Bibliographic record
Abstract
The aim of this paper is to demonstrate that in models with large extra dimensions under special conditions one can extract information from the interior of 4D black holes. For this purpose we study an induced geometry on a test brane in the background of a higher-dimensional static black string or a black brane. We show that, at the intersection surface of the test brane and the bulk black string or brane, the induced metric has an event horizon, so that the test brane contains a black hole. We call it a brane hole. When the test brane moves with a constant velocity $V$ with respect to the bulk black object, it also has a brane hole, but its gravitational radius ${r}_{e}$ is greater than the size of the bulk black string or brane ${r}_{0}$ by the factor $(1\ensuremath{-}{V}^{2}{)}^{\ensuremath{-}1}$. We show that bulk ``photon'' emitted in the region between ${r}_{0}$ and ${r}_{e}$ can meet the test brane again at a point outside ${r}_{e}$. From the point of view of observers on the test brane, the events of emission and capture of the bulk photon are connected by a spacelike curve in the induced geometry. This shows an example in which extra dimensions can be used to extract information from the interior of a lower-dimensional black object. Instead of the bulk black string or brane, one can also consider a bulk geometry without a horizon. We show that nevertheless the induced geometry on the moving test brane can include a brane hole. In such a case the extra dimensions can be used to extract information from the complete region of the brane-hole interior. We discuss thermodynamic properties of brane holes and interesting questions which arise when such an extra-dimensional channel for the information mining exists.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".