Machine learning classification of black holes in the mass-spin diagram
Bibliographic record
Abstract
We present the mass-spin diagram for classifying black holes and studying their formation pathways providing an analog to the Hertzsprung-Russell diagram. This allows for black hole evolutionary tracks as a function of redshift, combining formation, accretion, and merger histories for the variety of black hole populations. A realistic black hole continuum constructed from initial mass and spin functions and approximate redshift evolution reveals possible black hole main sequences, such as sustained coherent accretion through cosmic time or hierarchical merger trees. In the stellar-mass regime, we use a binary population synthesis software to compare three spin prescriptions for tidal evolution of Wolf-Rayet progenitors, showing how the mass-spin diagram exposes interesting modeling differences. We then classify black hole populations by applying supervised and unsupervised machine learning clustering methods to mass-spin datasets. While bare unsupervised clustering can nearly recover canonical population boundaries (stellar-mass, intermediate-mass, and supermassive), a more sophisticated approach utilizing deep learning via variational autoencoders for latent space representation learning aids in clustering of realistic datasets with subclasses that highly overlap in mass-spin space. We find that a supervised random forest can accurately recover the correct clusters from the learned latent space representation depending on the complexity of the underlying dataset, semisupervised methods show potential for further development, and the performance of unsupervised classifiers is a great challenge. Our findings motivate future machine learning applications and demonstrate that the mass-spin diagram can be used to connect gravitational-wave and electromagnetic observations with theoretical models.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".