Making a Map of Shadows and Stars: Astrophysical Probes of Dark Sectors
Bibliographic record
Abstract
While the Standard Model is an achievement of our understanding of the physical world down to fundamental levels, it is incomplete. Namely it cannot address the Hierarchy between the Higgs mass and the Planck scale, or provide a particle description of what constitutes dark matter. In this thesis I review these two issues and then outline my novel research to motivate models of dark sectors as potential solutions, and how we can test these theories with astrophysical probes. First, I develop tools for modeling hadronization in confining dark sectors without light dark quarks, allowing the showering of dark sector glueballs to be simulated with uncertainty estimates. I then use this work to constrain models of dark matter that annihilate to dark glueballs with indirect detection data from Fermi-LAT and AMS-02. I also present work updating this model of hadronization, and show how these dark sectors could be studied at the LHC. Lastly, I consider another dark sector, atomic dark matter, and present work studying its behaviour on subgalactic scales using cosmological simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".