Detectability of satellites around directly imaged exoplanets and brown dwarfs
Bibliographic record
Abstract
ABSTRACT Satellites around substellar companions are a heterogeneous class of objects with a variety of different formation histories. Focusing on potentially detectable satellites around exoplanets and brown dwarfs (BDs), we might expect to find objects belonging to two main populations: planet-like satellites similar to Titan or the Galileian Satellites – likely formed within the scope of core accretion; and binary-like objects, formed within different scenarios, such as disc instability. The properties of these potential satellites would be very different from each other. Additionally, we expect that their characterization would provide insightful information about the history of the system. This is particularly important for planets/BDs discovered via direct imaging (DI) with ambiguous origins. In this paper, we review different techniques, applied to DI planets/BDs, that can be used to discover such satellites. This was achieved by simulating a population of satellites around the exoplanet β Pic b, which served as a test case. For each simulated satellite, the amplitude of DI, radial velocity, transit and astrometric signals, with respect to the planet, were retrieved and compared with the detection limits of current and future instruments. Furthermore, we compiled a list of 38 substellar companions discovered via DI to give a preliminary estimate on the probability of finding satellites extracted from the two populations mentioned above, with different techniques. This simplified approach shows that detection of planet-like satellites, though not strictly impossible, is very improbable. On the other hand, the detection of binary-like satellites is within the capabilities of current instrumentation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".