Pith. sign in

REVIEW 4 major objections 5 minor 123 references

GATOS: Distinct Feedback Modes in AGN Central Regions Revealed by Spatially Resolved JWST Spectroscopy

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read JWST spectroscopy of five AGN shows radiative and kinetic feedback leave opposite, identifiable signatures in PAH molecules, and their coexistence explains both the observed bimodal PAH ratios and earlier conflicting results.

desk verdict Genuinely new JWST data and a plausible unified framework for PAH suppression in AGN, but the headline regression is partly circular and the qualitative case rests on the independent line-ratio probes. read the letter →

arxiv 2608.08369 v1 pith:AHAW7TDA submitted 2026-08-08 astro-ph.GA

Lulu Zhang , Chris Packham , Erin K. S. Hicks , Ismael García-Bernete , Almudena Alonso-Herrero , Ric I. Davies , Martin J. Ward , Daniel E. Delaney
show 20 more authors
Takuma Izumi Dimitra Rigopoulou Cristina Ramos Almeida Omaira González-Martín Rogemar A. Riffel Claudio Ricci Montserrat Villar-Martín Francoise Combes Miguel Pereira-Santaella Sebastian F. Hoenig Andrew J. Bunker Peter G. Boorman Enrica Bellocchi Nancy A. Levenson Santiago García-Burillo Fergus R. Donnan Anelise Audibert Lindsay Fuller Tanio Díaz-Santos David J. Rosario
This is my paper · ORCID
classification astro-ph.GA
keywords AGNfeedbackPAHemissionshockprocessingirradiationJWSTMIRI/MRSspectroscopymolecularhydrogenbimodalbandratiosstarformationratecalibration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Using JWST MIRI/MRS spectra of the central ~40–240 pc of five active galactic nuclei, this paper argues that the two classic AGN feedback modes—radiative (irradiation) and kinetic (shock processing)—leave opposite, identifiable fingerprints on polycyclic aromatic hydrocarbon (PAH) molecules. In the three LINERs, spaxels with strong shock indicators show elevated PAH 11.3/7.7 ratios, the signature of large, neutral PAHs; in the two Seyferts, strong irradiation indicators accompany low 11.3/7.7 and 6.2/7.7 ratios, the signature of large, ionized PAHs. The two modes coexist in the nuclear regions, with one generally dominating. The paper claims this coexistence explains both the bimodal PAH band-ratio distribution it maps and the conflicting literature on which PAH feature is a reliable star-formation tracer. If right, the physical state of PAHs becomes a practical diagnostic of which AGN feedback mode is at work, with direct consequences for calibrating star-formation rates in AGN hosts.

What carries the argument

The load-bearing tools are two spaxel-based diagnostic ratios—$r_{\rm H_2}={\rm H_2\,S(1\!-\!5)}/{\rm PAH\,7.7}$, adopted as a shock-processing indicator because shock heating creates excess H$_2$ emission relative to PAHs, and $r_{\rm Ne}=[{\rm Ne\,V}]14.32\,\mu{\rm m}/{\rm PAH\,11.3}$, adopted as an AGN-irradiation indicator because the coronal $[{\rm Ne\,V}]$ line tracks AGN luminosity—combined with the multiple linear regression $\log\Sigma_{\rm PAH}=\alpha_1\log\Sigma_{\rm SFR}+\alpha_2\log r_{\rm H_2}+\alpha_3\log r_{\rm Ne}+\beta$. These ratios are read against the PAH 6.2/7.7 versus 11.3/7.7 band-ratio diagram, where theoretical grids (neutral, 70% ionized, and fully ionized PAHs with carbon numbers $N_C=20$–$400$ at interstellar radiation fields up to $10^3$ times the local value) convert observed ratios into PAH size and ionization state. The explanatory mechanism is differential destruction: shocks selectively destroy small and ionized PAHs, while irradiation ionizes PAHs and preferentially destroys small grains.

What would settle it

A targeted test: in a sample of AGN with resolved radio jets and known ionization cones, the framework predicts 11.3/7.7 > 0.3 along the jet axis and < 0.3 inside the cone for the same galaxy; a single galaxy showing the opposite spatial association would falsify the scheme.

Watch

Extended reading notes

Core claim

The central claim is that PAH band ratios—specifically PAH 6.2/7.7 versus 11.3/7.7—separate cleanly into two tracks across the spaxels of five AGN. One track, driven by shock processing (traced by ${\rm H_2\,S(1\!-\!5)}/{\rm PAH\,7.7}$ and $[{\rm Fe\,II}]5.34\,\mu{\rm m}/[{\rm Ar\,II}]6.99\,\mu{\rm m}$), runs toward higher 11.3/7.7 and reflects a population of larger, neutral PAHs, because shocks preferentially destroy small and ionized PAHs. The other track, driven by AGN irradiation (traced by $[{\rm Ne\,V}]14.32\,\mu{\rm m}/{\rm PAH\,11.3}$ and $[{\rm Ne\,V}]/[{\rm Ne\,III}]$), runs toward lower 11.3/7.7 and 6.2/7.7 and reflects larger, more ionized PAHs, because extreme-UV and X-ray photons ionize and preferentially destroy smaller grains. A multiple linear regression including SFR surface density and both ratios reproduces the observed PAH surface brightness with 0.19–0.25 dex scatter, showing the suppression is jointly governed by the two modes. The paper further argues that the bimodal 11.3/7.7 distribution (demarcated at 0.3) and the apparently contradictory findings of earlier work are reconciled once sample selection is accounted for: shock-dominated nuclei behave like one earlier sample, irradiation-dominated nuclei like another.

Load-bearing premise

The case rests on the premise that the H2-to-PAH ratio mainly measures shock heating and the [NeV]-to-PAH ratio mainly measures AGN irradiation; the paper itself notes Circinus's H2 may be substantially excited by star formation, so if that contamination is widespread the two tracks and the bimodal interpretation weaken.

Editorial extensions

If this is right

  • PAH 7.7 µm emission corrected with the two-ratio formula becomes the tightest-correlated PAH-based SFR tracer in AGN, with 0.19 dex scatter in this sample.
  • Relative PAH suppression at a given SFR can be decomposed into an irradiation term and a shock term, so a PAH deficit no longer has an ambiguous physical origin.
  • Nuclei dominated by shocks (high 11.3/7.7, high $r_{\rm H_2}$) and nuclei dominated by irradiation (low ratios, high $r_{\rm Ne}$) follow opposite PAH–SFR behaviors, explaining why prior studies concluded different PAH features were the reliable SFR tracer.
  • Shock processing from jets or outflows is not confined to LINERs: all NGC 7314 spaxels with 11.3/7.7 > 0.4 lie above the shock-contribution threshold in $r_{\rm H_2}$, showing Seyferts also carry kinetic-mode imprints.
  • The framework lays the groundwork for a quantitative AGN feedback diagnostic based on PAH features, with future anchor points and a mixing-sequence PAH diagram planned from larger samples.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A magnitude-limited survey of AGN spaxels classified by $r_{\rm H_2}$ and $r_{\rm Ne}$ should show a visibly double-peaked distribution in 11.3/7.7; this can be tested with existing archival JWST data without new observations.
  • Because PAH destruction operates on roughly $10^3$–$10^5$ yr timescales (as the paper notes), combining PAH-state maps with instantaneous coronal-line strength could measure AGN duty cycles, an application the paper raises but does not develop.
  • The $r_{\rm H_2}$ shock indicator is vulnerable to star-formation contamination, as the Circinus case shows; a natural extension is to calibrate it spaxel-by-spaxel against the H$_2$ temperature-distribution slope $\beta$ and exclude spaxels where $\beta$ indicates photoelectric heating rather than shocks.
  • If the framework holds, corrected PAH 7.7 fluxes would give SFRs in AGN accurate to roughly 0.2 dex without optical Balmer extinction corrections, simplifying SFR estimates in large AGN surveys.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents JWST MIRI/MRS spatially resolved spectroscopy of the central regions of five AGN (three LINERs and two Seyferts) and analyzes PAH, H2, and neon/iron line diagnostics at ~4--24 pc scales. The central claim is that two AGN feedback modes, radiative (AGN irradiation) and kinetic (shock processing), jointly suppress PAH emission and that their relative importance explains the bimodal distribution of PAH band ratios in these galaxies and reconciles apparently conflicting results in the literature. The quantitative support is based on: (i) a multiple linear regression (Eq. 1) that reconstructs PAH surface brightness from SFR, rH2 = H2 S(1--5)/PAH7.7, and rNe = [NeV]/PAH11.3; (ii) spatially resolved maps and PAH ratio diagrams, with model grids for neutral and ionized PAHs; and (iii) independent ionized-line diagnostics, particularly [FeII]/[ArII] and [NeV]/[NeIII]. The paper also proposes a practical framework for calibrating SFRs from PAH emission in AGN and for diagnosing AGN feedback in JWST-era observations.

Significance. If the central claim holds, the paper provides a genuinely useful observational framework: PAH band ratios, when combined with shock and irradiation tracers, could encode the dominant feedback mode in AGN, with implications for SFR calibration and for subgrid prescriptions in galaxy-formation simulations. The manuscript has notable strengths: the reduction is careful, with PSF matching, spaxel-based MCMC decomposition, and aperture robustness checks; the sample spans a wide range in L_bol; and the independent ionized-line diagnostics in Figure 5 and Appendix A2 provide partially non-circular support for the qualitative picture. These strengths are, however, tempered by a load-bearing statistical problem: the main regression and the main color-coding variables are built from ratios that contain the same PAH fluxes that are being explained, so the tight correlations in Figure 2 and the apparent trends in Figure 4 are partly algebraic rather than physical.

major comments (4)
  1. [§3.1, Eq. (1), Table A1] The multiple linear regression is subject to direct mathematical coupling. For PAH7.7, the dependent variable is log Σ_PAH,7.7 and one predictor is log rH2 = log H2 − log PAH7.7; rearranging Eq. (1) puts (1+α2) log PAH7.7 on the left, and with α2 ≈ −0.86 this leaves only a residual coefficient of about 0.14. The regression can therefore produce a tight fit and a large negative α2 even if H2 and PAH7.7 are only weakly related physically. Similarly, for PAH11.3 the predictor rNe contains PAH11.3 in the denominator while PAH11.3 is on the left-hand side, so α3 ≈ −0.32 is partially built in. The RMS scatters in Figure 2 are consequently not a valid measure of how well the physical model reproduces PAH suppression. The same issue affects Eq. (2) and its stated predictive scatter. Please re-run the analysis with predictors that do not contain the dependent flux, for example SFR, H2 S(1--5), and [NeV] as separate terms, or H2/[NeII] and [NeV]/[NeII]; and provide a null test (e.g., shuffled PAH fluxes or simulated noise with the same covariance) to demonstrate that the improved fit is not reproduced by pure coupling.
  2. [Figure 4] The main color-coding in Figure 4 reproduces the concern. In panel (a) the color variable rH2 = H2/PAH7.7 shares the PAH7.7 denominator with both the x-axis (PAH6.2/PAH7.7) and the y-axis (PAH11.3/PAH7.7), so low-PAH7.7 spaxels simultaneously move to the upper right and receive high rH2 values; this can manufacture the apparent shock-processing sequence. In panel (b), rNe = [NeV]/PAH11.3 shares PAH11.3 with the y-axis numerator, so a spaxel with weak PAH11.3 appears both as a low PAH11.3/7.7 value and as a high-irradiation point. The independent proxies in Figure 5 do not share denominators with the PAH ratio axes and partially mitigate this problem, but the main quantitative demonstration in Figure 2 and the primary visual separation in Figure 4 remain compromised. Please make the independent-proxy version the primary figure, or demonstrate with a null simulation that the binned trends in Figure 4 are not produced by shared denominators.
  3. [§4.2 (Circinus); §3.1] The paper states that the rH2 values of most spaxels in Circinus are consistent with a significant contribution from star-formation activity to the excitation of H2 (Section 4.2). This directly undercuts the assumption, used throughout Section 3, that rH2 is a clean tracer of shock processing. Since Circinus is one of only two Seyferts in the sample and contributes to the irradiation-dominated arm, this confound affects the separation of the two modes in Figures 3 and 4. Please quantify the sensitivity of the results to this issue: exclude Circinus from the regression and from the median-bin trends, or model the star-formation contribution to H2 excitation, and show that the shock-processing arm is not an artifact of SF-heated H2 in (post-)starburst systems.
  4. [§3.1, Table A1; §3.2] The statistical analysis treats individual spaxels as independent data points, but the five galaxies occupy different regions of the parameter space and neighboring spaxels are spatially correlated. The quoted coefficient uncertainties and RMS scatters therefore overstate the effective sample size. In addition, the claim of a bimodal distribution of PAH 11.3/7.7 ratios is made on the basis of visual separation around a demarcation value of 0.3, without a formal two-component test. Please report leave-one-galaxy-out cross-validation or cluster-bootstrap errors for the regression coefficients, and apply a standard bimodality test (e.g., Hartigan's dip test) to the PAH ratio distribution before describing it as bimodal.
minor comments (5)
  1. [§3.1] There is a typo in the opening sentence: “ionzied gas phases” should be “ionized gas phases.”
  2. [§1] In the introduction, “UV-optical-infared emission lines” should read “UV-optical-infrared emission lines.”
  3. [§3.2] The phrase “exhibit the the same bimodal distribution” contains a duplicated article; please correct it.
  4. [Figure A1 caption] The axis label “log (H2 S(1 5)/PAH7.7)” is missing the en dash in S(1--5); please fix the formatting for consistency with the text.
  5. [§2.2] The description of the pipeline options (“master bg function”, “firstframe option”) would be clearer if the actual calibration pipeline parameter names were given in a monospaced style, since these are implementation details used by other JWST users.

Circularity Check

2 steps flagged · score 6.0 of 10

Quantitative support for the two-mode PAH-suppression claim is partially self-referential: Eq. (1) regresses PAH7.7 on H2/PAH7.7 and PAH11.3 on [NeV]/PAH11.3, so the tight correlations are partly algebraic, although Figure 5's independent line-ratio proxies keep the central bimodal framework from being entirely circular.

  1. self definitional [Sec. 3.1, Eq. (1), Table A1, Figure 2]
    "This analysis aims to reproduce the observed PAH surface brightness (in unit of erg s−1 pc−2) as a function of SFR surface density (in unit of M⊙ pc−2), the ratio of the summed H2 S(1)–S(5) emission to PAH 7.7µm feature (i.e., H2 S(1–5)/PAH7.7; hereafter rH2), and the ratio of [NeV]14.32µm emission to PAH 11.3µm feature (i.e., [NeV]14.32/PAH11.3; hereafter rNe). The multiple linear regression analysis yields log Σfit PAH = α1 log ΣSFR + α2 log rH2 + α3 log rNe + β, (1)"

    Equation (1) is fitted separately for each PAH feature, but for PAH 7.7 the regressor rH2 contains −log PAH7.7 while the dependent variable is log PAH7.7; for PAH 11.3, rNe contains −log PAH11.3. Any spaxel with weak PAH7.7 automatically has high rH2 at fixed H2, and any spaxel with weak PAH11.3 automatically has high rNe at fixed [NeV]. The large negative coefficients in Table A1 (α2 = −0.86 to −0.90; α3 = −0.19 to −0.32) are therefore partly algebraic, and the reported RMS scatters (0.19–0.25 dex) do not independently demonstrate that the two feedback modes 'drive' PAH suppression: the predictor is defined through the very flux being fit.

  2. self definitional [Sec. 3.2, Figure 4 caption]
    "PAH band ratio diagrams for 0.″2×0.″2 spaxels in the central ∼4″×4″ regions of the five AGN targets, color-coded by the strength indicators of (a) shock processing (rH2) and (b) AGN irradiation (rNe), respectively."

    Panel (a) colors by rH2 = H2/PAH7.7 while the two axes are PAH6.2/7.7 and PAH11.3/7.7; at fixed H2, a low measured PAH7.7 simultaneously raises both plotted ratios and the color variable, so the reddish trend toward high PAH11.3/7.7 is partly manufactured. Panel (b) colors by rNe = [NeV]/PAH11.3, sharing the PAH11.3 that appears in the y-axis numerator, so low PAH11.3 spaxels are forced toward low y and high rNe. This is the same mathematical coupling as Eq. (1) and directly affects the Figure 4 evidence for the bimodal trends. Figure 5, based on [FeII]/[ArII] and [NeV]/[NeIII], is not PAH-coupled and provides partially independent support.

full rationale

The paper's central quantitative step is Eq. (1), a multiple linear regression that 'reproduces' the observed PAH surface brightness using rH2 = H2 S(1–5)/PAH7.7 and rNe = [NeV]14.32/PAH11.3 as predictors. Because the PAH flux being fit appears in the denominator of the corresponding ratio, the tight correlations and the negative coefficients in Table A1 are partly built into the predictor definitions rather than measuring an independent causal effect. The same coupling appears in Figure 4's color-coding, so the apparent bimodal separation in that figure is not fully independent evidence for the two feedback modes. This is genuine partial circularity of the 'prediction reduces by construction' type, and it affects the abstract's causal claim that the two modes 'collectively drive the relative suppression of PAH emission.' However, the paper is not wholly circular. The PAH-SFR deficit in Figure 1 is compared against an external Shipley et al. (2016) calibration; Figure 5 repeats the bimodal-trend analysis with [FeII]/[ArII] and [NeV]/[NeIII] ratios that do not contain PAH fluxes; and the Rigopoulou et al. (2024) model grids are independent theoretical input. The heavy self-citations to Zhang et al. (2026, 2024b) supply interpretation, but the shock and irradiation identifications are corroborated by external diagnostics, so I do not treat those citations as independently load-bearing. The paper also acknowledges in Sec. 4.2 that rH2 in Circinus is consistent with a significant star-formation contribution to H2 excitation; that caveat weakens the shock-tracer assumption but is a robustness limitation rather than a circular step. On balance, the quantitative regression and Figure 4 color-coding are partially self-referential, while the central bimodal claim retains independent content from Figures 1 and 5; score 6 reflects that partial circularity rather than full equivalence of the derivation to its inputs.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. Its interpretive load is carried by the diagnostic ratios and external model grids, which are assumptions about how observed line and PAH emission map to physical conditions. The regression coefficients are free parameters fitted to the same data they are used to explain.

free parameters (3)
  • Regression coefficients alpha1, alpha2, alpha3, beta for Eq 1 and Eq 2 = e.g., PAH7.7: alpha1=0.88, alpha2=-0.86, alpha3=-0.19, beta=39.60 (Table A1)
    Fitted to the observed spaxel data; the low RMS scatter is an in-sample measure and the coefficients are not predicted independently.
  • Dust optical depth tau_9.7 = per spaxel, e.g., -0.60 to 0.09 in Table A3
    Fitted free parameter in the spectral decomposition used for extinction correction.
  • PAH 11.3/7.7 demarcation value = 0.3
    Chosen by hand to separate the bimodal distribution in Figure 4; not derived from data.
assumptions (5)
  • domain assumption PAH emission and neon line SFR prescription of Zhuang et al. (2019) give valid SFR surface densities in AGN
    Used in Section 3.1 to normalize PAH emission and derive SFR; assumes the AGN contribution to neon lines is correctly removed.
  • domain assumption rH2 = H2 S(1-5)/PAH7.7 traces shock processing
    Adopted in Section 3.1 based on prior work; excess H2 relative to PAH is assumed to come from shock heating, though star formation and X-ray heating can also contribute.
  • domain assumption rNe = [NeV]14.32/PAH11.3 traces AGN irradiation
    Coronal [NeV] line strength is assumed to scale with AGN luminosity and irradiation of PAHs; external correlations cited.
  • domain assumption PAH band ratio grids of Rigopoulou et al. (2024) correctly map PAH size and ionization fraction
    Used in Figures 4 and 5 to interpret the observed ratios as changes in PAH size and ionization.
  • domain assumption H2 power-law temperature distribution dN = m T^-beta dT with beta ~ 4.6 separating shock and irradiation regimes
    Section 4.1 uses this to argue shock-dominated regions have steeper temperature distributions; based on Togi & Smith (2016).

how reviews work

0 comments
Cite this review

Pith. "Pith review of GATOS: Distinct Feedback Modes in AGN Central Regions Revealed by Spatially Resolved JWST Spectroscopy." pith.science (2026). https://pith.science/paper/AHAW7TDA

@misc{pith2026260808369,
  author       = {Pith},
  title        = {Pith review of: GATOS: Distinct Feedback Modes in AGN Central Regions Revealed by Spatially Resolved JWST Spectroscopy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AHAW7TDA}},
  note         = {Machine review of arXiv:2608.08369}
}
abstract

This manuscript presents JWST MIRI/MRS observations of the central $r \approx 40-240$ pc regions of five active galactic nuclei (AGN) spanning a wide range of luminosities (${\rm log}\,L_{\rm bol}/{\rm erg\,s^{-1}} \approx 39.8-43.8$). Combining multiphase diagnostics from polycyclic aromatic hydrocarbons (PAHs), molecular hydrogen (H$_2$), and ionized gas at spatial scales of $\sim 4-24$ pc, this study presents a spatially resolved investigation into the effects of the two distinct AGN feedback modes--radiative and kinetic--on the surrounding medium. The results indicate that these two feedback modes, associated with AGN irradiation and shock processing, respectively, collectively drive the relative suppression of PAH emission in the nuclear regions of the targets studied here. Moreover, the coexistence of these two AGN feedback modes, especially the shock processing associated with either jets or outflows, in the central regions of AGN naturally explains both the bimodal distribution of PAH band ratios observed in the targets studied here and the seemingly disparate results reported in the literature. Although based on a limited sample, these findings provide new insights into calibrating star-formation rates (SFRs) from PAH emission in AGN, and more importantly, lay the groundwork for a practical framework to diagnose and quantify AGN feedback in the JWST era.

Figures

Figures reproduced from arXiv: 2608.08369 by the authors.

Figure 1
Figure 1. Correlations between the surface brightness of the PAH (a) 6.2 µm (b) 7.7 µm, and (c) 11.3 µm emission and the SFR surface density for 0. ′′2 × 0. ′′2 spaxels in the central ∼ 4 ′′ × 4 ′′ regions of the five AGN targets, where ΣPAH = LPAH cos(i)/A and ΣSFR = SFR cos(i)/A. Here, i is the galaxy inclination angle and A is the spaxel area in pc2 . The dashed lines represent the unity PAH-SFR relations calibrated by Shi… view at source ↗
Figure 2
Figure 2. Correlations between the surface brightness of the measured PAH (a) 6.2 µm, (b) 7.7 µm, and (c) 11.3 µm emission and those predicted by Equation 1 for data points in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Spatially resolved maps of PAH 6.2/7.7 ratio (top panels), PAH 11.3/7.7 ratio (middle panels), and PAH 11.3 µm emission (bottom panels) for the five AGN targets. Each column corresponds to one target, with the AGN position denoted by a black star. A common color scale is adopted for all panels within a given row and is shown on the right. The contours in the top, middle, and bottom panels show the distributions of l… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: PAH band ratio diagrams for 0. ′′2 × 0. ′′2 spaxels in the central ∼ 4 ′′ × 4 ′′ regions of the five AGN targets, color-coded by the strength indicators of (a) shock processing (rH2) and (b) AGN irradiation (rNe), respectively. The horizontal dashed lines mark the empi…
Figure 5
Figure 5. Figure 5: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Illustrative schematic showing the two regimes in our AGN targets affected by the two feedback modes associated with AGN irradiation and shock processing, respectively. Gray arrows in the lower right corner indicate the directions in theory of increasing overall PAH si…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

123 extracted references · 69 canonical work pages

  1. [1]

    Aitken, D. K. & Roche, P. F. 1985, MNRAS, 213, 777

    1985

  2. [2]

    Alexander, D. M. & Hickox, R. C. 2012, NewAR, 56, 4, 93

    2012

  3. [3]

    1996, A&A, 305, 616

    Allain, T., Leach, S., & Sedlmayr, E. 1996, A&A, 305, 616

    1996

  4. [4]

    G., Groves, B

    Allen, M. G., Groves, B. A., Dopita, M. A., et al. 2008, ApJS, 178, 1, 20

    2008

  5. [5]

    F., et al

    Alonso-Herrero, A., Garc´ıa-Burillo, S., H¨onig, S. F., et al. 2021, A&A, 652, A99 Alonso Herrero, A., Hermosa Mu˜noz, L., Labiano, A., et al. 2025, A&A, 699, A334

    2021

  6. [6]

    2020, A&A, 639, A43

    Alonso-Herrero, A., Pereira-Santaella, M., Rigopoulou, D., et al. 2020, A&A, 639, A43

    2020

  7. [7]

    2014, MNRAS, 443, 2766

    Alonso-Herrero, A., Ramos Almeida, C., Esquej, P., et al. 2014, MNRAS, 443, 2766

    2014

  8. [8]

    M., Gandhi, P., et al

    Annuar, A., Alexander, D. M., Gandhi, P., et al. 2025, MNRAS, 540, 4, 3827

    2025

Show all 123 references
  1. [9]

    R., et al

    Argyriou, I., Glasse, A., Law, D. R., et al. 2023, A&A, 675, A111

    2023

  2. [10]

    2023, A&A, 671, L12

    Audibert, A., Ramos Almeida, C., Garc´ıa-Burillo, S., et al. 2023, A&A, 671, L12

    2023

  3. [11]

    L., Salom´e, Q., et al

    Bellocchi, E., Longinotti, A. L., Salom´e, Q., et al. 2026, A&A, 709, A260

    2026

  4. [12]

    J., et al

    Bierschenk, M., Ricci, C., Temple, M. J., et al. 2024, ApJ, 976, 2, 257

    2024

  5. [13]

    2025, Zenodo, 1.18.0

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2025, Zenodo, 1.18.0. doi:10.5281/zenodo.15178003

    2025 doi

  6. [14]

    2025, A&A, 698, A86

    Chown, R., Okada, Y ., Peeters, E., et al. 2025, A&A, 698, A86

    2025

  7. [15]

    2014, A&A, 562, A21

    Cicone, C., Maiolino, R., Sturm, E., et al. 2014, A&A, 562, A21

    2014

  8. [16]

    A., Knapen, J

    Cisternas, M., Gadotti, D. A., Knapen, J. H., et al. 2013, ApJ, 776, 1, 50

    2013

  9. [17]

    2015, A&A, 578, A48

    Colina, L., Piqueras L´opez, J., Arribas, S., et al. 2015, A&A, 578, A48

    2015

  10. [18]

    2019, MNRAS, 482, 2, 1618

    Cortzen, I., Garrett, J., Magdis, G., et al. 2019, MNRAS, 482, 2, 1618

    2019

  11. [19]

    H., Colina, L., Riffel, R

    Costa-Souza, J. H., Colina, L., Riffel, R. A., et al. 2026, arXiv:2605.04925

    2026 arXiv

  12. [20]

    A., Schaye, J., Bower, R

    Crain, R. A., Schaye, J., Bower, R. G., et al. 2015, MNRAS, 450, 2, 1937 da Silva, P., Menezes, R. B., D´ıaz, Y ., et al. 2023, MNRAS, 519, 1, 1293

    2015

  13. [21]

    M., Paraschos, G

    Dasyra, K. M., Paraschos, G. F., Combes, F., et al. 2024, ApJ, 977, 2, 156 Dav´e, R., Crain, R. A., Stevens, A. R. H., et al. 2020, MNRAS, 497, 1, 146

    2024

  14. [22]

    I., Maciejewski, W., Hicks, E

    Davies, R. I., Maciejewski, W., Hicks, E. K. S., et al. 2014, ApJ, 792, 2, 101

    2014

  15. [23]

    2024, A&A, 689, A263

    Davies, R., Shimizu, T., Pereira-Santaella, M., et al. 2024, A&A, 689, A263

    2024

  16. [24]

    E., Hicks, E

    Delaney, D. E., Hicks, E. K. S., Zhang, L., et al. 2026, ApJ, 1002, 1, 20 Di Matteo, T., Springel, V ., & Hernquist, L. 2005, Nature, 433, 7026, 604

    2026

  17. [25]

    Diamond-Stanic, A. M. & Rieke, G. H. 2010, ApJ, 724, 140

    2010

  18. [26]

    L., Riffel, R

    Dors, O. L., Riffel, R. A., Cardaci, M. V ., et al. 2012, MNRAS, 422, 1, 252

    2012

  19. [27]

    R., Garc´ıa-Bernete, I., Rigopoulou, D., et al

    Donnan, F. R., Garc´ıa-Bernete, I., Rigopoulou, D., et al. 2024, MNRAS, 529, 2, 1386

    2024

  20. [28]

    R., Sandstrom, K., Shivaei, I., et al

    Donnan, F. R., Sandstrom, K., Shivaei, I., et al. 2026, MNRAS, in press, arXiv:2606.18244

    2026 arXiv

  21. [29]

    A., Ho, I.-T., Dressel, L

    Dopita, M. A., Ho, I.-T., Dressel, L. L., et al. 2015, ApJ, 801, 1, 42

    2015

  22. [30]

    Draine, B. T. & Li, A. 2007, ApJ, 657, 810

    2007

  23. [31]

    T., Li, A., Hensley, B

    Draine, B. T., Li, A., Hensley, B. S., et al. 2021, ApJ, 917, 3 16 ZHANG ET AL. Durr´e, M. & Mould, J. 2018, ApJ, 867, 2, 149

    2021

  24. [32]

    E., Strader, J., & Ho, L

    Greene, J. E., Strader, J., & Ho, L. C. 2020, ARA&A, 58, 257

    2020

  25. [33]

    2018, ApJ, 859, 2, 124

    Esparza-Arredondo, D., Gonz´alez-Mart´ın, O., Dultzin, D., et al. 2018, ApJ, 859, 2, 124

    2018

  26. [34]

    2024, A&A, 686, A46

    Esposito, F., Alonso-Herrero, A., Garc´ıa-Burillo, S., et al. 2024, A&A, 686, A46

    2024

  27. [35]

    2014, ApJ, 780, 1, 86

    Esquej, P., Alonso-Herrero, A., Gonz´alez-Mart´ın, O., et al. 2014, ApJ, 780, 1, 86

    2014

  28. [36]

    Fabian, A. C. 2012, ARA&A, 50, 455 Fern´andez-Ontiveros, J. A., Prieto, M. A., Acosta-Pulido, J. A., et al. 2012, Journal of Physics Conference Series, 372, 1, 012006

    2012

  29. [37]

    2017, A&A, 601, A143

    Fiore, F., Feruglio, C., Shankar, F., et al. 2017, A&A, 601, A143

    2017

  30. [38]

    2019, MNRAS, 483, 4, 4586

    Fluetsch, A., Maiolino, R., Carniani, S., et al. 2019, MNRAS, 483, 4, 4586

    2019

  31. [39]

    S., & Jarrett, T

    For, B.-Q., Koribalski, B. S., & Jarrett, T. H. 2012, MNRAS, 425, 3, 1934

    2012

  32. [40]

    Forbes, D. A. & Ward, M. J. 1993, ApJ, 416, 150

    1993

  33. [41]

    W., Lang, D., et al

    Foreman-Mackey, D., Hogg, D. W., Lang, D., et al. 2013, PASP, 125, 925, 306 Garc´ıa-Bernete, I., Pereira-Santaella, M., Gonz´alez-Alfonso, E., et al. 2026, Nature Astronomy, 10, 420 Garc´ıa-Bernete, I., Ramos Almeida, C., Acosta-Pulido, J. A., et al. 2015, MNRAS, 449, 2, 1309 ...

    2013

  34. [42]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Abbott, R., et al. 2023, PASP, 135, 1048, 068001

    2023

  35. [43]

    M., Mainieri, V ., et al

    Girdhar, A., Harrison, C. M., Mainieri, V ., et al. 2022, MNRAS, 512, 2, 1608

    2022

  36. [44]

    M., Tristram, K

    Goesaert, W. M., Tristram, K. R. W., Impellizzeri, C. M. V ., et al. 2025, A&A, 704, A125

    2025

  37. [45]

    2024, ApJ, 966, 2, 204

    Goold, K., Seth, A., Molina, M., et al. 2024, ApJ, 966, 2, 204

    2024

  38. [46]

    2026, ApJ, 1000, 2, 281

    Goold, K., Seth, A., Molina, M., et al. 2026, ApJ, 1000, 2, 281

    2026

  39. [47]

    2009, A&A, 502, 2, 515

    Guillard, P., Boulanger, F., Pineau Des Forˆets, G., et al. 2009, A&A, 502, 2, 515

    2009

  40. [48]

    M., Emonts, B

    Guillard, P., Ogle, P. M., Emonts, B. H. C., et al. 2012, ApJ, 747, 95

    2012

  41. [49]

    Harrison, C. M. & Ramos Almeida, C. 2024, Galaxies, 12, 2, 17

    2024

  42. [50]

    Heckman, T. M. & Best, P. N. 2014, ARA&A, 52, 589 Hermosa Mu˜noz, L., Gonz´alez Fern´andez, J. R., Alonso-Herrero, A., et al. 2026, A&A, 708, A297

    2014

  43. [51]

    Ho, L. C. 2008, ARA&A, 46, 475

    2008

  44. [52]

    Ho, L. C. 2009, ApJ, 699, 1, 626

    2009

  45. [53]

    C., Filippenko, A

    Ho, L. C., Filippenko, A. V ., & Sargent, W. L. W. 2003, ApJ, 583, 1, 159

    2003

  46. [54]

    C., Greene, J

    Ho, L. C., Greene, J. E., Filippenko, A. V ., et al. 2009, ApJS, 183, 1, 1

    2009

  47. [55]

    & McKee, C

    Hollenbach, D. & McKee, C. F. 1989, ApJ, 342, 306

    1989

  48. [56]

    Holm, A. I. S., Johansson, H. A. B., Cederquist, H., et al. 2011, JChPh, 134, 4, 044301

    2011

  49. [57]

    F., Hernquist, L., Cox, T

    Hopkins, P. F., Hernquist, L., Cox, T. J., et al. 2008, ApJS, 175, 2, 356

    2008

  50. [58]

    2011, ApJ, 736, 2, 129

    Hsieh, P.-Y ., Matsushita, S., Liu, G., et al. 2011, ApJ, 736, 2, 129

    2011

  51. [59]

    M., & Keel, W

    Hummel, E., van der Hulst, J. M., & Keel, W. C. 1987, A&A, 172, 32

    1987

  52. [60]

    2018, ApJ, 867, 1, 48

    Izumi, T., Wada, K., Fukushige, R., et al. 2018, ApJ, 867, 1, 48

    2018

  53. [61]

    J., H¨onig, S

    Jensen, J. J., H¨onig, S. F., Rakshit, S., et al. 2017, MNRAS, 470, 3, 3071

    2017

  54. [62]

    C., Armus, L., Bendo, G., et al

    Kennicutt, R. C., Armus, L., Bendo, G., et al. 2003, PASP, 115, 810, 928

    2003

  55. [63]

    E., Godard, B., Guillard, P., et al

    Kristensen, L. E., Godard, B., Guillard, P., et al. 2023, A&A, 675, A86

    2023

  56. [64]

    C., & Kim, H.-J

    Koo, B.-C., Raymond, J. C., & Kim, H.-J. 2016, Journal of Korean Astronomical Society, 49, 3, 109

    2016

  57. [65]

    Kormendy, J. & Ho, L. C. 2013, ARA&A, 51, 1, 511

    2013

  58. [66]

    S.-Y ., Armus, L., U, V ., et al

    Lai, T. S.-Y ., Armus, L., U, V ., et al. 2022, ApJL, 941, 2, L36

    2022

  59. [67]

    M., Heckman, T

    LaMassa, S. M., Heckman, T. M., Ptak, A., et al. 2012, ApJ, 758, 1, 1

    2012

  60. [68]

    Law, D. R., E. Morrison, J., Argyriou, I., et al. 2023, AJ, 166, 2, 45

    2023

  61. [69]

    K., Sandstrom, K., Rosolowsky, E., et al

    Leroy, A. K., Sandstrom, K., Rosolowsky, E., et al. 2023, ApJL, 944, 2, L9

    2023

  62. [70]

    M., D´ıaz Santos, T., Shivaei, I., et al

    Lofaro, C. M., D´ıaz Santos, T., Shivaei, I., et al. 2026, arXiv:2606.18230

    2026 arXiv

  63. [71]

    2020, Nature Astronomy, 4, 339 L´opez, I

    Li, A. 2020, Nature Astronomy, 4, 339 L´opez, I. E., Bertola, E., Reynaldi, V ., et al. 2025, A&A, 704, A88

    2020

  64. [72]

    2026, Nature Communications, 17, 1, 42

    Lopez-Rodriguez, E., Sanchez-Bermudez, J., Gonz´alez-Mart´ın, O., et al. 2026, Nature Communications, 17, 1, 42

    2026

  65. [73]

    2026, A&A, 709, A38

    Maragkoudakis, A., Boersma, C., Peeters, E., et al. 2026, A&A, 709, A38

    2026

  66. [74]

    2025, ApJ, 979, 1, 90

    Maragkoudakis, A., Boersma, C., Temi, P., et al. 2025, ApJ, 979, 1, 90

    2025

  67. [75]

    2018, MNRAS, 481, 5370

    Maragkoudakis, A., Ivkovich, N., Peeters, E., et al. 2018, MNRAS, 481, 5370

    2018

  68. [76]

    Marconi, A., Moorwood, A. F. M., Origlia, L., et al. 1994, The Messenger, 78, 20

    1994

  69. [77]

    A., Herter, T

    Marshall, J. A., Herter, T. L., Armus, L., et al. 2007, ApJ, 670, 1, 129

    2007

  70. [78]

    McNamara, B. R. & Nulsen, P. E. J. 2007, ARA&A, 45, 1, 117

    2007

  71. [79]

    & Prieto, M

    Mezcua, M. & Prieto, M. A. 2014, ApJ, 787, 1, 62 AGN FEEDBACKREVEALED BYJWST SPECTROSCOPY17

    2014

  72. [80]

    2000, ApJ, 528, 1, 186

    Mouri, H., Kawara, K., & Taniguchi, Y . 2000, ApJ, 528, 1, 186

    2000

  73. [81]

    M., Falcke, H., & Wilson, A

    Nagar, N. M., Falcke, H., & Wilson, A. S. 2005, A&A, 435, 2, 521

    2005

  74. [82]

    S., Storchi-Bergmann, T., & Eracleous, M

    Nemmen, R. S., Storchi-Bergmann, T., & Eracleous, M. 2014, MNRAS, 438, 4, 2804

    2014

  75. [83]

    Nesvadba, N. P. H., Drouart, G., De Breuck, C., et al. 2017, A&A, 600, A121 O’Dowd, M. J., Schiminovich, D., Johnson, B. D., et al. 2009, ApJ, 705, 885

    2017

  76. [84]

    2010, ApJ, 724, 2, 1193

    Ogle, P., Boulanger, F., Guillard, P., et al. 2010, ApJ, 724, 2, 1193

    2010

  77. [85]

    M., Sebastian, B., Aravindan, A., et al

    Ogle, P. M., Sebastian, B., Aravindan, A., et al. 2025, ApJ, 983, 2, 98

    2025

  78. [86]

    2026, arXiv:2603.23674

    Pantoni, L., Baes, M., Decin, L., et al. 2026, arXiv:2603.23674

    2026 arXiv

  79. [87]

    2002, A&A, 383, 1

    Pellegrini, S., Fabbiano, G., Fiore, F., et al. 2002, A&A, 383, 1

    2002

  80. [88]

    2018, MNRAS, 473, 3, 4077

    Pillepich, A., Springel, V ., Nelson, D., et al. 2018, MNRAS, 473, 3, 4077

    2018

  81. [89]

    M., Salyk, C., Banzatti, A., et al

    Pontoppidan, K. M., Salyk, C., Banzatti, A., et al. 2024, ApJ, 963, 2, 158 Ramos Almeida, C., Bischetti, M., Garc´ıa-Burillo, S., et al. 2022, A&A, 658, A155 Ramos Almeida, C., Esparza-Arredondo, D., Gonz´alez-Mart´ın, O., et al. 2023, A&A, 669, L5 Ramos Almeida, C., Garc´ıa-B...

    2024

  82. [90]

    A., Bianchin, M., Riffel, R., et al

    Riffel, R. A., Bianchin, M., Riffel, R., et al. 2021, MNRAS, 503, 4, 5161

    2021

  83. [91]

    A., Souza-Oliveira, G

    Riffel, R. A., Souza-Oliveira, G. L., Colina, L., et al. 2026b, arXiv:2605.02663

    arXiv

  84. [92]

    A., Zakamska, N

    Riffel, R. A., Zakamska, N. L., & Riffel, R. 2020, MNRAS, 491, 1, 1518

    2020

  85. [93]

    C., et al

    Rigopoulou, D., Barale, M., Clary, D. C., et al. 2021, MNRAS, 504, 5287

    2021

  86. [94]

    R., Garc´ıa-Bernete, I., et al

    Rigopoulou, D., Donnan, F. R., Garc´ıa-Bernete, I., et al. 2024, MNRAS, 532, 2, 1598

    2024

  87. [95]

    F., Packham, C., Telesco, C

    Roche, P. F., Packham, C., Telesco, C. M., et al. 2006, MNRAS, 367, 4, 1689 Rodr´ıguez-Ardila, A., Mason, R. E., Martins, L., et al. 2017, MNRAS, 465, 1, 906

    2006

  88. [96]

    J., et al

    Roussel, H., Helou, G., Hollenbach, D. J., et al. 2007, ApJ, 669, 959

    2007

  89. [97]

    2026, ApJ, 1002, 1, 57

    Roy, N., Heckman, T., Henry, A., et al. 2026, ApJ, 1002, 1, 57

    2026

  90. [98]

    Rupke, D. S. N. & Veilleux, S. 2011, ApJL, 729, 2, L27

    2011

  91. [99]

    A., Pastoriza, M

    Sales, D. A., Pastoriza, M. G., & Riffel, R. 2010, ApJ, 725, 605

    2010

  92. [100]

    2010, in Proc

    Seabold, S., & Perktold, J. 2010, in Proc. of the 9th Python 558 in Science Conf., ed. S. van der Walt & J. Millman (Austin, TX:

    2010

  93. [101]

    V ., Papovich, C., Rieke, G

    Shipley, H. V ., Papovich, C., Rieke, G. H., et al. 2016, ApJ, 818, 60

    2016

  94. [102]

    & Rees, M

    Silk, J. & Rees, M. J. 1998, A&A, 331, L1

    1998

  95. [103]

    Smith, J. D. T., Draine, B. T., Dale, D. A., et al. 2007, ApJ, 656, 2, 770

    2007

  96. [104]

    J., Riffel, R

    Storchi-Bergmann, T., McGregor, P. J., Riffel, R. A., et al. 2009, MNRAS, 394, 3, 1148

    2009

  97. [105]

    S., Minguez, P., Prieto, M

    Tabatabaei, F. S., Minguez, P., Prieto, M. A., et al. 2018, Nature Astronomy, 2, 83

    2018

  98. [106]

    J., et al

    Thean, A., Pedlar, A., Kukula, M. J., et al. 2000, MNRAS, 314, 3, 573

    2000

  99. [107]

    Tielens, A. G. G. M. 2008, ARA&A, 46, 289

    2008

  100. [108]

    & Smith, J

    Togi, A. & Smith, J. D. T. 2016, ApJ, 830, 18

    2016

  101. [109]

    D., et al

    Treyer, M., Schiminovich, D., Johnson, B. D., et al. 2010, ApJ, 719, 1191 Van De Putte, D., Peeters, E., Gordon, K. D., et al. 2025, A&A, 701, A111

    2010

  102. [110]

    2005, ARA&A, 43, 1, 769

    Veilleux, S., Cecil, G., & Bland-Hawthorn, J. 2005, ARA&A, 43, 1, 769

    2005

  103. [111]

    2021, A&A, 648, A17

    Venturi, G., Cresci, G., Marconi, A., et al. 2021, A&A, 648, A17

    2021

  104. [112]

    2023, A&A, 678, A127 V oit, G

    Venturi, G., Treister, E., Finlez, C., et al. 2023, A&A, 678, A127 V oit, G. M. 1992, MNRAS, 258, 841

    2023

  105. [113]

    A., Mel´endez, M., Mushotzky, R

    Weaver, K. A., Mel´endez, M., Mushotzky, R. F., et al. 2010, ApJ, 716, 2, 1151

    2010

  106. [114]

    2017, MNRAS, 465, 3, 3291

    Weinberger, R., Springel, V ., Hernquist, L., et al. 2017, MNRAS, 465, 3, 3291

    2017

  107. [115]

    2015, PASP, 127, 953, 646

    Wells, M., Pel, J.-W., Glasse, A., et al. 2015, PASP, 127, 953, 646

    2015

  108. [116]

    S., Rieke, G

    Wright, G. S., Rieke, G. H., Glasse, A., et al. 2023, PASP, 135, 1046, 048003

    2023

  109. [117]

    Xie, Y . & Ho, L. C. 2019, ApJ, 884, 136

    2019

  110. [118]

    Xie, Y . & Ho, L. C. 2022, ApJ, 925, 218

    2022

  111. [119]

    & Narayan, R

    Yuan, F. & Narayan, R. 2014, ARA&A, 52, 529 Yui Dan, K., Seebeck, J., Veilleux, S., et al. 2026, arXiv:2605.03016

    2014 arXiv

  112. [120]

    I., Packham, C., et al

    Zhang, L., Davies, R. I., Packham, C., et al. 2025, ApJS, 280, 2, 65

    2025

  113. [121]

    C., & Li, A

    Zhang, L., Ho, L. C., & Li, A. 2022, ApJ, 939, 22 18 ZHANG ET AL

    2022

  114. [122]

    Zhang, L., Packham, C., Hicks, E. K. S., et al. 2026, ApJL, 998, 2, L32

    2026

  115. [123]

    C., & Shangguan, J

    Zhuang, M.-Y ., Ho, L. C., & Shangguan, J. 2019, ApJ, 873, 2, 103 AGN FEEDBACKREVEALED BYJWST SPECTROSCOPY19 APPENDIX A.SUPPLEMENTARY FIGURES See Figures A1 and A2 for the diagnostic diagram of [NeV]14.32/PAH11.3 vs H2 S(1–5)/PAH7.7 and the diagnostic diagrams of: (a) [NeV]/[N...

    2025

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.