Search for a Dark Higgs Boson Produced in Association with Inelastic Dark Matter at the Belle II Experiment
Bibliographic record
Abstract
Inelastic dark matter models that have two dark matter particles and a massive dark photon can reproduce the observed relic dark matter density without violating cosmological limits. The mass splitting between the two dark matter particles χ_{1} and χ_{2}, with m(χ_{2})>m(χ_{1}), is induced by a dark Higgs field and a corresponding dark Higgs boson h^{'}. We present a search for dark matter in events with two vertices, at least one of which must be displaced from the interaction region, and missing energy. Using a 365 fb^{-1} data sample collected at Belle II, which operates at the SuperKEKB e^{+}e^{-} collider, we observe no evidence for a signal. We set upper limits on the product of the production cross section σ(e^{+}e^{-}→h^{'}χ_{1}χ_{2}), and the product of branching fractions B(χ_{2}→χ_{1}e^{+}e^{-})×B(h^{'}→x^{+}x^{-}), where x^{+}x^{-} indicates μ^{+}μ^{-},π^{+}π^{-}, or K^{+}K^{-}, as functions of h^{'} mass and lifetime at the level of 10^{-1} fb. We set model-dependent upper limits on the dark Higgs mixing angle at the level of 10^{-5} and on the dark photon kinetic mixing parameter at the level of 10^{-3}. This is the first search for dark Higgs bosons in association with inelastic dark matter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".