Evolution of dwarf galaxies hosting GW150914-like events
Bibliographic record
Abstract
Here we present a detailed analysis of the properties and evolution of different dwarf galaxies, candidates to host the coalescence of black hole binary systems generating GW150914-like events. By adopting a novel theoretical framework coupling the binary population synthesis code seba with the Galaxy formation model gamesh, we can investigate the detailed evolution of these objects in a well-resolved cosmological volume of 4 cMpc, having a Milky Way (MW)-like galaxy forming at its centre. We identify three classes of interesting candidate galaxies: MW progenitors, dwarf satellites, and dwarf galaxies evolving in isolation. We find that (i) despite differences in individual histories and specific environments the candidates reduce to only nine representative galaxies; (ii) among them, |${\sim }44{{\ \rm per\ cent}}$| merge into the MW halo progenitors by the redshift of the expected signal, while the remaining dwarfs are found as isolated or as satellites of the MW and their evolution is strongly shaped by both peculiar dynamical history and environmental feedback; (iii) a stringent condition for the environments where GW150914-like binaries can form comes from a combination of the accretion history of their dark matter haloes and the radiative feedback in the high-redshift universe; and (iv) by comparing with the observed catalogues from the DGS and ALLSMOG surveys we find two observed dwarfs respecting the properties predicted by our model. We finally note how the present analysis opens the possibility to build future strategies for host galaxy identification.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".