Measuring cavity powers of active galactic nuclei in clusters using a hybrid X-ray–radio method
Bibliographic record
Abstract
Measurements of the quantity of radio-mode feedback injected by an active galactic nucleus into the cluster environment have mostly relied on X-ray observations, which reveal cavities in the intracluster medium excavated by the radio lobes. However, the sensitivity required to accurately constrain the dimensions of these cavities has proven to be a major limiting factor and it is the main bottleneck of high-redshift measurements. We describe a hybrid method based on a combination of X-ray and radio observations, which aims to enhance our ability to study radio-mode feedback. In this paper, we present one of the first samples of galaxy clusters observed with the International LOFAR Telescope (ILT) at 144 MHz and use this sample to test the hybrid method at lower frequencies than before. By comparing our measurements with results found in literature based on the traditional method using only X-ray observations, we find that the hybrid method provides consistent results to the traditional method. In addition, we find that the correlation between the traditional method and the hybrid method improves as the X-ray cavities are more clearly defined. This suggests that using radio lobes as proxies for cavities may help to circumvent systematic uncertainties in the cavity volume measurements. Encouraged by the high volume of unique ILT observations which have been successfully processed, this hybrid method enables radio-mode feedback to be studied at high redshifts for the first time even for large samples of clusters.
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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 | 0.003 | 0.001 |
| 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) | 0.001 | 0.001 |
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".