Simulating supermassive black hole mass measurements for a sample of ultramassive galaxies using ELT/HARMONI high-spatial-resolution integral-field stellar kinematics
Bibliographic record
Abstract
ABSTRACT As the earliest relics of star formation episodes of the Universe, the most massive galaxies are the key to our understanding of the stellar population, cosmic structure, and supermassive black hole (SMBH) evolution. However, the details of their formation histories remain uncertain. We address these problems by planning a large survey sample of 101 ultramassive galaxies (z ≤ 0.3, |δ + 24°| < 45°, |b| > 8°), including 76 per cent ellipticals, 17 per cent lenticulars, and 7 per cent spirals brighter than MK ≤ −27 mag (stellar mass 2 × 1012 ≲ M⋆ ≲ 5 × 1012 M⊙) with ELT/HARMONI. Our sample comprises diverse galaxy environments ranging from isolated to dense-cluster galaxies. The primary goals of the project are to (1) explore the stellar dynamics inside galaxy nuclei and weigh SMBHs, (2) constrain the black hole scaling relations at the highest mass, and (3) probe the late-time assembly of these most massive galaxies through the stellar population and kinematical gradients. We describe the survey, discuss the distinct demographics and environmental properties of the sample, and simulate their HARMONI Iz-, Iz + J-, and H + K-band observations by combining the inferred stellar-mass models from Pan-STARRS observations, an assumed synthetic spectrum of stars, and SMBHs with masses estimated based on different black hole scaling relations. Our simulations produce excellent state-of-the-art integral field spectrography and stellar kinematics (ΔVrms ≲ 1.5 per cent) in a relatively short exposure time. We use these stellar kinematics in combination with the Jeans anisotropic model to reconstruct the SMBH mass and its error using a Markov chain Monte Carlo simulation. Thus, these simulations and modellings can be benchmarks to evaluate the instrument models and pipelines dedicated to HARMONI to exploit the unprecedented capabilities of ELT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".