Search for Galactic PeV gamma rays with the IceCube Neutrino Observatory
Bibliographic record
Abstract
Gamma-ray induced air showers are notable for their lack of muons, compared to hadronic showers. Hence, air shower arrays with large underground muon detectors can select a sample greatly enriched in photon showers by rejecting showers containing muons. IceCube is sensitive to muons with energies above $\ensuremath{\sim}500\text{ }\text{ }\mathrm{GeV}$ at the surface, which provides an efficient veto system for hadronic air showers with energies above 1 PeV. One year of data from the 40-string IceCube configuration was used to perform a search for point sources and a Galactic diffuse signal. No sources were found, resulting in a 90% C.L. upper limit on the ratio of gamma rays to cosmic rays of $1.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$ for the flux coming from the Galactic plane region ($\ensuremath{-}80\ifmmode^\circ\else\textdegree\fi{}\ensuremath{\lesssim}l\ensuremath{\lesssim}\ensuremath{-}30\ifmmode^\circ\else\textdegree\fi{}$; $\ensuremath{-}10\ifmmode^\circ\else\textdegree\fi{}\ensuremath{\lesssim}b\ensuremath{\lesssim}5\ifmmode^\circ\else\textdegree\fi{}$) in the energy range 1.2--6.0 PeV. In the same energy range, point source fluxes with ${E}^{\ensuremath{-}2}$ spectra have been excluded at a level of $(E/\mathrm{TeV}{)}^{2}\mathrm{d}\ensuremath{\Phi}/\mathrm{d}E\ensuremath{\sim}{10}^{\ensuremath{-}12}--{10}^{\ensuremath{-}11}\text{ }\text{ }{\mathrm{cm}}^{\ensuremath{-}2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{TeV}}^{\ensuremath{-}1}$ depending on source declination. The complete IceCube detector will have a better sensitivity (due to the larger detector size), improved reconstruction, and vetoing techniques. Preliminary data from the nearly final IceCube detector configuration have been used to estimate the 5-yr sensitivity of the full detector. It is found to be more than an order of magnitude better, allowing the search for PeV extensions of known TeV gamma-ray emitters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".