Imprints of supermassive black hole evolution on the spectral and spatial anisotropy of nano-hertz stochastic gravitational-wave background
Bibliographic record
Abstract
ABSTRACT The formation and evolution of supermassive black holes (SMBHs) remains an open question in the field of modern cosmology. The detection of nanohertz (n-Hz) gravitational waves via pulsar timing arrays (PTAs) in the form of individual events and the stochastic gravitational wave background (SGWB) offers a promising avenue for studying SMBH evolution across cosmic time, with SGWB signal being the immediately detectable signal with the currently accessible telescope sensitivities. By connecting the galaxy properties in the large scale (Gpc scale) cosmological simulation such as MICECAT with the small scale ($\sim$Mpc scale) galaxy simulations from ROMULUS, we show that different scenarios of galaxy–SMBH evolution with redshift leads to a frequency-dependent spatial anisotropy in the SGWB signal. The presence of slow evolution of the SMBHs in the Universe leads to a pronounced blue anisotropic spectrum of the SGWB. In contrast, if SMBHs grow faster in the Universe in lighter galaxies, the frequency-dependent spatial anisotropy exhibits a more flattened anisotropic spectrum. This additional aspect of the SGWB signal on top of the monopole SGWB signal, can give insight on how the SMBHs form in the high-redshift Universe and its interplay with the galaxy formation from future measurements.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".