Millisecond pulsar kicks cause difficulties in explaining the Galactic Centre gamma-ray excess
Bibliographic record
Abstract
ABSTRACT The unexplained excess gamma-ray emission from the Milky Way’s Galactic Centre has puzzled astronomers for nearly a decade. Two theories strive to explain the origin of this excess: self-annihilating dark matter particles or an unresolved population of radio millisecond pulsars. We examine the plausibility of a pulsar origin for the GeV excess using N-body simulations. We simulated millisecond pulsars in a realistic dynamical environment: (i) pulsars were born from the known stellar mass components of our Galaxy; (ii) pulsars were given natal velocity kicks as empirically observed from two different studies (or, for comparison, without kicks); (iii) pulsars were evolved in a Galactic gravitational potential consistent with observations. Multiple populations of pulsars (with different velocity kicks) were simulated over 1 Gyr. With final spatial distributions of pulsars, we constructed synthetic gamma-ray surface brightness profiles. From comparisons with published Fermi-LAT surface brightness profiles, our pulsar simulations cannot reproduce the concentrated emission in the central degrees of the Bulge, though models without natal velocity kicks approach the data. We considered additive combinations of our (primordial MSP) simulations with models where pulsars are deposited from destroyed globular clusters in the Bulge, and a simple model for pulsars produced in the nuclear star cluster. We can reasonably reproduce the measured central gamma-ray surface brightness distribution of Horiuchi and collaborators using several combinations of these models, but we cannot reproduce the measured distribution of Di Mauro with any combination of models. Our fits provide constraints on potential pathways to explain the gamma-ray excess using MSPs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".