Closing the light gluino gap with electron-proton colliders
Bibliographic record
Abstract
The future electron-proton collider proposals, LHeC and FCC-he, can deliver $\mathcal{O}(\mathrm{TeV})$ center-of-mass energy collisions, higher than most of the proposed lepton accelerators, with $\mathcal{O}({\mathrm{ab}}^{\ensuremath{-}1})$ luminosity, while maintaining a much cleaner experimental environment as compared to the hadron machines. This unique capability of ${e}^{\ensuremath{-}}p$ colliders can be harnessed in probing beyond the Standard Model scenarios giving final states that look like hadronic noise at $pp$ machines. In the present study, we explore the prospects of detecting such a prompt signal having multiple soft jets at the LHeC. Such a signal can come from the decay of gluino in $R$-parity-violating or stealth supersymmetry, where there exists a gap in the current experimental search with ${m}_{\stackrel{\texttildelow{}}{g}}\ensuremath{\approx}50--70\text{ }\text{ }\mathrm{GeV}$. We perform a simple analysis to demonstrate that, with simple signal selection cuts, we can close this gap at the LHeC at the 95% confidence level, even in the presence of a reasonable systematic error. More sophisticated signal selection strategies and detailed knowledge of the detector can be used to improve the prospects of signal detection.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".