All-flavour search for neutrinos from dark matter annihilations in the Milky Way with IceCube/DeepCore
Bibliographic record
Abstract
We present the first IceCube search for a signal of dark matter annihilations in the Milky Way using all-flavour neutrino-induced particle cascades. The analysis focuses on the DeepCore sub-detector of IceCube, and uses the surrounding IceCube strings as a veto region in order to select starting events in the DeepCore volume. We use 329 live-days of data from IceCube operating in its 86-string configuration during 2011–2012. No neutrino excess is found, the final result being compatible with the background-only hypothesis. From this null result, we derive upper limits on the velocity-averaged self-annihilation cross-section, $$\langle \sigma _A \mathrm{v} \rangle $$ , for dark matter candidate masses ranging from 30 GeV up to 10 TeV, assuming both a cuspy and a flat-cored dark matter halo profile. For dark matter masses between 200 GeV and 10 TeV, the results improve on all previous IceCube results on $$\langle \sigma _A \mathrm{v} \rangle $$ , reaching a level of 10 $$^{-23}$$ cm $$^3$$ s $$^{-1}$$ , depending on the annihilation channel assumed, for a cusped NFW profile. The analysis demonstrates that all-flavour searches are competitive with muon channel searches despite the intrinsically worse angular resolution of cascades compared to muon tracks in IceCube.
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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 | 0.001 | 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".