Unveiling the secrets of black holes and neutron stars with high-throughput, high-energy resolution X-ray spectroscopy
Bibliographic record
Abstract
Neutron stars and black holes (compact objects) are among the most fascinating and puzzling objects in the Universe. They uniquely provide an environment to test the laws of physics at their extremes, as density in a neutron star reaches values several times higher than nuclear density, magnetic fields are billions of times higher than the Sun’s, and gravity around black holes is so strong as to trap light itself. Compact objects, however, do not like to reveal their secrets all at once. Fifty years after their discovery, we still do not know what neutron stars are made of, and the question of how black holes modify space and time around them is still open. The X-ray emission of compact objects presents a rich phenomenology that can lead us to a better understanding of their nature and to address more general physics questions, like: Does general relativity (GR) apply in the strong gravity regime? Is spacetime around black holes described by the Kerr metric? What are the masses, radii and composition of neutron stars and their atmospheres? Recent advancements in transition-edge sensors (TES) present a unique opportunity to open a new window on compact objects: high energy-resolution spectroscopy combined with high-precision timing and high sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".