Towards the Final Spectrum Analysis of PROSPECT-I
Bibliographic record
Abstract
The Precision Reactor Oscillation and SPECTrum (PROSPECT) experiment measures the spectrum of antineutrinos from the High Flux Isotope Reactor and searches for potential short-baseline oscillations. The analyses performed to produce the last published results from the current dataset (PROSPECT-I) excluded initially unusable data obtained from some detector segments containing non-operating PMTs. Recent efforts from the collaboration have resulted in a more sophisticated analysis that includes the Single Ended Event Reconstruction (SEER) of the previously unused segments, and the careful data splitting (DS) of the different time periods to maximize the available statistics.In this poster we will focus on the spectrum analysis of PROSPECT-I using the updated dataset. In particular, the application of the Wiener-SVD unfolding technique to produce the final PROSPECT-I antineutrino energy spectrum will be reviewed emphasizing its effect over the multi-period data splitting. We will also detail a pathway to compare the updated positron energy spectrum with PROSPECT's previous results and how can be used to test the gaussian-bump extended Huber-Mueller model. This work was performed under the auspices of the US DOE Office of High Energy Physics by LLNL under Contract DE-AC52-07NA27344, the Heising-Simons Foundation, CFREF and NSERC of Canada, and internal investments at all institutions. LLNL-ABS-833063.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".