XRISM Constrains Atmospheric Motion and Turbulent Dissipation in the Archetypal Radio-mode Feedback System Hydra-A
Bibliographic record
Abstract
Abstract We present XRISM Resolve observations centered on Hydra-A, a redshift z = 0.054 brightest cluster galaxy, which hosts one of the largest and most powerful FR-I radio sources in the nearby Universe. We examine the effects of its high jet power on the velocity structure of the cluster’s hot atmosphere. Hydra-A’s central radio jets have inflated X-ray cavities with energies upward of 1061 erg. They reach altitudes of 225 kpc from the cluster center, well beyond the atmosphere’s central cooling region. Resolve’s 3′× 3′ field of view covers 190 × 190 kpc, which encompasses most of the cooling volume. We find a one-dimensional atmospheric velocity dispersion across the volume of 164 ± 10 km s−1. The fraction in isotropic turbulence or unresolved bulk velocity is unknown. Assuming pure isotropic turbulence, the turbulent kinetic energy is 2.5% of the thermal energy radiated away over the cooling timescale, implying that kinetic energy must be supplied continually to offset cooling. While Hydra-A’s radio jets are powerful enough to supply kinetic energy to the atmosphere at the observed level, turbulent dissipation alone would struggle to offset cooling throughout the cooling volume. The central galaxy’s radial velocity is similar to the atmospheric velocity, with an offset of −37 ± 23 km s−1.
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 | 0.000 | 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".