Bibliographic record
Abstract
In recent years the search for dark matter has intensified with competitive bounds coming from collider searches, direct detection, and indirect detection. Collider searches at the Large Hadron Collider (LHC) lack the necessary center-of-mass energy to probe TeV-scale dark matter. It is TeV-scale dark matter, however, that remains viable for many models of supersymmetry. In this paper, we study the reach of a 100 TeV proton-proton collider for neutralino dark matter and compare to 14 TeV LHC projections. We employ a supersymmetric simplified model approach and present reach estimates from monojet searches, soft lepton searches, and disappearing track searches. The searches are applied to pure neutralino spectra, compressed neutralino spectra, and coannihilating spectra. We find a factor of 4-5 improvement in mass reach in going from 14 TeV to 100 TeV. More specifically, we find that given a 1% systematic uncertainty, a 100 TeV collider could exclude winos up to 1.4 TeV and higgsinos up to 850 GeV in the monojet channel. Coannihilation scenarios with gluinos can be excluded with neutralino masses of 6.2 TeV, with stops at 2.8 TeV, and with squarks at 4.0 TeV. Using a soft lepton search, compressed spectra with a chargino-neutralino splitting of Δm = 20 − 30 GeV can exclude neutralinos at ~1 TeV. Given a sufficiently long chargino lifetime, the disappearing track search is very effective and we extrapolate current experimental bounds to estimate that a ~2TeVwinocouldbediscoveredanda ~3TeVwinocouldbeexcluded.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".