AMON: TeV Gamma and Neutrino Coincidence Alerts from HAWC and IceCube subthreshold data
Bibliographic record
Abstract
The era of multimessenger astrophysics has arrived with the simultaneous operation of large cosmic-ray, gamma-ray, neutrino, and gravitational-wave observatories. In just the past two years, an electromagnetic (EM) counterpart was detected for a gravitational wave event, and evidence for an EM counterpart of high energy neutrinos has been identified. These measurements have had a major impact on our view of the non-thermal universe, but understanding cosmic accelerators require a substantial increase in the number of multimessenger observations. The Astrophysical Multimessenger Observatory Network (AMON) is designed for high-statistics searches of sub-threshold transient alerts from gamma-ray and neutrino detectors. Within AMON, we have implemented a joint-likelihood analysis of TeV gamma-ray measurements from the High Altitude Water Cherenkov (HAWC) Observatory and neutrinos from the IceCube Neutrino Observatory. AMON is ready to produce real-time coincidence alerts using HAWC ``hotspots'' and IceCube astrophysical neutrino events. These alerts will be distributed to AMON follow-up partners with a median anticipated delay of six hours, which corresponds to a full transit in the field of view of HAWC. The alerts will have an angular resolution of ${\sim} 0.2^{\circ}$, making them well- suited for deep electromagnetic follow-up observations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".