Simulation-based Inference of Radio Millisecond Pulsars in Globular Clusters
Bibliographic record
Abstract
Abstract Millisecond pulsars (MSPs) are abundant in globular clusters (GCs), which offer favorable environments for their creation. While the advent of recent, powerful facilities led to a rapid increase in MSP discoveries in GCs through pulsation searches, detection biases persist. In this work, we investigate the ability of current and future detections in GCs to constrain the parameters of the MSP population in GCs through a careful study of their luminosity function. Parameters of interest are the number of MSPs hosted by a GC, as well as the mean and the width of their luminosity function, which are typically affected by large uncertainties. While, as we show, likelihood-based studies can lead to ill-behaved posteriors on the size of the MSP population, we introduce a novel, likelihood-free analysis, based on marginal neural ratio estimation, which consistently produces well-behaved posteriors. We focus on the GC Terzan 5 (or Ter 5), which currently counts 48 detected MSPs. We find that 158 − 104 + 294 MSPs should be hosted in this GC, but the uncertainty on this number remains large. We explore the performance of our new method on simulated Terzan 5-like data sets mimicking possible future observational outcomes. We find that significant improvement on the posteriors can be obtained by adding a reliable measurement of the diffuse radio emission of the GC to the analysis or by improving the detection threshold of current radio pulsation surveys by at least a factor of 2.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".