Signatures of Massive Black Hole Merger Host Galaxies from Cosmological Simulations. II. Unique Stellar Kinematics in Integral Field Unit Spectroscopy
Bibliographic record
Abstract
Abstract Secure methods for identifying the host galaxies of individual massive black hole (MBH) binaries and mergers detected by gravitational-wave experiments such as the Laser Interferometer Space Antenna and pulsar timing arrays are currently lacking, but will be critical to a variety of science goals. Recently, in Bardati et al., we used the Romulus25 cosmological simulation to show that MBH merger host galaxies have unique morphologies in imaging, due to their stronger bulges. Here, we use the same sample of simulated MBH merger host galaxies to investigate their stellar kinematics, as probed by optical integral field unit (IFU) spectroscopy. We perform stellar population synthesis and dust radiative transfer to generate synthetic 3D optical spectral data cubes of each simulated galaxy, and produce mock stellar kinematic maps. Based on a linear discriminant analysis of a combination of kinematic parameters derived from these maps, we show that this approach can identify MBH binary and merger host galaxies with accuracies that increase with chirp mass and mass ratio. For mergers with high chirp masses (≳108.2 M ⊙) and high mass ratios (≳0.5), the accuracies reach ≳85%, and their host galaxies are uniquely characterized by slower rotation and stronger stellar kinematic misalignments. These kinematic properties are commonly associated with massive early-type galaxies that have experienced major mergers, and naturally act as signposts for MBH binaries and mergers with high chirp masses and mass ratios. These results suggest that IFU spectroscopy should also play a role in telescope follow-up of future MBH binaries and mergers detected in gravitational waves.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".