Assessing subhalo finders in cosmological hydrodynamical simulations
Bibliographic record
Abstract
ABSTRACT Cosmological simulations are essential for inferring cosmological and galaxy population properties based on forward-modelling, but this typically requires finding the population of (sub)haloes and galaxies that they contain. The properties of said populations vary depending on the algorithm used to find them, which is concerning as it may bias key statistics. We compare how the predicted (sub)halo mass functions, satellite radial distributions, and correlation functions vary across algorithms in the dark-matter-only and hydrodynamical versions of the flamingo simulations. We test three representative approaches to finding subhaloes: grouping particles in configuration- (Subfind), phase- (rockstar and VELOCIraptor) and history-space (hbt-herons). We also present hbt-herons, a new version of the hbt+ subhalo finder that improves the tracking of subhaloes. We find 10 per cent-level differences in the $M_{\mathrm{200c}}$ mass function, reflecting different field halo definitions and occasional miscentring. The bound mass functions can differ by 75 per cent at the high mass end, even when using the maximum circular velocity as a mass proxy. The number of well-resolved subhaloes differs by up to 20 per cent near $R_{\mathrm{200c}}$, reflecting differences in the assignment of mass to subhaloes and their identification. The predictions of different subhalo finders increasingly diverge towards the centres of the host haloes. The performance of most subhalo finders does not improve with the resolution of the simulation and is worse for hydrodynamical than for dark-matter-only simulations. We conclude that hbt-herons is the preferred choice of subhalo finder due to its low computational cost, self-consistently made and robust merger trees, and robust subhalo identification capabilities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".