Prospects for GeV Neutrino Transient Searches with the IceCube Upgrade
Bibliographic record
Abstract
The recent detection of TeV neutrino sources by the IceCube Neutrino Observatory demonstrates the detector's advanced capabilities in detecting high-energy astrophysical neutrinos. At lower energies, down to the GeV range, a variety of transient phenomena, such as novae, supernovae, and gamma-ray bursts, are expected to emit neutrinos. Observations of these neutrinos can provide unique insights into processes below the photosphere and offer clues to identifying their emission mechanisms. We have searched for these neutrinos intensively with IceCube’s existing infill array, DeepCore. Although no significant detections have been made, strong constraints on astrophysical environments in these transients, such as the baryon loading factor in gamma-ray bursts, have been obtained. A denser infill array, called the IceCube Upgrade, will enhance sub-TeV neutrino searches with its unprecedented sensitivity to GeV neutrinos. The Upgrade, set to be deployed in the 2025-2026 South Pole season, will consist of seven new strings, adding approximately 700 novel optical modules with multiple photomultiplier tubes in the DeepCore volume. The denser arrangement of high-efficiency modules will significantly improve IceCube’s sensitivity between 1 GeV and 1 TeV. We present an initial assessment of the astrophysical capabilities of the IceCube Upgrade, using preliminary simulated data and an event selection similar to that used for DeepCore. We explore the detectability of GeV neutrino transients compared to DeepCore and discuss potential sensitivity enhancements through advanced detector simulations and optimized analysis techniques, including refined triggering conditions and event selection criteria.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".