The Future of X-Ray Time-Domain Surveys
Bibliographic record
Abstract
Abstract Modern X-ray observatories yield unique insight into the astrophysical time domain. Each X-ray photon can be assigned an arrival time, an energy and a sky position, yielding sensitive, energy-dependent light curves and enabling time-resolved spectra down to millisecond time-scales. Combining those with multiple views of the same patch of sky (e.g., in the Chandra and XMM-Newton deep fields) so as to extend variability studies over longer baselines, the spectral timing capacity of X-ray observatories then stretch over 10 orders of magnitude at spatial resolutions of arcseconds, and 13 orders of magnitude at spatial resolutions of a degree. A wealth of high-energy time-domain data already exists, and indicates variability on timescales ranging from microseconds to years in a wide variety of objects, including numerous classes of AGN, high-energy phenomena at the Galactic centre, Galactic and extra-Galactic X-ray binaries, supernovæ, gamma-ray bursts, stellar flares, tidal disruption flares, and as-yet unknown X-ray variables. This workshop explored the potential of strategic X-ray surveys to probe a broad range of astrophysical sources and phenomena. Here we present the highlights, with an emphasis on the science topics and mission designs that will drive future discovery in the X-ray time domain.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".