Searching for Dark Matter from the Sun with the IceCube Detector
Bibliographic record
Abstract
The existence of dark matter (DM) has been well-established by repeated experiments probing various length scales. Even though DM is expected to make up 85\% of the current matter content of the Universe, its nature remains unknown. One broad class of corpuscular DM motivated by Standard Model (SM) extensions is weakly interacting massive particles (WIMPs). WIMPs can generically have a non-zero cross-section with SM nuclei, which allows them to scatter off nuclei in large celestial bodies such as the Sun, losing energy and becoming gravitationally bound in the process. After repeated scattering, WIMPs sink to the solar center, leading to an excess of WIMPs there. Subsequently, WIMPs can annihilate to stable SM particles, either directly or through a decay chain of unstable SM particles. Among stable SM particles, only neutrinos can escape the dense solar core. Thus, one may look for an excess of neutrinos from the Sun's direction as evidence of WIMPs. The IceCube Neutrino Observatory, which detects Cherenkov radiation of charged particles produced in neutrino interactions, is especially well-suited to such searches since it is sensitive to WIMPs with masses in the region preferred by supersymmetric extensions of the SM. In this contribution, I will present the results of IceCube's most recent solar WIMP search, which includes all neutrino flavors, covers the WIMP mass range from 10 GeV to 1 TeV, and has world-leading sensitivity over this entire range for most channels considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 0.002 |
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".