THE ENERGETICS OF CUSP DESTRUCTION
Bibliographic record
Abstract
We present a new analytic estimate for the energy required to create a constant density core within a dark matter halo that, based on more realistic assumptions, leads to demands that are orders of magnitude lower than claimed in earlier works. We define a core size based on the logarithmic slope of the dark matter density profile as it is insensitive to the functional form used to fit observed data. The energy required to form a core sensitively depends on the radial scale over which dark matter within the cusp is redistributed within the halo. Simulations indicate that within a region in size comparable to the active star forming regions of the central galaxy inhabiting a halo, dark matter particles have their orbits radially increased by a factor of 2–3 during core formation. Thus, the inner properties of the dark matter halo set the energy requirements. The energy cost increases slowly with halo mass as for core sizes ≲1 kpc. We use the expected star formation history for a given halo mass to predict dwarf galaxy core sizes. We find that supernovae alone would create well over 4 kpc cores in 10 10 M galaxies if 100% of the energy were transferred to dark matter particle orbits. We can directly constrain the efficiency factor by studying galaxies with known stellar content and core size. We find that the efficiency of coupling between stellar feedback and dark matter orbital energy need only be ≲1% to explain Fornax's 1 kpc core.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".