Current Status of TAMBO: Realizing PeV Neutrino Astronomy with a Cost-Effective Observatory
Bibliographic record
Abstract
The detection of high-energy astrophysical neutrinos by IceCube has opened a new window on our Universe. While IceCube has measured the flux of these neutrinos at energies up to several PeV, much remains to be discovered regarding their origin and nature. Currently, the discovery of point sources of neutrinos is hindered by atmospheric neutrino backgrounds; likewise, astrophysical neutrino flavor ratio measurements are limited by the difficulty of discriminating between electron and tau neutrinos. TAMBO is a next-generation neutrino telescope specifically designed to detect tau neutrinos in the 100 TeV to 1 EeV energy range at a fraction of the cost of traditional neutrino telescopes. The tau neutrino specificity enables the low-background identification of astrophysical neutrino sources, as well as tests of the flavor ratio of astrophysical neutrinos. Additionally, the high-energy reach of TAMBO will allow us to probe models of cosmogenic neutrino production. TAMBO will comprise an array of water Cherenkov and plastic scintillator detectors deployed on the face of a deep valley, with its unique geometry facilitating the high-purity measurement of astrophysical tau neutrinos. In this talk, I will present the particle physics and astrophysics that TAMBO will study in the context of next-generation neutrino observatories. I will also provide an update on the status of detector construction.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".