches for dark matter interactions on a 3.3 tonne liquid argon target. Over nearly a decade, from start of detector construction through the end of the data analysis phase, well over 200 scientists will have contributed to the project. The DEAP-3600 detector will amass in excess of 900 TB of data representing more than 10 $$^{10}$$ particle interactions, a few of which could be from dark matter. At the same time, metadata exceeding 80 GB will be generated. This metadata is crucial for organizing and interpreting the dark matter search data and contains both structured and unstructured information. The scale of the data collected, the important role of metadata in interpreting it, the number of people involved, and the long lifetime of the project necessitate an industrialized approach to metadata management. We describe how the CouchDB and the PostgreSQL database systems were integrated into the DEAP detector operation and analysis workflows. This integration provides unified, distributed access to both structured (PostgreSQL) and unstructured (CouchDB) metadata at runtime of the data analysis software. It also supports operational and reporting requirements.
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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".