Directional neutrino searches for Galactic Center dark matter at large underground LArTPCs
Bibliographic record
Abstract
We investigate the sensitivity of a large underground Liquid Argon Time Projection Chamber (LArTPC)-based neutrino detector to dark matter in the Galactic Center annihilating into neutrinos. Such a detector could have the ability to resolve the direction of the electron in a neutrino scattering event and thus to infer information about the source direction for individual neutrino events. We consider the improvements on the expected experimental sensitivity that this directional information would provide. Even without directional information, we find a DUNE-like LArTPC detector is capable of setting limits on dark matter annihilation to neutrinos for dark matter masses above 30 MeV that are competitive with or exceed current experimental reach. While currently-demonstrated angular resolution for low-energy electrons is insufficient to allow any significant increase in sensitivity, these techniques could benefit from improvements to algorithms and the additional spatial information provided by novel 3D charge imaging approaches. We consider the impact of such enhancements to the resolution for electron directionality and find that, where electron-scattering events can be distinguished from charged-current neutrino interactions, limits on dark matter annihilation in the mass range where solar neutrino backgrounds dominate ($\ensuremath{\lesssim}15\text{ }\text{ }\mathrm{MeV}$) can be significantly improved using directional information, and would be competitive with existing limits using $40\text{ }\text{ }\mathrm{kton}\ifmmode\times\else\texttimes\fi{}\mathrm{year}$ of exposure.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".