Hunting wino and higgsino dark matter at the muon collider with disappearing tracks
Bibliographic record
Abstract
A bstract We study the capabilities of a muon collider experiment to detect disappearing tracks originating when a heavy and electrically charged long-lived particle decays via X + → Y + Z 0 , where X + and Z 0 are two almost mass degenerate new states and Y + is a charged Standard Model particle. The backgrounds induced by the in-flight decays of the muon beams (BIB) can create detector hit combinations that mimic long-lived particle signatures, making the search a daunting task. We design a simple strategy to tame the BIB, based on a detector-hit-level selection exploiting timing information and hit-to-hit correlations, followed by simple requirements on the quality of reconstructed tracks. Our strategy allows us to reduce the number of tracks from BIB to an average of 0.08 per event, hence being able to design a cut-and-count analysis that shows that it is possible to cover weak doublets and triplets with masses close to $$ \sqrt{s}/2 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:math> in the 0.1–10 ns range. In particular, this implies that a 10 TeV muon collider is able to probe thermal MSSM higgsinos and thermal MSSM winos, thus rivaling the FCC-hh in that respect, and further enlarging the physics program of the muon collider into the territory of WIMP dark matter and long-lived signatures. We also provide parton-to-reconstructed level efficiency maps, allowing an estimation of the coverage of disappearing tracks at muon colliders for arbitrary models.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".