VLTI/Hi-5: detection yield predictions for young giant exoplanets
Bibliographic record
Abstract
The Hi-5 instrument, a proposed high-contrast L' band (3.5-4.0 μm) nulling interferometer for the visitor focus of the Very Large Telescope Interferometer (VLTI), will characterize young extra-solar planetary systems and exozodiacal dust around nearby main-sequence stars. Thanks to VLTI's angular resolution (λ=B = 5 mas for the longest UT baseline), it will fill the gap between young giant exoplanets discovered by ongoing single-aperture direct imaging surveys and exoplanet populations discovered by radial velocity surveys. In this paper, we investigate the exoplanet detection yield of Hi-5. First, we present the latest catalog of stars identified as members of young stellar associations within 150 pc of the Sun thanks to the BANYAN algorithm and other searches for young moving group members. Realistic exoplanet populations are then generated around these stars and processed with the SCIFYsim tool, the end-to-end simulator for the Hi-5 instrument. Then, two formation models are used to estimate the giant planet's luminosity. The first is the New Generation Planetary Population Synthesis (NGPPS), also known as the Bern model, and the second is a statistical model based on gravitational instability (hot-start model - AMES-Dusty model). We show that Hi-5 is insensitive to cold-start planets but can detect giant hot-start planets. With ATs, more than 40 planets could be detected assuming 20 nights of observations. With its unique capabilities, Hi-5 is also able to constrain in mass the observed systems. Hi-5 is sensitive to planets with a mass > 2 Mjup around the snow line.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".