Implications of MicroBooNE’s low sensitivity to electron antineutrino interactions in the search for the MiniBooNE excess
Bibliographic record
Abstract
The MicroBooNE experiment searched for an excess of electron-neutrinos in the Booster Neutrino Beam (BNB), providing direct constraints on ${\ensuremath{\nu}}_{e}$-interpretations of the MiniBooNE low-energy excess (LEE). In this article, we show that if the MiniBooNE LEE is caused instead by an excess of ${\overline{\ensuremath{\nu}}}_{e}$, then liquid argon detectors, such as MicroBooNE, SBND, and ICARUS, would have poor sensitivity to it. This is due to a strong suppression of ${\overline{\ensuremath{\nu}}}_{e}--^{40}\mathrm{Ar}$ cross sections in the low-energy region of the excess. The MicroBooNE results are consistent at the $2\ensuremath{\sigma}$ CL with a scenario in which the MiniBooNE excess is sourced entirely by ${\overline{\ensuremath{\nu}}}_{e}$ interactions. The opportune location of ANNIE, a Gd-loaded water Cherenkov detector, allows for a direct search for a ${\overline{\ensuremath{\nu}}}_{e}$ flux excess in the BNB using inverse beta-decay events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".