A classification of UV models for higher-dimensional SMEFT operators for neutrinoless double beta decay
Bibliographic record
Abstract
The observation of lepton number violation (LNV) would be clear evidence for physics beyond the Standard Model. Non-zero neutrino masses motivate a study of processes that violate lepton number by two units, like e.g.\ neutrinoless double beta decay. In the Standard Model Effective Field Theory (SMEFT), a ubiquitous framework used for indirect new physics searches, $\Delta L =2$ operators appear at dimension-5 and higher odd dimensions. The dimension-5 Weinberg operator, that can explain neutrino masses, does not have to be realised at tree level but could arise at higher loop order in the UV models. These models, however, could produce higher dimensional operators at tree level, leaving the question which contribution dominates the neutrinoless double beta decay. We use a diagrammatic approach to systematically list all possible tree-level models for dimension-9 operators and perform a scan over which lower dimensional operators they produce. Then, we present the matching for one specific model, featuring a scalar leptoquark and a fermionic colour octet, that produces the Weinberg operator at 2-loop. We find that for all BSM couplings of order one the dim-9 operator could only dominate over the loop-suppressed operators at lower dimensions in a small mass range. We conclude that a thorough analysis including a fit to the neutrino sector will be crucial to address the question which operator class dominates in a specific model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".