Measuring dark matter spikes around primordial black holes with Einstein Telescope and Cosmic Explorer
Bibliographic record
Abstract
Future ground-based gravitational wave observatories will be ideal probes of the environments surrounding black holes with masses $1\ensuremath{-}10{M}_{\ensuremath{\bigodot}}$. Binary black hole mergers with mass ratios of order $q={m}_{2}/{m}_{1}\ensuremath{\lesssim}{10}^{\ensuremath{-}3}$ can remain in the frequency band of such detectors for months or years, enabling precision searches for modifications of their gravitational waveforms with respect to vacuum inspirals. As a concrete example of an environmental effect, we consider here a population of binary primordial black holes which are expected to be embedded in dense cold dark matter spikes. We provide a viable formation scenario for these systems compatible with all observational constraints and predict upper and lower limits on the merger rates of small-mass-ratio pairs. Given a detected signal of one such system by either Einstein Telescope or Cosmic Explorer, we show that the properties of the binary and of the dark matter spike can be measured to excellent precision with one week's worth of data, if the effect of the dark matter spike on the waveform is taken into account. However, we show that there is a risk of biased parameter inference or missing the events entirely if the effect of the predicted dark matter overdensity around these objects is not properly accounted for.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".