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REVIEW 3 major objections 5 minor 139 references

Hydro solver sets AGN jet lobe shape; feedback energy stays the same.

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2026-08-04 08:52 UTC pith:2IYCNGDV

load-bearing objection A careful, honest three-code comparison of a genuinely code-agnostic jet injection scheme; the qualitative morphology ordering holds up, but the paper itself shows that part of that ordering is set by the fixed-neighbour-number injection choice, not by the solver alone. the 3 major comments →

arxiv 2510.18293 v2 pith:2IYCNGDV submitted 2025-10-21 astro-ph.GA

On the consistency of jet feedback modelling across different astrophysics hydrodynamical codes

N. Maragkakis , M. A. Bourne , C. Power , F. Hu\v{s}ko , A. Ludlow , S. Shabala This is my paper
classification astro-ph.GA
keywords AGN feedbackjet feedbacksubgrid modelsSPHmoving-meshEulerian gridjet lobescode comparison
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper asks whether the choice of hydrodynamical code changes how AGN jet feedback deposits energy in the gas when the subgrid injection model is exactly identical. The authors build a code-agnostic virtual-particle jet launcher and run it in three codes: an SPH code, a moving-mesh code, and a static-grid code. In uniform and stratified media, all jets drive bow shocks and inflate self-similar lobes, but lobe shape and temperature differ systematically—short, wide, hot lobes in SPH; long, thin, cool lobes in the moving-mesh; intermediate in the grid code. Yet the energy budget is the same: all codes transfer about 60% of the injected energy to the ambient medium, and after switch-off the remnants heat their surroundings almost identically. The paper concludes that the hydrodynamical method, through its effective resolution and how feedback couples to it, is responsible for the morphological differences, and that in cosmological simulations these solver differences are probably smaller than subgrid-model uncertainties.

Core claim

Central claim: with the subgrid jet prescription held identical, the hydrodynamical discretisation sets lobe morphology. A virtual-particle launcher deposits mass, momentum, and energy into the ten nearest gas elements; in a uniform medium SWIFT produces short, wide, hot lobes, AREPO long, thin, cool lobes, and PLUTO intermediate ones. The same ordering holds in stratified media and in post-switch-off remnants, yet all codes transfer about 60% of the injected energy to the ambient medium and remnant thermodynamic profiles converge. The mechanism is injection coupling: PLUTO's fixed-volume cells reach very low masses in the jet channel, so equal energy injection yields higher effective jet ve

What carries the argument

The virtual-particle jet injection scheme: pairs of hydrodynamically decoupled particles are launched from the centre in opposite directions within 15-degree cones, travel ballistically to a radius of 10 kpc, and deposit their mass, momentum, and kinetic energy into the 10 nearest gas elements in a mass-weighted fashion. This scheme is deliberately identical across the three codes, making each code's effective resolution the only remaining variable. The self-similar analytic scaling for lobe length, L ~ (P_j / rho0)^(1/5) t^(3/5) in a uniform medium (with a generalisation for power-law atmospheres), serves as the benchmark against which all three codes are judged; SWIFT under-predicts, AREPO

Load-bearing premise

The paper's central comparison assumes the virtual-particle injection scheme is truly code-agnostic; the paper itself demonstrates that it is not, because the fixed-neighbour-number deposition produces very different effective jet velocities when a code's resolution elements have very different masses.

What would settle it

Run the fixed-neighbour-mass injection in all three codes across the full resolution and jet-velocity grid; if the lobe length, width, and temperature distributions collapse onto each other, the claim that the hydrodynamical method itself drives the differences is falsified. The paper's own lower-resolution runs show better code-to-code agreement, so a resolution-convergence study is the decisive test.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Identical subgrid jet prescriptions do not guarantee identical jet morphology: solver choice alone shifts lobe length, width, and temperature by tens of percent at fixed resolution and parameters.
  • The energy budget is code-independent at the level that matters for feedback: all codes deposit roughly 60% of the injected jet energy into the ambient medium, mostly as thermal energy, and leave similar density, temperature, and entropy profiles.
  • In cosmological simulations, where resolution is coarser and subgrid choices dominate, solver differences in jet morphology are likely subdominant; averaging over galaxy populations would further wash them out.
  • The way a subgrid model couples to the resolution elements—fixed neighbour number vs fixed neighbour mass—is itself a modelling choice that can change lobe evolution as much as the solver, so feedback schemes should specify and test this coupling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A direct consequence the authors leave implicit: reported 'code differences' in AGN feedback studies may often be partly an artefact of injection-region coupling rather than the Riemann solver or discretisation; future comparison projects could standardise both the injected neighbour mass and the time-stepping near the injection region.
  • If the energetic impact is truly code-independent while lobe morphology is not, then radio-lobe appearance (length, width, temperature) is a poor guide to the feedback energy actually deposited in the gas—a caution for interpreting X-ray cavities and radio observations.
  • A testable extension: implement the same virtual-particle scheme in a fourth, meshless finite-mass code without mass refinement; the framework predicts its lobe properties should fall between the SPH and moving-mesh results.
  • The resolution trend—codes agreeing better at low resolution—implies that calibration of subgrid jet models on one code may not transfer to another at high resolution; calibrating per code may be necessary even with identical physics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces a virtual-particle jet-launching scheme implemented identically in three hydrodynamical codes—SWIFT (SPH), AREPO (moving-mesh), and PLUTO (static grid)—and uses it to compare idealised, non-relativistic AGN jets and their remnants in uniform and stratified media. The authors report that all codes produce self-similar lobe expansion, with SWIFT lobes shorter/wider/hotter, AREPO lobes longer/thinner/cooler, and PLUTO lobes intermediate, and that all transfer about 60% of injected energy to the ambient medium. They also study resolution, injection-velocity, and neighbour-mass variations, and conclude that code differences arise from the coupling of feedback to resolvable scales and from effective resolution, but are likely subdominant to subgrid-model uncertainty in cosmological simulations.

Significance. If the reported differences are robust, the paper would be a valuable reference for the AGN-feedback community: it uses a deliberately code-agnostic injection model, includes systematic resolution and velocity studies, a stratified-medium setup, and a remnant phase, and it confronts the simulations with an analytic self-similar benchmark that is not fitted to the data. The inclusion of the fixed-neighbour-mass control in §4.4 is a particular strength, as it partially tests the sensitivity of the comparison to the injection coupling. The main value lies not in any single code's prediction but in quantifying the spread across methods and in identifying effective jet velocity and mass resolution as key mediating factors.

major comments (3)
  1. [§4.4, Fig. 9, Eqs. (6)–(11)] The claim in the abstract and §1 that the comparison 'isolates the impact of hydrodynamical solvers' is not supported by the fiducial injection scheme. Virtual particles deposit into the nearest 10 gas elements, but PLUTO cells in the jet region can reach arbitrarily low masses, so the same injected kinetic energy (Eq. 8) produces a higher effective jet velocity in PLUTO than in SWIFT/AREPO. Section 4.4 explicitly shows that with fixed neighbour mass M_ngb = 20×10^5 Msun, AREPO and PLUTO lobe-length tracks converge at all three injection velocities (Fig. 9 bottom vs middle), while SWIFT is nearly unchanged; Section 6 concedes that 'Using a fixed-neighbour-mass scheme reduced differences between these two codes.' Thus the headline ordering SWIFT-short/AREPO-long/PLUTO-intermediate is partly an artefact of the fixed-neighbour-number choice rather than a pure property of the hydrodynamical
  2. [§4.1, Figs. 5–7 and 9] All lobe metrics are single realisations. Only one random launch-angle sequence is used per run (Section 3.2), and Section 4.4 itself notes sensitivity to 'the stochasticity of individual runs.' Figures 5–7 and 9 contain no error bars or ensemble scatter, so the magnitude of code-to-code differences (e.g., 'PLUTO over-predicts lobe volume by 20%' in §4.1, or the 4–14% resolution trends in §4.2) is not separated from run-to-run variance, which is likely significant given the observed KH and RT instabilities. I ask for at least a small number of repeat runs for the fiducial and for the key neighbour-mass comparison, with reported scatter; if that is impractical, the text should explicitly state that all quantitative differences are single-realization estimates.
  3. [§4.1, bullet list 'Jet lobe material'] The lobe length and width are defined by ad hoc thresholds (T > 10^8 K or v > 0.5×10^4 km/s) and by averaging the furthest 1% of lobe elements. These thresholds are not varied, and the cross-check with tracer-based definitions is only reported for AREPO and PLUTO, not SWIFT. Since all quantitative claims about code ordering rest on these definitions, the paper should provide a robustness test (e.g., varying the temperature/velocity thresholds and the percentile) or demonstrate explicitly that the code ordering is insensitive to the choice.
minor comments (5)
  1. [§3.2, Eq. (11)] The momentum-update formula is taken from Bourne & Sijacki (2017), but the sign convention for p_i0 relative to the jet direction is not defined, and the condition under which 'an additional thermal component' is added is not specified. A short derivation or explanatory sentence would help reproducibility.
  2. [§3.2 and §4.1] The parameter v_j is called the 'injection velocity' throughout, but Section 4.1 shows that the effective gas velocity in PLUTO can be higher because of low cell masses. Consider consistently using 'virtual-particle speed' for v_j to avoid confusion.
  3. [§4.2] The point that an equal number of resolution elements 'inherently disadvantages' SWIFT is important, but it appears only parenthetically in the text. It should be stated in the initial-conditions section (Section 3.3) so that the reader can interpret the resolution study from the start.
  4. [§4.5, Fig. 12] The two PLUTO resolutions (SR and HR) are both discussed, but the figures and text sometimes refer only to 'red' without always clarifying which run is shown. Explicit labels (e.g., 'PLUTO-SR' and 'PLUTO-HR') in all panels would improve clarity.
  5. [§5.1.3] The statement that PLUTO jet simulations 'almost always employ' the special-relativistic or MHD modules is a broad claim without supporting citations. Please add specific references or soften the statement.

Circularity Check

0 steps flagged

No material circularity: the code comparison is an independent numerical experiment with an external analytic benchmark; self-citations are methodological, not load-bearing.

full rationale

The paper's central result—differences in jet lobe properties across SWIFT, AREPO, and PLUTO—is produced by running three independent hydrodynamical codes with a fully specified injection model; it is not derived from a fitted parameter or from a quantity defined in terms of the outcome. The analytic self-similar solution (Eqs. 2–5) is an external benchmark from Falle (1991), Kaiser & Alexander (1997), and Komissarov & Falle (1998); the constant C is obtained from the assumed lobe geometry, not from the simulation curves. The injection model is described self-containedly (Section 3.2, Eqs. 6–11). The momentum-update formula is attributed to Bourne & Sijacki (2017), a methodological citation that is not used to force any conclusion; the paper explicitly lists it as a caveat. The paper itself identifies the main confound—fixed-neighbour-number injection couples differently in PLUTO because of low cell masses (§4.4)—and tests it with a fixed-neighbour-mass scheme, which is an honest internal control rather than a circular step. The claim of 'identical subgrid prescriptions' is weakened by this coupling effect, but that is an internal-validity caveat, not an equivalence of inputs and outputs. No step in the derivation chain reduces to its own input by construction.

Axiom & Free-Parameter Ledger

8 free parameters · 5 axioms · 1 invented entities

The central comparison rests on a small number of hand-set injection and environment parameters rather than fitted constants; the analytic self-similar solution is an external benchmark, not a fit. The main added assumption is the virtual-particle injection recipe, whose coupling details demonstrably affect the size of code differences (Section 4.4).

free parameters (8)
  • Jet power P_j = 5×10^45 erg/s
    Hand-set to typical moderate-to-high-power radio-galaxy jets; controls self-similar amplitude and injected energy.
  • Virtual-particle injection velocity v_j = 2×10^4, 4×10^4, 8×10^4 km/s
    Hand-set; higher velocities produce faster, lighter injections and alter lobe morphology (Section 4.3).
  • Virtual-particle mass m_j = 4×10^4 M⊙, halved to 2×10^4 M⊙ in stratified runs
    Hand-set to fix injection cadence and mass per event.
  • Injection radius r0 = 10 kpc
    Hand-set distance virtual particles travel decoupled before depositing mass, momentum, and energy.
  • Injection neighbour number nngb = 10
    Fiducial fixed-neighbour-number coupling; drives effective velocity differences across codes (Section 4.4).
  • Fixed neighbour mass M_ngb (alternative scheme) = 20×10^5 M⊙
    Control scheme; reduces AREPO-PLUTO differences and demonstrates coupling-scheme dependence.
  • Lobe definition thresholds = T>10^8 K or v>0.5×10^4 km/s; length/width from top 1% of elements
    Ad hoc analysis definitions that set measured lobe properties; authors checked rough agreement with tracers.
  • Ambient medium parameters = Uniform: ρ0=2.5×10^-26 g/cm3, T=10^7 K; stratified: ρ0=10^-25 g/cm3, β=0.38, rc=10 kpc
    Chosen to represent cluster cores and to make jets reach the self-similar regime; environment affects scaling.
axioms (5)
  • domain assumption Self-similar analytic lobe solution (Eqs. 2-5; Falle 1991; Kaiser & Alexander 1997) with constant aspect ratio accurately represents large-scale jet lobe evolution.
    Used as external benchmark in Figs 5-6; ignores breakout, backflows, instabilities and non-thermal pressure, as acknowledged in Section 3.1.
  • domain assumption Adiabatic ideal gas with no cooling, self-gravity, magnetic fields or special relativity.
    Set in Section 3; isolates pure hydrodynamics but excludes processes known to affect jet stability and lobe properties.
  • ad hoc to paper Virtual particles can travel 10 kpc fully decoupled from the gas, then deposit into the nearest 10 gas elements.
    Defines the subgrid injection model; has no direct physical counterpart and its coupling choices affect the code-difference magnitude (Section 4.4).
  • domain assumption Equal initial numbers of resolution elements with uniform spacing is a fair cross-code baseline.
    Adopted in Sections 3.3 and 4.2; authors note this inherently disadvantages SPH smoothing, so code ordering may change with matched spatial resolution.
  • domain assumption Stratified initial conditions are in hydrostatic equilibrium and remain stable over the run.
    Tested for four jet durations and relaxed about 200 Myr before launch (Section 4.5).
invented entities (1)
  • Hydrodynamically decoupled virtual particle no independent evidence
    purpose: Carries mass, momentum and energy from the jet origin to r0=10 kpc, then injects into nearest gas elements; enables an identical subgrid recipe across codes.
    Pure numerical construct with no observational handle; its 10 kpc free-streaming distance, neighbour number, and cone geometry are hand-set.

pith-pipeline@v1.3.0-alltime-deepseek · 31764 in / 16737 out tokens · 143008 ms · 2026-08-04T08:52:54.378698+00:00 · methodology

0 comments
read the original abstract

Active Galactic Nuclei (AGN) feedback is essential in cosmological simulations of galaxy formation, yet its implementation has to rely on subgrid models due to limited resolution. We present a novel subgrid jet-launching method for galaxy formation simulations and implement it in three hydrodynamical codes: the smoothed particle hydrodynamics (SPH) code SWIFT, the moving-mesh code AREPO, and the Eulerian grid code PLUTO. To isolate the impact of hydrodynamical solvers on jet evolution, we compare idealised jets and their remnants in uniform and stratified media across resolutions and jet parameters. In uniform media, all jets drive bow shocks, inflate hot lobes, exhibit backflows, and evolve self-similarly. For the parameters explored, SWIFT lobes are shorter, wider, and hotter; AREPO lobes are longer, thinner, and cooler; while PLUTO lobes display complex flows with intermediate characteristics. In stratified media, jets deviate from self-similar evolution, inflating longer and thinner lobes due to lower external ram pressure. After switch-off, SWIFT jets evolve into smooth cylindrical bubbles, AREPO jets produce long filamentary remnants, and PLUTO jets yield intermediate-length remnants with varying degrees of mixing. Despite such differences, all jets and remnants have a similar impact on the ambient medium. We conclude that variations in lobe properties between codes emerge even for identical subgrid prescriptions, since the coupling of jet feedback to resolvable scales and the effective resolution depend on the hydrodynamical method. In structure formation simulations, these solver differences are likely subdominant to uncertainties in subgrid modelling and calibration, while averaging over galaxy populations may lessen their impact.

Figures

Figures reproduced from arXiv: 2510.18293 by A. Ludlow, C. Power, F. Hu\v{s}ko, M. A. Bourne, N. Maragkakis, S. Shabala.

Figure 1
Figure 1. Figure 1: Sketch of our virtual-particle model for a single injection event. Two virtual particles are launched in opposite directions, inside two cones, and travel for 10 kpc without interacting with the medium, before they inject mass, momentum, and energy into their gas neighbours. Denoted are the different components of the resulting jets and lobes they inflate. are 2 × Δ𝑚 and 2 × Δ𝐸. Each neighbouring gas eleme… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of swift, arepo and pluto jets. Top: Overview slices in the 𝑦 = 0 plane in the uniform medium after 98 Myr at our highest resolution (𝑁 = 108 , 𝑀gas = 2 × 105 M⊙) simulations. Mapped quantities are temperature (top left), density (top right), pressure (bottom left), and velocity magnitude (bottom right). All jets drive a bow shock in the ambient medium, inflate lobes, and develop backflows. swif… view at source ↗
Figure 3
Figure 3. Figure 3: Radial velocity slices of jets simulated with the three codes in the uniform medium after 98 Myr. All jets display a central sheath of high-velocity material moving outwards along the jet axis, surrounded by backflows. swift jets show the least coherent backflows, arepo jets display stronger and more streamlined backflows, and pluto jets present fast but irregular backflows. Hardcastle & Krause 2013; Huško… view at source ↗
Figure 4
Figure 4. Figure 4: Temperature (top) - density (bottom) slices of the time evolution of the jets simulated with the three codes in the uniform medium. Different rows showcase the different jets at progressively later times. The swift lobes retain their shape and evolve self-similarly from early on, while arepo and pluto lobes are becoming more elongated, for at least the first 50 Myr. The top and bottom lobes progressively f… view at source ↗
Figure 5
Figure 5. Figure 5: Plots of lobe length and width with respect to time for jets simulated with the three codes in the uniform medium. Black lines represent the lobes simulated with swift, blue lines the arepo lobes, and red lines the pluto lobes. Solid lines represent estimates of length, and dotted lines represent estimates of width. The dashed grey lines represent the analytic, self-similar solution. All jets produce lobes… view at source ↗
Figure 6
Figure 6. Figure 6: Lobe properties of jets produced by the three codes with respect to time. Solid coloured lines represent the three codes and dashed grey lines the analytic self-similar model. First row: Aspect ratio of jet lobes (lobe length divided by width) with respect to time. swift lobes show minimal aspect ratio evolution with time, while arepo and pluto lobes are increasing their aspect ratio for the first ∼ 40 − 6… view at source ↗
Figure 7
Figure 7. Figure 7: Evolution of the different energy components (thermal energy in red, kinetic energy in blue and total energy, as the sum of the two, in black) in the lobes (dashed line) or ambient medium (solid line) of the jets simulated with the three codes. All jets transfer around 60% of their injected energy to the ambient medium, and all lobes contain more thermal energy than kinetic energy. regarding lobe masses an… view at source ↗
Figure 8
Figure 8. Figure 8: Temperature–density slices of jets simulated with the three codes in the uniform medium after 98 Myr, at three different resolutions (𝑁 = 107 , 𝑁 = 5 × 107 , 𝑁 = 108 ), shown in separate rows. swift lobes present only minor changes at different resolutions, while arepo and pluto lobes significantly increase and then decrease their length with increasing resolution. MNRAS 000, 1–25 (2025) [PITH_FULL_IMAGE:… view at source ↗
Figure 9
Figure 9. Figure 9: Lobe length as a function of time for all our runs. Coloured lines represent the three codes, line style shows the parameter variation, and the dashed grey line is the analytic prediction. Top row: Runs at three different resolutions. swift lobes show only minor variations, while arepo and pluto lobes become longer at intermediate resolution and shorter again at the highest resolution. Middle row: Runs wit… view at source ↗
Figure 10
Figure 10. Figure 10: Temperature-density slices of jets simulated with the three codes in the uniform medium after 68 Myr, with three different jet injection velocities (𝑣j = 2 × 104 km/s, 𝑣j = 4 × 104 km/s, 𝑣j = 8 × 104 km/s), shown in separate rows. Increasing jet injection velocity results in shorter, wider and hotter lobes with more obvious backflows. MNRAS 000, 1–25 (2025) [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Temperature-density slices of jets simulated with the three codes in the uniform medium after 68 Myr, with injection into the same neighbour mass. We test three different jet velocities, shown in separate rows, and include the lobe contours of the fiducial fixed-number-of-neighbours scheme. swift lobes are almost identical between the two schemes. arepo lobes present some deviations but no systemic differ… view at source ↗
Figure 12
Figure 12. Figure 12: Top: Temperature-density slices of swift, arepo and pluto jets in a stratified medium after 44 Myr. We include two resolution cases for pluto for a fairer comparison; SR denotes standard resolution and HR high resolution as described in the main text. All jet lobes appear longer and thinner compared to the uniform medium lobes and the analytic self-similar solution for the stratified medium. Bottom: Ambie… view at source ↗
Figure 13
Figure 13. Figure 13: Top: 3D Volume renderings of remnant lobes simulated with the three codes in the stratified medium, after 196 Myr (when jets turn off after 44 Myr). Middle: Temperature-density slices of jet remnants and their ambient medium. swift produces cylindrical bubbles while arepo long filamentary remnants. pluto SR presents a thin hollow remnant with internal mixing, while its HR run produces a more coherent stru… view at source ↗

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