Bibliographic record
Abstract
Astrophysical observations on a wide range of scales indicate that the majority of matter in our Universe seems to be approximately inert and non-luminous. The existence of this dark matter implies the existence of an undiscovered particle, since there is no viable dark matter candidate within the Standard Model. Many terrestrial searches for dark matter particles are underway; however, there is no evidence to date that the dark matter interacts with particles in the Standard Model except through gravity. It highly conceivable that the dark matter exists as part of a rich hidden sector with diverse matter content and its own dark forces (in analogy to the Standard Model) which would imply that terrestrial searches may not pose an optimal path to discovering dark matter. Instead, observing astrophysical systems — where dark matter is known to be present through its gravitational influence — would be the best available way to test theories of dark matter where dark forces play a role in altering the properties of those systems. The complementarity of observing various astrophysical systems is a powerful asset for exploring the physics of dark sectors: one can explore broad classes of theories with dark forces in different environments, on different length scales, and at different epochs in the history of our Universe. This dissertation explores several scenarios where astrophysical observations inform our understanding of dark forces in a way that would not necessarily be possible on Earth. In particular, we consider dark sector energy dissipation, dark matter self-interaction, and the early Universe production of dark matter through dark channels. We propagate the implications of these effects for stars, supernovae, the Milky Way stellar disk, dwarf galaxies, galaxy clusters, large-scale structure, the epoch of reionization, and the cosmic microwave background.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".