Interpreting Sunyaev–Zel’dovich observations with MillenniumTNG: mass and environment scaling relations
Bibliographic record
Abstract
ABSTRACT Sunyaev–Zel’dovich (SZ) measurements can dramatically improve our understanding of the intergalactic medium and the role of feedback processes in galaxy formation, allowing us to calibrate important astrophysical systematics in cosmological constraints from weak lensing galaxy clustering surveys. However, the signal is only measured in a two-dimensional projection, and its correct interpretation relies on understanding the connection between observable quantities and the underlying intrinsic properties of the gas, in addition to the relation between the gas and the underlying matter distribution. One way to address these challenges is through the use of hydrodynamical simulations such as the high-resolution, large-volume MillenniumTNG suite. We find that measurements of the optical depth, τ, and the Compton-y parameter, Y, receive large line-of-sight contributions that can be removed effectively by applying a compensated aperture photometry filter. In contrast with other τ probes (e.g. X-rays and fast radio bursts), the kinematic SZ-inferred τ receives most of its signal from a confined cylindrical region around the halo due to the velocity decorrelation along the line of sight. Additionally, we perform fits to the Y–M and τ–M scaling relations and report best-fitting parameters adopting the smoothly broken power law formalism. We note that subgrid physics modelling can broaden the error bar on these by 30 per cent for intermediate-mass haloes (${\sim }10^{13} \, {\rm M}_{\odot }$). The scatter of the scaling relations can be captured by an intrinsic dependence on concentration and an extrinsic dependence on tidal shear. Finally, we comment on the effect of using galaxies rather than haloes in observations, which can bias the inferred profiles by ∼20 per cent for L* galaxies.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".