Shock breakouts from tidal disruption events
Bibliographic record
Abstract
Tidal disruption events of stars by supermassive black holes have so far been discovered months to years after the fact. In this paper, we explore the short, faint, and hard burst of radiation that is emitted at maximum compression as a result of shock breakout. The detection of this burst can be used to capture tidal disruption events in real time. We verify that shock breakout from main-sequence stars produces radiation in the X-ray range, but find that it is difficult to detect using all sky X-ray surveying telescopes. In the case of shock breakout from red giants, most of the radiation is emitted in the UV and visible range, which is significantly easier to detect. A similar burst of UV/optical radiation will also be emitted by stars puffed by tidal heating from a previous passage close to the central black hole. This radiation can be detected by surveys like ZTF and LSST. We calculate detection rates for different types of galactic nuclei. For the case of a very full or very empty loss cones, we predict a detection rate of once per year with LSST. Evidence from a recent tidal disruption event, ASASSN-14li, seems to favour a very full loss cone, in which case LSST is expected to detect one such event every year.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".