Use of event-level neutrino telescope data in global fits for theories of new physics
Bibliographic record
Abstract
We present a fast likelihood method for including event-level neutrino telescope data in parameter explorations of theories for new physics, and announce its public release as part of DarkSUSY 5.0.6. Our construction includes both angular and spectral information about neutrino events, as well as their total number. We also present a corresponding measure for simple model exclusion, which can be used for single models without reference to the rest of a parameter space. We perform a number of supersymmetric parameter scans with IceCube data to illustrate the utility of the method: example global fits and a signal recovery in the constrained minimal supersymmetric standard model (CMSSM), and a model exclusion exercise in a 7-parameter phenomenological version of the MSSM. The final IceCube detector configuration will probe almost the entire focus-point region of the CMSSM, as well as a number of MSSM-7 models that will not otherwise be accessible to e.g. direct detection. Our method accurately recovers the mock signal, and provides tight constraints on model parameters and derived quantities. We show that the inclusion of spectral information significantly improves the accuracy of the recovery, providing motivation for its use in future IceCube analyses.
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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 | 0.001 |
| 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".