Average X-ray properties of galaxy groups: From Milky Way-like halos to massive clusters
Bibliographic record
Abstract
Context. In this study, we present the average X-ray properties of massive halos at z < 0.2 over the largest halo mass range ever probed so far, bridging the gap from Milky Way-like halos to massive clusters. Aims. The results show the average X-ray properties of galaxy groups, obtained through the stacking analysis in the eFEDS area of the GAMA galaxy group sample at z < 0.2. The results have been rigorously tested using a synthetic dataset that mirrors the observed eROSITA X-ray and GAMA optical data based on the lightcones of the Magneticum simulations. Methods. We used a halo mass proxy based on group total luminosity, avoiding systematics linked to velocity dispersion and richness cuts. The stacking is done in bins of halo mass and tested in the synthetic dataset for AGN and X-ray binaries contamination, systematics due to the halo mass proxy, and uncertainty in the optical group center. Results. We provide the average X-ray surface brightness profile in six bins of mass, ranging from Milky Way-like systems to poor clusters at M 200 ∼ 10 14 M ⊙ . We find that the scatter in the L X − M relation is driven by gas concentration in groups, as undetected X-ray systems at fixed halo mass exhibit lower central gas concentrations than detected ones, aligning with Magneticum predictions. However, there is a discrepancy regarding dark matter concentration: Magneticum predictions suggest that undetected groups are more concentrated, implying they are older and more relaxed, whereas previous observational findings suggest the opposite. We present new measurements of the L X, 500 − M 500 and L X, 200 − M 200 relations, from Milky Way-like halos to massive clusters. Our results indicate that a single power law fits the data across three decades of halo mass, and they align well with previous studies focused on specific halo mass ranges. Magneticum best matches the observed gas distribution across the entire halo mass range, while IllustrisTNG, EAGLE, Simba, and FLAMINGO show larger discrepancies at different mass ranges. This highlights that simulations such as Magneticum, which are not calibrated on z = 0 galaxy properties, reproduce gas properties well but still lead to overly massive galaxies at the centers of massive halos. Conversely, simulations calibrated on z = 0 galaxy properties fail to reproduce the gas properties. Conclusions. This evidence reveals a potential gap in our understanding of the relationship between galaxies and their host structures. Therefore, this work emphasizes the need for a deeper investigation into the connection between gas and dark matter distributions and their impact on central galaxy properties. Such an inquiry is crucial to comprehensively understanding the role and interplay of gravitational forces and feedback-related processes in shaping both the large-scale structure and the galaxy population.
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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 | 0.001 |
| 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".