Fast X-ray Transient Detection with AXIS: Application to Magnetar Giant Flares
Bibliographic record
Abstract
Magnetar giant flares (MGFs) are among the most luminous high-energy transients in the local universe, consisting of a short, intense MeV gamma-ray spike followed by a softer, pulsating X-ray tail and possibly delayed radioactive emission. While only three Galactic events have been firmly detected, several extragalactic candidates have recently been reported, motivating the need for sensitive, rapid-response γ - and X-ray facilities to constrain their rates and energetics. We present a feasibility study of detecting MGFs with the Advanced X-ray Imaging Satellite (AXIS), focusing on two complementary pathways: (i) serendipitous discovery of the prompt gamma-ray spike within the field of view, and (ii) rapid follow-up of MGF tails in nearby galaxies. Using sensitivity rescaling and volumetric rate estimates, we find that serendipitous detection of prompt spikes during the mission lifetime is possible but unlikely, primarily because of their short duration and primarily because of their short duration and hard spectrum, in the assumption that the hard gamma-ray spectrum can be reliably extrapolated to the instrument’s energy range. In contrast, AXIS’s superior sensitivity, if accompanied by fast repointing capabilities, offer an extraordinary opportunity to detect pulsating X-ray tails out to ~20 Mpc, enabling the first extragalactic measurements of periodic modulations from a magnetar and potentially constraining emission geometry and fireball physics. Finally, we evaluate the detectability of soft X-ray line emission from r-process nucleosynthesis in MGFs, finding that such signals are extremely faint and confining the detection to Galactic distances. Our study offer a general framework for assessing the detectability of short transients with future missions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".