The Large Magellanic Cloud: expanding the low-mass parameter space of dark matter direct detection
Bibliographic record
Abstract
Abstract We investigate how the Large Magellanic Cloud (LMC) impacts the predicted signals in near-future direct detection experiments for non-standard dark matter (DM) interactions, using the Auriga cosmological simulations. We extract the local DM distribution of a simulated Milky Way-like halo that has an LMC analogue and study the expected signals in DarkSide-20k, SBC, DARWIN/XLZD, SuperCDMS, NEWS-G, and DarkSPHERE considering DM-nucleon effective interactions, as well as inelastic DM scattering. We find that the LMC causes substantial shifts in direct detection exclusion limits towards smaller cross sections and DM masses for all non-relativistic effective field theory (NREFT) operators, with the impact being highly pronounced for velocity-dependent operators at low DM masses. For inelastic DM, where the DM particle up-scatters to a heavier state, the LMC shifts the direct detection exclusion limits towards larger DM mass splitting and smaller cross sections. Thus, we show that the LMC significantly expands the parameter space that can be probed by direct detection experiments towards smaller DM-nucleon cross sections for all NREFT operators and larger values of mass splitting for inelastic DM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".