Vector boson fusion signatures of superheavy Majorana neutrinos at muon colliders
Bibliographic record
Abstract
We investigate the sensitivity of future high-energy muon colliders to heavy Majorana neutrinos, considering both opposite-sign ( μ + μ − ) and same-sign ( μ + μ + ) collision modes. We focus on μ + μ − colliders operating at center-of-mass energies of 1, 3, and 10 TeV, as well as the proposed μ TRISTAN facility at 2 TeV, and we analyze the production of heavy neutrinos via vector boson fusion in the t -channel, a mechanism that becomes dominant in the multi-TeV regime. We evaluate its exclusion potential in terms of the heavy neutrino mass and the mixing of the heavy neutrino with its Standard Model counterparts, using both cut-based selections and boosted decision trees trained to exploit the distinct kinematic signatures of heavy Majorana neutrino exchanges. Our results demonstrate the complementarity between collider configurations and show that active-sterile mixing angles as small as 0.001 could be probed for neutrino masses up to 100 TeV, an experimentally inaccessible region of the parameter space at current facilities. Altogether, this work establishes the discovery potential of muon colliders for testing super-heavy Majorana neutrinos, complementary to conventional probes, and provides compelling motivation for the next generation of high-energy lepton colliders.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".