Bow Ties in the Sky. II. Searching for Gamma-Ray Halos in the Fermi Sky Using Anisotropy
Bibliographic record
Abstract
Abstract Many-degree-scale gamma-ray halos are expected to surround extragalactic high-energy gamma-ray sources. These arise from the inverse Compton emission of an intergalactic population of relativistic electron/positron pairs generated by the annihilation of gamma rays on the extragalactic background light. These are typically anisotropic due to the jetted structure from which they originate (in the case of radio galaxies) or are oriented perpendicular to a large-scale intergalactic magnetic field (for blazar geometries). Here, we propose a novel method for detecting these inverse Compton gamma-ray halos based on this anisotropic structure that is centered on the active galactic nucleus (AGN). By marginalizing over the radial distribution of halo photons, we demonstrate that the angular power spectrum shows a characteristic sawtooth pattern with a dominant dipolar power and elevated even multipoles. Specifically, we show that by stacking those angular power spectra instead of images, it is possible to robustly detect gamma-ray halos with existing Fermi Large Area Telescope (LAT) observations for a broad class of intergalactic magnetic fields. Importantly, by testing a large number of systematics, our suggested power spectrum statistics appears to be robust and unbiased with respect to systematic uncertainties within the LAT instrumental response and associated with contaminating astronomical sources.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".