Constraining Galactic Structure with the LISA White Dwarf Foreground
Bibliographic record
Abstract
Abstract White dwarfs (WDs) comprise 95% of all stellar remnants, and are thus an excellent tracer of old stellar populations in the Milky Way. Current and planned telescopes are not able to directly probe the WD population in its entirety due to its inherently low luminosity. However, the Galactic population of double white dwarf binaries (DWDs) gives rise to a millihertz gravitational-wave foreground detectable by the Laser Interferometer Space Antenna (LISA). Here we show how characterizing the angular power of the WD foreground will enable probes of the Galactic structure in a novel way to determine whether the Galactic WD population traces the spatial distribution of young, bright stars, or traces a vertically heated spatial distribution associated with the Galaxy’s oldest stellar populations. We do this using a binary population synthesis study that incorporates different Galactic spatial distributions for the DWD population. We find that the level of anisotropy in the WD foreground’s angular power spectrum is dependent on the vertical scale height of the population, but show that multipole coefficients from the spherical harmonic decomposition must be considered individually because of LISA’s angular resolution. Finally, we show that LISA can probe the vertical scale height of the Galactic WD population with an accuracy of 300 pc, using the hexadecapole moment of the WD foreground.
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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 | 0.001 | 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 | 0.000 | 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".