Neutron stars and the determination of the dense matter equation of state
Bibliographic record
Abstract
A physical understanding of the behaviour of cold ultra-dense matter -at and above nuclear density -can only be achieved by the study of neutron stars.The surface thermal emission from neutron stars in quiescent low-mass X-ray binaries (qLMXBs) inside globular clusters has proven useful for that purpose.These systems rely on the relatively precisely measured distances to globular clusters to produce measurements of the radiation radius of neutron stars R ∞ , the radius as seen from infinite distance.Such measurements can be compared to the Mass-Radius relation resulting from proposed dense matter equations of state.Individually, the R ∞ measurements from qLMXBs do not produce stringent constraints on the dense matter equation of state.However, when several of these qLMXBs spectra are combined in a coherent manner, they can lead to useful constraints on the dense matter equation of state.This work first presents the discovery of a new spectrally identified qLMXB inside a globular cluster, and the spectral analysis of high signal-to-noise ratio data from another qLMXB, leading to a precise R ∞ measurement.Then, spectra from multiple qLMXBs hosted in globular clusters are combined in a simultaneous analysis using a Markov-Chain Monte-Carlo approach, placing new constraints on the dense matter equation of state.This method and the Bayesian approach developed in this analysis permits including all quantifiable sources of uncertainty (e.g., in the distance, the hydrogen column density) to produce the most conservative constraints on the dense matter equation of state.Finally, this Bayesian approach is modified and adapted to quantitatively reject or confirm a selection of proposed equations of state.xi First, I would like to thank my supervisor, Bob Rutledge.I am truly grateful for his availability, his patience, and his many pieces of advice about research, career choices, or other aspects.His resolute and rigorous approach toward research surely guided me on the way to become a better scientist over the past seven years.During these years at McGill, I have had the chance to interact on countless occasions with faculty members of the physics department.I am thinking in particular about Ken Ragan, with whom I shared many discussions.I would also thank Vicky Kaspi for her support of the astronomy outreach activities at the Physics department.I have a particular thought for friends and colleagues of AstroMcGill.For more than two years now, we have shared our passion of astronomy with kids of all ages and adults alike.It was not always easy, but it was surely rewarding.Friends and colleagues, at McGill or those I met during conferences, also ought to be acknowledged, for the many useful and less useful discussions about neutron stars, research, science in general, and many other topics.This statement is general enough that I am not forgetting anyone.Last, but definitely not the least, I would like to acknowledge the Gouvernment of Canada / Gouvernement du Canada, which provided support during my PhD via the Vanier Canada Graduate Scholarship program.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".