The impact of baryons on the internal structure of dark matter haloes from dwarf galaxies to superclusters in the redshift range 0 &lt; <i>z</i> &lt; 7
Bibliographic record
Abstract
ABSTRACT We investigate the redshift evolution of the concentration–mass relationship of dark matter haloes in state-of-the-art cosmological hydrodynamic simulations and their dark-matter-only (DMO) counterparts. By combining the IllustrisTNG suite and the novel MillenniumTNG simulation, our analysis encompasses a wide range of box size ($50{-}740 \: \rm cMpc$) and mass resolution ($8.5 \times 10^4 {-} 3.1 \times 10^7 \: \rm {\rm M}_{\odot }$ per baryonic mass element). This enables us to study the impact of baryons on the concentration–mass relationship in the redshift interval $0\lt z\lt 7$ over an unprecedented halo mass range, extending from dwarf galaxies to superclusters ($\sim 10^{9.5}{-}10^{15.5} \, \rm {\rm M}_{\odot }$). We find that the presence of baryons increases the steepness of the concentration–mass relationship at higher redshift, and demonstrate that this is driven by adiabatic contraction of the profile, due to gas accretion at early times, which promotes star formation in the inner regions of haloes. At lower redshift, when the effects of feedback start to become important, baryons decrease the concentration of haloes below the mass scale $\sim 10^{11.5} \, \rm {\rm M}_{\odot }$. Through a rigorous information criterion test, we show that broken power-law models accurately represent the redshift evolution of the concentration–mass relationship, and of the relative difference in the total mass of haloes induced by the presence of baryons. We provide the best-fitting parameters of our empirical formulae, enabling their application to models that mimic baryonic effects in DMO simulations over six decades in halo mass in the redshift range $0\lt z\lt 7$.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".