The Dynamical Implications for Stars, Star Formation, and Dark Matter Cores in Dwarf Galaxies
Bibliographic record
Abstract
I investigate the observational signatures of the formation of dark matter cores in dwarf galaxies. I adopt the paradigm where the energy from star formation feedback is injected into the orbits of dark matter particles, forming a constant density core consistent with observations of dwarf galaxies. Using physically motivated constraints I show there is ample feedback energy available given the average stellar mass of dwarf galaxies to form cores in $10^{8}$--$10^{11}$\thinspace M$_{\odot}$ halos, and predict the maximum core size as a function of stellar mass. I describe how observational features of the old stellar content of dwarf galaxies are due to this core formation paradigm. As both dark matter and stars are collisionless fluids, the stars responsible for the feedback form in the centres of dwarf galaxies and have their orbits grown by subsequent star formation. This will naturally lead to age and metallicity gradients, with the younger and more metal rich stellar population near the dwarf centres. This process also prevents the destruction of globular clusters by driving them out of the dwarf nucleus --- the decrease in central dark matter density reduces the strength of dynamical friction --- and increases the likelihood of being stripped onto the stellar halos of larger galaxies. It also offers a model for forming multiple populations in globular clusters, with the only assumption being that the source of the polluted gas resides within the dwarf progenitor. As the orbit of a globular cluster grows, it will experience multiple accretion events with each pass through the gas-rich galaxy centre. The simple accretion model exhibits two traits revealed from observations --- a short accretion timescale and a sensitive dependence on mass --- without requiring an exotic initial stellar mass function or the initial globular cluster mass function to be 10--25 times larger than at present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".