TIME SERIES ANALYSIS FOR THE CF SOURCE IN SUDBURY NEUTRINO OBSERVATORY
Bibliographic record
Abstract
The Sudbury Neutrino Observatory uses a 252Cf source to measure the neutron detection efficiency of its detector. We propose a method, the Time Series Analysis, that uses pairs of time intervals between the detected events to find the neutron detection efficiency, the probability to detect from a fission the prompt γ-rays, the neutron mean life inside the detector, the source fission rate and the residual activity rate from non-fission events. We explain our theoretical model of the source and the procedure to fit the data. With a 2.5 MeV threshold cut on the data, the neutron mean life is 5.281±0.004(stat) msec and the source fission rate at June 12, 2001 is 4.360±0.004(stat) sec−1. At this moment, the Time Series can only be applied to data when the source is near the centre. The technique is also applied with an AmBe source. We also show the latest progress to improve the technique. Finally, we briefly show another method, the Multiplicity Analysis, which was used before we changed to the Time Series. Acknowledgments I would like first to thank Prof. Bernie G. Nickel. He spent his time on this project with so much generosity, particularly to have conceived the whole Time Series theoretical
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".