Development of Eccentric Black Hole Binary Searches in the LIGO and PTA Regimes
Bibliographic record
Abstract
In the past several years, a plethora of gravitational wave events have been detected leading to better understanding of binary black holes, binary neutron stars, and neutron star black hole binaries. All of these transient detections have helped us better understand the dynamics of these systems as well as the populations of these objects, but each of these sources was detected with models that neglected eccentricity. Eccentricity is one of several potential markers for determining the formation of binary systems. Detecting gravitational waves from eccentric sources can better our understanding of such systems and help constrain theories about their formation. In the ground-based gravitational-wave regime, most eccentric binary black hole sources will be detected with little to no eccentricity (e0.1), but the lack of eccentricity-based models implemented into current search methods will make detecting such systems difficult. In the pulsar timing array regime, previous implementations of eccentricity-based models proved to be too computationally expensive. Recent developments in eccentric modeling of supermassive black hole binary systems have made it possible to incorporate eccentricity in a search for continuous gravitational waves from eccentric supermassive black hole binary sources. This work details the methods developed to aid in searching for eccentric stellar-mass black hole binary sources in the ground-based gravitational-wave regime and eccentric supermassive black hole binaries in the pulsar timing array regime.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".