The Lower Limit of Dynamical Black Hole Masses Detectable in Virgo Compact Stellar Systems Using the JWST/NIRSpec IFU
Bibliographic record
Abstract
Abstract Due to observational challenges, the mass function of black holes (BH) at lower masses is poorly constrained in the local universe. Understanding the occupation fraction of BHs in low-mass galaxies is crucial for constraining the origins of supermassive BH seeds. Compact stellar systems (CSSs), including ultracompact dwarf galaxies (UCDs) and compact elliptical galaxies (cEs), are potential intermediate-mass BH hosts. Despite the difficulties posed by their limited spheres of influence, stellar dynamical modeling has been effective in estimating central BH masses in CSSs. Some CSSs may harbor a BH constituting up to 20% of their host stellar mass, while others might not have a central BH. In support of our ongoing efforts to determine the BH masses in select CSSs in the Virgo cluster using JWST/NIRSpec IFU observations and orbit-superposition dynamical models, we create mock kinematic data mimicking the characteristics of observed cEs/UCDs in the Virgo cluster with different BH masses. We then construct a series of dynamical models using the orbit-superposition code FORSTAND and explore the accuracy of recovering the BH mass. We find that the mass of BHs comprising 1% or more of the total host stellar mass can be accurately determined through kinematic maps featuring higher-order velocity moments. We also assess how BH mass measurement is affected by deprojection methods, regularization factors, anisotropy parameters, orbit initial conditions, the absence of higher-order velocity moments, the spatial resolution, and the signal-to-noise ratio.
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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 | 0.002 |
| 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 | 0.001 |
| 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".