XLSSC 122 caught in the act of growing up: Spatially resolved SZ observations of a z=1.98 galaxy cluster
Bibliographic record
Abstract
How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot ICM at high redshifts (z~2) is key to understanding how the continuous accretion from and mergers along the filamentary large-scale structure impact the first phases of cluster formation. We study the dynamical state and morphology of the z=1.98 galaxy cluster XLSSC 122 with high-resolution observations (~5") of the ICM through the SZ effect. Via Bayesian forward modeling, we map the ICM on scales from the virial radius down to the core of the cluster. To constrain such a broad range of spatial scales, we employ a new technique that jointly forward-models parametric descriptions of the pressure distribution to interferometric ACA and ALMA observations and multi-band imaging data from the 6-m, single-dish Atacama Cosmology Telescope. We detect the SZ effect with $11σ$ in the ALMA+ACA observations and find a flattened inner pressure profile that is consistent with a non-cool core classification with a significance of $>3σ$. In contrast to the previous works, we find better agreement between the SZ effect signal and the X-ray emission as well as the cluster member distribution. Further, XLSSC 122 exhibits an excess of SZ flux in the south of the cluster where no X-ray emission is detected. By reconstructing the interferometric observations and modeling in the uv-plane, we obtain a tentative detection of an infalling group or filamentary-like structure that is believed to boost and heat up the ICM while the density of the gas is low. In addition, we provide an improved SZ mass of $M_{500,\mathrm{c}} = 1.66^{+0.23}_{-0.20} \times 10^{14} \rm M_\odot$. Altogether, the observations indicate that we see XLSSC 122 in a dynamic phase of cluster formation while a large reservoir of gas is already thermalized.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".