Reply to “Comment on ‘Understanding the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>γ</mml:mi></mml:math>-ray emission from the globular cluster 47 Tuc: Evidence for dark matter?”’
Bibliographic record
Abstract
Analyzing nine years of Fermi-LAT observations, we recently studied the spectral properties of the prominent globular cluster [Phys. Rev. 98, 041301 (2018)]. In particular, we investigated several models to explain the observed gamma-ray emission, ranging from millisecond pulsars (MSPs) to dark matter (DM), with the motivation for the latter model driven by recent evidence that 47 Tuc harbors an intermediate-mass black hole [B. Kiziltan et al., Nature (London) 542, 203 (2017)]. This investigation found evidence that the observed gamma-ray emission from 47 Tuc is due to two source populations of MSPs and DM. In preceding Comment [Phys. Rev. D 100, 068301 (2019)], Bartels and Edwards comment that this evidence is an artifact of the MSP spectra used in our paper. Here, we reply to this comment and argue that (i) Bartels and Edwards do not give due consideration to a very important implication of their result and (ii) there is tension between our MSP fit and their MSP fit when taking uncertainties into consideration. As such, we still conclude there is evidence for a DM component which motivates a deeper radio study of the prominent globular cluster 47 Tuc.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.053 | 0.055 |
| Insufficient payload (model declined to judge) | 0.016 | 0.020 |
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".