Rapid far-infrared spectral timing of X-ray binaries with PRIMA
Bibliographic record
Abstract
The most powerful cosmic engines in our universe are fueled by compact objects. These objects accrete large amounts of material and eject matter in the form of jets. Recent groundbreaking discoveries of gravitational waves from merging compact objects and the direct imaging of the black hole shadows with the Event Horizon Telescope represent major steps forward in our understanding of such systems. However, there exists a large population of stellar-mass compact objects in our own Galaxy, present in X-ray binaries (XRBs), which provide better laboratories with which to study the processes of accretion and ejection. XRBs produce highly variable emissions on timescales ranging from milliseconds (for light-travel time in the region close to the compact object) to weeks (governing the mass-inflow process). Therefore, high-time resolution observations can be a powerful tool to study these systems. However, as XRBs emit across the electromagnetic spectrum, a suite of facilities is needed to take full advantage of these techniques. The PRIMA Observatory (PRobe far-Infrared Mission for Astrophysics) will provide unique access to a wavelength range that has not been sampled in XRBs, representing an exciting new possibility for characterizing rapid time-domain phenomena of XRBs (and potentially other transient sources) in the far-infrared regime.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".