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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".