Applying the Metallicity-dependent Binary Fraction to Double White Dwarf Formation: Implications for LISA
Bibliographic record
Abstract
Abstract Short-period double white dwarf (DWD) binaries will be the most prolific source of gravitational waves (GWs) for the Laser Interferometer Space Antenna (LISA). DWDs with GW frequencies below ∼1 mHz will be the dominant contributor to a stochastic foreground caused by overlapping GW signals. Population modeling of Galactic DWDs typically assumes a binary fraction of 50% and a log-uniform zero-age main sequence (ZAMS) orbital period distribution. However, recent observations have shown that the binary fraction of close, solar-type stars exhibits a strong anticorrelation with metallicity that modulates the ZAMS orbital period distribution below 10 4 days. In this study, we perform the first simulation of the Galactic DWD population observable by LISA that incorporates an empirically derived metallicity-dependent binary fraction, using the binary population synthesis suite COSMIC and a metallicity-dependent star formation history. We compare two models: one that assumes a metallicity-dependent binary fraction, and one with a binary fraction of 50%. We repeat our analysis for three different assumptions for Roche-lobe overflow interactions. We find that while metallicity impacts the evolution and intrinsic properties of our simulated DWD progenitor binaries, the LISA-resolvable populations of the two models remain roughly indistinguishable. However, the size of the total Galactic DWD population orbiting in the LISA frequency band is reduced by more than half when accounting for a metallicity-dependent binary fraction for two of our four variations, which also lowers the effective foreground. The LISA population remains unchanged in number for two variations, highlighting the sensitivity of the population to binary evolution prescriptions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".