Central radio galaxies in galaxy clusters: Joint surveys by eROSITA and ASKAP
Bibliographic record
Abstract
Contact. The extended ROentgen Survey with an Imaging Telescope Array (eROSITA) telescope on board the Spectrum-Roentgen-Gamma (SRG) mission has completed the first eROSITA All-Sky Survey (eRASS:1). It detected ∼104 galaxy clusters in the western Galactic hemisphere. In the radio band, the Australian Square Kilometre Array Pathfinder (ASKAP) telescope completed its pilot 1 phase of the project Evolutionary Map of the Universe (EMU) with 220 000 sources in a 270 deg2 field overlapping with eRASS:1. These two surveys are used to study radio-mode active galactic nuclei in clusters. Aims. In order to understand the efficiency of radio-mode feedback at the centers of galaxy clusters, we relate the radio properties of the brightest cluster galaxies to the X-ray properties of the host clusters. Methods. We identified the central radio sources in eRASS:1 clusters or calculated corresponding upper limits on the radio luminosity. Then, we derived relations between the X-ray properties of the clusters and the radio properties of the corresponding central radio source. Results. In total, we investigated a sample of 75 clusters. We find a statistically significant correlation between the X-ray luminosity of the cluster and the 944 MHz radio luminosity of the corresponding central radio galaxy. There is also a positive trend between the radio power and the largest linear size of the radio source. The density and the largest linear size are not correlated. We find that the kinetic luminosity of the radio jets in high-luminosity clusters with LX > 1043 erg s−1 is no longer correlated with the X-ray luminosity, and we discuss various reasons. We find an anticorrelation between the central cooling time tcool and the radio luminosity LR, indicating a need for more powerful active galactic nuclei in clusters with short central cooling times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".