INTO THE LAIR: GRAVITATIONAL-WAVE SIGNATURES OF DARK MATTER
Bibliographic record
Abstract
The nature and properties of dark matter (DM) are both outstanding issues in physics. Besides clustering in halos, the universal character of gravity implies that self-gravitating compact DM configurations—predicted by various models—might be spread throughout the universe. Their astrophysical signature can be used to probe fundamental particle physics, or to test alternative descriptions of compact objects in active galactic nuclei. Here, we discuss the most promising dissection tool of such configurations: the inspiral of a compact stellar-size object and consequent gravitational-wave (GW) emission. The inward motion of this "test probe" encodes unique information about the nature of the supermassive configuration. When the probe travels through some compact region we show, within a Newtonian approximation, that the quasi-adiabatic inspiral is mainly driven by DM accretion and by dynamical friction, rather than by radiation reaction. When accretion dominates, the frequency and amplitude of the GW signal produced during the latest stages of the inspiral are nearly constant. In the exterior region we study a model in which the inspiral is driven by GW and scalar-wave emission, described at a fully relativistic level. Resonances in the energy flux appear whenever the orbital frequency matches the effective mass of the DM particle, corresponding to the excitation of the central object's quasinormal frequencies. Unexpectedly, these resonances can lead to large dephasing with respect to standard inspiral templates, to such an extent as to prevent detection with matched filtering techniques. We discuss some observational consequences of these effects for GW detection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".