Towards realistic modelling of the astrometric capabilities of MCAO systems: detecting an intermediate-mass black hole with MAVIS
Bibliographic record
Abstract
ABSTRACT Accurate astrometry is a key deliverable for the next generation of multiconjugate adaptive optics (MCAO) systems. The MCAO-Assisted Visible Imager and Spectrograph (MAVIS) is being designed for the Very Large Telescope Adaptive Optics Facility and must achieve 150 $\mu$as astrometric precision (50 $\mu$as goal). To test this before going on-sky, we have created MAVIS Image Simulator (mavisim), a tool to simulate MAVIS images. mavisim accounts for three major sources of astrometric error: high- and low-order point spread function (PSF) spatial variability, tip–tilt residual error, and static field distortion. When exploring the impact of these three error terms alone, we recover an astrometric accuracy of 50 $\mu$as for all stars brighter than m = 19 in a 30 s integration using PSF-fitting photometry. We also assess the feasibility of MAVIS detecting an intermediate-mass black hole (IMBH) in a Milky Way globular cluster. We use an N-body simulation of an NGC 3201-like cluster with a central 1500 M⊙ IMBH as input to mavisim and recover the velocity dispersion profile from proper motion measurements. Under favourable astrometric conditions, the dynamical signature of the IMBH is detected with a precision of ∼0.20 km s−1 in the inner ∼4 arcsec of the cluster where Hubble Space Telescope (HST) is confusion limited. This precision is comparable to measurements made by Gaia, HST, and Multi Unit Spectroscopic Explorer (MUSE) in the outer ∼60 arcsec of the cluster. This study is the first step towards building a science-driven astrometric error budget for an MCAO system and a prediction of what MAVIS could do once on sky.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".