Optimizing the hunt for extraterrestrial high-energy neutrino counterparts
Bibliographic record
Abstract
It has been a decade since the IceCube collaboration began detecting high-energy (HE) neutrinos originating from cosmic sources. Despite a few well-known individual associations and numerous phenomenological, observational, and statistical multiwavelength studies, the origin of astrophysical HE neutrinos largely remains a mystery. To date, the most convincing associations link HE neutrinos with active galactic nuclei (AGNs). Consequently, many studies have attempted population-based correlation tests between HE neutrinos and specific AGN subpopulations (such as blazars). While some of the associations are suggestive, no definitive population-based correlation has been established. This could result from either a lack of a population-based correlation or insufficient detection power, given the substantial atmospheric neutrino background. By leveraging blazar variability, we performed spatio-temporal blazar-neutrino correlation tests aimed at enhancing detection power by reliably incorporating temporal information into the statistical analysis. We used simulations to evaluate the detection power of our method under various test strategies. We find that: (1) with sufficiently large source samples, if 20% of astrophysical HE neutrinos originate from blazars, we should robustly observe ∼4σ associations; (2) a counting-based test statistic combined with a top-hat weighting scheme (rather than a Gaussian one) provides the greatest detection power; (3) applying neutrino sample cuts reduces detection power when a weighting scheme is used; and (4) in top-hat-like weighting schemes, low p-values do not occur arbitrarily with an increase in the HE neutrino error region size (any such occurrence is indicative of an underlying blazar–neutrino correlation).
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".