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arxiv: 2604.20056 · v1 · submitted 2026-04-21 · 🌌 astro-ph.GA

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MAUVE-MUSE: Ionization and Kinematic Signatures of Environmental Effects on Virgo Cluster Disks

Toby Brown , Luca Cortese , Barbara Catinella , A. Fraser-McKelvie , Adam B. Watts , Amirnezam Amiri , Alessandro Boselli , Woorak Choi
show 13 more authors
Aeree Chung Timothy A. Davis Eric Emsellem Pavel J\'achym Mar\'ia J. Jim\'enez-Donaire Tutku Kolcu Bumhyun Lee Andrei Ristea Jesse van de Sande Kristine Spekkens Sabine Thater Christine D. Wilson Nikki Zabel
Authors on Pith no claims yet

Pith reviewed 2026-05-10 01:17 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords Virgo Clusterenvironmental quenchingdiffuse ionized gasMUSE spectroscopyBPT diagramsgalaxy kinematicsstar formation quenchinginterstellar medium
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The pith

Environmental quenching in Virgo galaxies suppresses star formation and reveals diffuse ionized gas as the main emitter.

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

This paper uses MUSE integral-field spectroscopy to study the ionized gas in 12 Virgo Cluster disk galaxies as part of the MAUVE program. It compares these to field galaxies observed by PHANGS-MUSE and finds higher ionization line ratios and broader velocity dispersions in the cluster sample. The differences indicate that the cluster environment mainly stops star formation, leaving diffuse ionized gas to dominate the emission lines. This process explains the observed signals without requiring widespread direct excitation from the cluster itself. A small part of the gas shows kinematic signatures of shocks from intracluster interactions.

Core claim

The MAUVE-MUSE early sample shows systematically elevated line ratios with medians of [N II]/Hα = 0.75, [S II]/Hα = 0.57, and [O III]/Hβ = 1.04 compared to 0.50, 0.49, and 0.68 in field disks. Spatially resolved BPT diagrams indicate 74% of spaxels are ionized by sources other than H II regions, versus 61% in the field. Kinematic analysis reveals 44% of spaxels with Hα σ_LOS > 40 km/s, driven by non-star-forming gas, and a 5% tail at σ_LOS > 100 km/s. These results demonstrate that environmental quenching primarily suppresses star formation, unveiling DIG as the dominant ionized component, with the elevated ratios and kinematics reflecting the ISM state absent vigorous star formation rather,

What carries the argument

Spatially resolved BPT diagrams and Hα line-of-sight velocity dispersion maps from MUSE spectroscopy that separate ionization sources from kinematic states in galaxy disks.

If this is right

  • Quenching acts mainly by halting star formation, allowing DIG to dominate ionized gas emission in cluster disks.
  • Line ratio elevations and kinematic broadening serve as indicators of the post-quenching ISM state.
  • Shocks from intracluster medium interactions contribute a minor but detectable fraction of the emission.
  • High-resolution observations at 100 pc scales are needed to separate these effects in cluster environments.

Where Pith is reading between the lines

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

  • Similar observations in other galaxy clusters could test if this DIG dominance is a general feature of environmental quenching.
  • Models of galaxy evolution in dense environments should account for increased DIG contributions when star formation declines.
  • These signatures might be detectable in lower-resolution surveys to identify environmentally affected galaxies.
  • Extending the sample size could clarify the relative roles of shocks versus DIG in driving the offsets.

Load-bearing premise

The sample of 12 MAUVE-MUSE galaxies is directly comparable to the PHANGS-MUSE field sample without selection biases or analysis differences that could produce the reported offsets in line ratios and kinematics.

What would settle it

A larger sample of Virgo galaxies observed with the same MUSE methods showing no systematic elevation in median line ratios or velocity dispersions compared to field galaxies would falsify the conclusion that quenching unveils DIG as the dominant ionized component.

Figures

Figures reproduced from arXiv: 2604.20056 by Adam B. Watts, Aeree Chung, A. Fraser-McKelvie, Alessandro Boselli, Amirnezam Amiri, Andrei Ristea, Barbara Catinella, Bumhyun Lee, Christine D. Wilson, Eric Emsellem, Jesse van de Sande, Kristine Spekkens, Luca Cortese, Mar\'ia J. Jim\'enez-Donaire, Nikki Zabel, Pavel J\'achym, Sabine Thater, Timothy A. Davis, Toby Brown, Tutku Kolcu, Woorak Choi.

Figure 1
Figure 1. Figure 1: Voronoi-binned Hα maps for the MAUVE–MUSE early science sample. Galaxies are ordered by increasing stellar mass given in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Stellar mass vs. SFR for the MAUVE–MUSE early science sample (orange) and PHANGS–MUSE field control galaxies used in this work (blue). The gray density contours show density distribution of 15, 747 local galaxies from Leroy et al. (2019). gramme (MAUVE–MUSE, ID 110.244E), using MUSE in its Wide Field Mode. This configuration provides a 1 ′ × 1 ′ field of view, 0.2 ′′ spaxels, and a spectral sam￾pling of 1.… view at source ↗
Figure 3
Figure 3. Figure 3: Fraction of detected (≥ 5σ) spaxels greater than a given surface brightness for each emission line in the MAUVE–MUSE sample used in this study. We show the luminosity surface density on the top x-axis. Note that the [O iii]λ4958 and [N ii]λ6548 fluxes are tied to their corre￾sponding doublet line and are included for completeness. unresolved kinematic components. The velocity disper￾sions are corrected for… view at source ↗
Figure 4
Figure 4. Figure 4: Emission line maps for NGC 4501, an early-stage ram pressure stripped galaxy in the MAUVE–MUSE sam￾ple. Shown are the extinction-corrected fluxes of Hα, Hβ, [O iii]λλ4958,5006, [N ii]λλ6548,6583, and [S ii]λλ6716,6730, masked where flux/error < 3 and surface brightness < 5 × 10−20 erg s−1 cm−2 arcsec−2 . The thin white contour denotes 10−17 erg s−1 cm−2 arcsec−2 2. Fluxes have been corrected for dust atten… view at source ↗
Figure 5
Figure 5. Figure 5: Emission line maps for NGC 4522, a strongly stripped Virgo spiral with prominent extraplanar ionized gas. Panels are the same as [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Emission line ratio maps for NGC 4501. Panels (left to right, top to bottom) show the [N ii]/Hα, [S ii]/Hα, and [O iii]/Hβ, and Hα line widths along the line-of-sight (Hα σLOS). The full set of MAUVE–MUSE emission line ratio maps is provided in Appendix B. the median increases from 0.50 (PHANGS–MUSE) to 0.75 (MAUVE–MUSE), and the interquartile range (IQR = P75–P25) broadens from 0.30 to 0.76, with the 90% … view at source ↗
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of [N ii]/Hα, [S ii]/Hα, and [O iii]/Hβ for PHANGS–MUSE (blue) and MAUVE–MUSE (orange) spaxels. Top panels: box plots showing the weighted percentiles (P5, P25, Median, P75, P95) listed in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Spatially resolved BPT diagrams for the PHANGS–MUSE (top) and MAUVE–MUSE (bottom) samples. Left: [N ii]/Hα vs. [O iii]/Hβ. Right: [S ii]/Hα vs. [O iii]/Hβ. Colors indicate the orthogonal distance, dSF, in dex from the empirical Ka03 (dashed) and theoretical Ke01 (solid) star-formation boundaries, where positive values denote increasing departure from pure star-formation excitation toward non–H ii region re… view at source ↗
Figure 10
Figure 10. Figure 10: Distributions of orthogonal distance from the star-formation boundaries, dSF,NII (left; using Ka03 in [N ii] BPT) and dSF,SII (right; Ke01 [S ii] BPT), for the field (PHANGS–MUSE; top row) and cluster (MAUVE–MUSE; bottom row) samples. Spaxels are subdivided by Hα velocity dispersion into three bins (σLOS ≤ 40kms−1 , 40 ≤ σLOS < 80kms−1 , and σLOS ≥ 80kms−1 ). Black curves show kernel density estimates for… view at source ↗
Figure 11
Figure 11. Figure 11: CCDFs of Hα line-of-sight velocity dispersion (Hα σLOS) for the field (PHANGS–MUSE) and cluster (MAUVE– MUSE) galaxy samples. Left: all spaxels; middle and right panels: spaxels classified as star-forming and non-star-forming using the Ka03 boundary in the [N ii] BPT, respectively. Spaxels are weighted such that each galaxy contributes equally to the distribution [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
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read the original abstract

We present early science results from the MAUVE (Multiphase Astrophysics to Unveil the Virgo Environment) program which targets 40 Virgo Cluster galaxies to investigate the effect of environment on the interstellar medium (ISM) at ~100 pc scales. From 12 galaxies in the MAUVE-MUSE early sample, we find systematically elevated line ratios compared to PHANGS-MUSE field disks, with higher medians of [N II]/H$\alpha$ (0.75 vs. 0.50), [S II]/H$\alpha$ (0.57 vs. 0.49), and [O III]/H$\beta$ (1.04 vs. 0.68). Spatially resolved BPT diagrams show 74% of MAUVE-MUSE spaxels ionized by sources other than H II regions, versus 61% in the field, and we find these ionization differences to be closely coupled to broadened kinematics. 44% of MAUVE-MUSE spaxels exceed H$\alpha$ $\sigma_{LOS} = 40$ km/s (vs. 26% in the field), driven mainly by non-star-forming gas with $\sigma_{LOS}$ between 40 and 80 km/s, consistent with enhanced contribution of diffuse ionized gas (DIG). A subdominant tail of 5% of spaxels at $\sigma_{LOS} > 100$ km/s, largely absent in PHANGS-MUSE (1%), points to shocks or turbulent mixing layers from intracluster interactions. Our results show that environmental quenching primarily suppresses star formation, unveiling DIG as the dominant ionized component in cluster disks. The elevated line ratios and broadened kinematics observed in the MAUVE sample reflect the physical state of the ISM in the absence of vigorous star formation, rather than widespread direct environmental excitation. The observed shock-like emission provides an additional, secondary contribution likely driven by active interactions with the intracluster medium.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents early results from the MAUVE-MUSE program targeting Virgo cluster galaxies with MUSE integral-field spectroscopy. From an initial sample of 12 disk galaxies, it reports systematically higher median emission-line ratios ([N II]/Hα = 0.75 vs. 0.50, [S II]/Hα = 0.57 vs. 0.49, [O III]/Hβ = 1.04 vs. 0.68) relative to the PHANGS-MUSE field sample, a higher fraction of spaxels ionized by non-H II sources (74% vs. 61%), and a larger fraction of spaxels with Hα velocity dispersion σ_LOS > 40 km/s (44% vs. 26%). These differences are interpreted as arising primarily from environmental quenching that suppresses star formation and increases the relative contribution of diffuse ionized gas (DIG), with a secondary ~5% tail of high-σ_LOS (>100 km/s) spaxels attributed to shocks or turbulent mixing from intracluster medium interactions.

Significance. If the sample comparison holds, the work provides valuable ~100 pc-scale constraints on how cluster environments modify the ionized ISM, supporting the view that quenching acts mainly by reducing the H II region contribution rather than through widespread direct excitation. The reported coupling between elevated line ratios and broadened kinematics offers a useful observational diagnostic for DIG dominance in quenched disks. The use of a dedicated MUSE program on a well-studied cluster is a clear strength for future multi-phase studies.

major comments (2)
  1. [Abstract and sample description] The central claim that environmental effects are responsible for the reported offsets in line ratios, non-H II fraction (74% vs. 61%), and kinematics requires that the MAUVE-MUSE early sample of 12 galaxies is statistically comparable to the PHANGS-MUSE field sample. The manuscript provides no explicit matching or comparison of stellar mass, SFR, morphological type, or inclination distributions, nor a selection function for the 12 galaxies out of the 40 targeted. Without these controls, the differences could originate from intrinsic galaxy properties rather than environment.
  2. [BPT analysis and ionization diagnostics] The non-H II spaxel fractions and median line ratios depend on the precise BPT classification boundaries and S/N cuts applied to spaxels. It is unclear whether identical selection criteria and error propagation were used for both MAUVE-MUSE and PHANGS-MUSE datasets, which could affect the 74% vs. 61% comparison and the attribution to DIG.
minor comments (2)
  1. [Introduction and sample selection] Clarify in the methods or results section how the 12-galaxy early sample was chosen from the full 40-galaxy target list and whether any bias toward particular morphologies or star-formation rates was introduced.
  2. [Figures] Ensure that all figures comparing MAUVE and PHANGS data use identical color bars, binning, and axis ranges to facilitate direct visual assessment of the reported offsets.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and positive assessment of the work's significance. We address each of the major comments below.

read point-by-point responses

  1. Referee: [Abstract and sample description] The central claim that environmental effects are responsible for the reported offsets in line ratios, non-H II fraction (74% vs. 61%), and kinematics requires that the MAUVE-MUSE early sample of 12 galaxies is statistically comparable to the PHANGS-MUSE field sample. The manuscript provides no explicit matching or comparison of stellar mass, SFR, morphological type, or inclination distributions, nor a selection function for the 12 galaxies out of the 40 targeted. Without these controls, the differences could originate from intrinsic galaxy properties rather than environment.

    Authors: We agree that demonstrating sample comparability is essential for the central claim. In the revised manuscript we will add an explicit comparison of the stellar mass, SFR, morphological type, and inclination distributions between the MAUVE-MUSE early sample and the PHANGS-MUSE field sample. We will also describe the selection of the 12 galaxies (the first observed targets in the 40-galaxy program, chosen for disk morphology and observational accessibility) so that readers can evaluate whether the reported differences are attributable to environment. revision: yes

  2. Referee: [BPT analysis and ionization diagnostics] The non-H II spaxel fractions and median line ratios depend on the precise BPT classification boundaries and S/N cuts applied to spaxels. It is unclear whether identical selection criteria and error propagation were used for both MAUVE-MUSE and PHANGS-MUSE datasets, which could affect the 74% vs. 61% comparison and the attribution to DIG.

    Authors: We confirm that identical BPT boundaries and S/N thresholds were applied to both datasets, with the PHANGS-MUSE data re-analyzed using the same pipeline and error-propagation procedures as MAUVE-MUSE. We will expand the methods section to state these criteria explicitly and to detail the error handling for line ratios and spaxel classifications. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational comparison of measured line ratios and kinematics

full rationale

The paper reports direct measurements of emission-line ratios, BPT classifications, and Hα velocity dispersions from MUSE data on 12 Virgo galaxies, then compares medians and fractions to the PHANGS-MUSE field sample. No derivations, model fits, predictive equations, or self-referential definitions appear in the abstract or described results; the central claim follows immediately from the observed offsets without any step that reduces to a fitted parameter or prior self-citation by construction. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Claims rest on standard astrophysical interpretations of BPT diagrams and line-ratio diagnostics for distinguishing HII regions from DIG and shocks; no new free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption BPT line-ratio diagnostics reliably separate HII-region ionization from other sources such as DIG and shocks
    Invoked when stating 74% of spaxels are ionized by non-HII sources
  • domain assumption Line-of-sight velocity dispersion thresholds (40 km/s, 100 km/s) trace distinct physical components (DIG vs shocks)
    Used to interpret the 44% and 5% fractions

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discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE). XXI. Statistical properties of individual HII regions in perturbed galaxies

    astro-ph.GA 2026-05 unverdicted novelty 4.0

    HI-deficient perturbed galaxies show steeper faint-end slopes in HII region luminosity functions, brighter characteristic luminosities, and fewer HII regions per unit stellar mass and disk area than unperturbed system...

Reference graph

Works this paper leans on

79 extracted references · 77 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    Abramson, A., Kenney, J. D. P., Crowl, H. H., et al. 2011, AJ, 141, 164, doi: 10.1088/0004-6256/141/5/164

    work page doi:10.1088/0004-6256/141/5/164 2011

  2. [2]

    G., Groves, B

    Allen, M. G., Groves, B. A., Dopita, M. A., Sutherland, R. S., & Kewley, L. J. 2008, ApJS, 178, 20, doi: 10.1086/589652 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068

    work page doi:10.1086/589652 2008

  3. [3]

    2025, arXiv e-prints, arXiv:2512.10574, doi: 10.48550/arXiv.2512.10574

    Attwater, A., Catinella, B., Cortese, L., et al. 2025, arXiv e-prints, arXiv:2512.10574, doi: 10.48550/arXiv.2512.10574

    work page doi:10.48550/arxiv.2512.10574 2025

  4. [4]

    and Accardo, M

    Bacon, R., Accardo, M., Adjali, L., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 773508, doi: 10.1117/12.856027

    work page doi:10.1117/12.856027 2010

  5. [5]

    1981 , month = feb, journal =

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5, doi: 10.1086/130766

    work page doi:10.1086/130766 1981

  6. [6]

    2016, MNRAS, 461, 3111, doi: 10.1093/mnras/stw1234

    Belfiore, F., Maiolino, R., Maraston, C., et al. 2016, MNRAS, 461, 3111, doi: 10.1093/mnras/stw1234

    work page doi:10.1093/mnras/stw1234 2016

  7. [7]

    2022, A&A, 659, A26, doi: 10.1051/0004-6361/202141859

    Belfiore, F., Santoro, F., Groves, B., et al. 2022, A&A, 659, A26, doi: 10.1051/0004-6361/202141859

    work page doi:10.1051/0004-6361/202141859 2022

  8. [8]

    M., Beltr´ an, J

    Bertola, F., Corsini, E. M., Beltr´ an, J. C. V., et al. 1999, ApJL, 519, L127, doi: 10.1086/312111

    work page doi:10.1086/312111 1999

  9. [9]

    G., Stasi´ nska, G., & Bruzual, A

    Binette, L., Magris, C. G., Stasi´ nska, G., & Bruzual, A. G. 1994, A&A, 292, 13

    1994

  10. [10]

    and Cortese, L

    Boselli, A., Cortese, L., Boquien, M., et al. 2014, A&A, 564, A67, doi: 10.1051/0004-6361/201322313

    work page doi:10.1051/0004-6361/201322313 2014

  11. [11]

    2022 , month = dec, journal =

    Boselli, A., Fossati, M., & Sun, M. 2022, A&A Rv, 30, 3, doi: 10.1007/s00159-022-00140-3

    work page doi:10.1007/s00159-022-00140-3 2022

  12. [12]

    Environmental

    Boselli, A., & Gavazzi, G. 2006, PASP, 118, 517, doi: 10.1086/500691

    work page doi:10.1086/500691 2006

  13. [13]

    2020, astropy/photutils: 1.0.0 , 1.0.0, Zenodo, 10.5281/zenodo.4044744

    Bradley, L., Sip˝ ocz, B., Robitaille, T., et al. 2020, astropy/photutils: 1.0.0, 1.0.0, Zenodo, doi: 10.5281/zenodo.4044744

    work page doi:10.5281/zenodo.4044744 2020

  14. [14]

    2017, MNRAS, 466, 1275, doi: 10.1093/mnras/stw2991

    Brown, T., Catinella, B., Cortese, L., et al. 2017, MNRAS, 466, 1275, doi: 10.1093/mnras/stw2991

    work page doi:10.1093/mnras/stw2991 2017

  15. [15]

    D., Zabel, N., et al

    Brown, T., Wilson, C. D., Zabel, N., et al. 2021, ApJS, 257, 21, doi: 10.3847/1538-4365/ac28f5

    work page doi:10.3847/1538-4365/ac28f5 2021

  16. [16]

    Improving the Full Spectrum Fitting Method: Accurate Convolution with

    Cappellari, M. 2017, MNRAS, 466, 798, doi: 10.1093/mnras/stw3020 —. 2023, MNRAS, 526, 3273, doi: 10.1093/mnras/stad2597

    work page internal anchor Pith review doi:10.1093/mnras/stw3020 2017

  17. [17]

    and Copin, Y

    Cappellari, M., & Copin, Y. 2003, MNRAS, 342, 345, doi: 10.1046/j.1365-8711.2003.06541.x

    work page doi:10.1046/j.1365-8711.2003.06541.x 2003

  18. [18]

    Parametric

    Cappellari, M., & Emsellem, E. 2004, PASP, 116, 138, doi: 10.1086/381875

    work page doi:10.1086/381875 2004

  19. [19]

    and Clayton, Geoffrey C

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900

    work page doi:10.1086/167900 1989

  20. [20]

    2025, The Messenger, 195, 15, doi: 10.18727/0722-6691/5393

    Catinella, B., Cortese, L., Sun, J., et al. 2025, The Messenger, 195, 15, doi: 10.18727/0722-6691/5393

    work page doi:10.18727/0722-6691/5393 2025

  21. [21]

    H., Balkowski, C., & Kotanyi, C

    Cayatte, V., van Gorkom, J. H., Balkowski, C., & Kotanyi, C. 1990, AJ, 100, 604, doi: 10.1086/115545

    work page doi:10.1086/115545 1990

  22. [22]

    H., Kenney, J

    Chung, A., van Gorkom, J. H., Kenney, J. D. P., Crowl, H., & Vollmer, B. 2009, AJ, 138, 1741, doi: 10.1088/0004-6256/138/6/1741

    work page doi:10.1088/0004-6256/138/6/1741 2009

  23. [23]

    A., & Rand, R

    Collins, J. A., & Rand, R. J. 2001, ApJ, 551, 57, doi: 10.1086/320072

    work page doi:10.1086/320072 2001

  24. [24]

    and Catinella, B

    Cortese, L., Catinella, B., & Smith, R. 2021, PASA, 38, e035, doi: 10.1017/pasa.2021.18

    work page doi:10.1017/pasa.2021.18 2021

  25. [25]

    2012, A&A, 544, A101, doi: 10.1051/0004-6361/201219312

    Cortese, L., Boissier, S., Boselli, A., et al. 2012, A&A, 544, A101, doi: 10.1051/0004-6361/201219312

    work page doi:10.1051/0004-6361/201219312 2012

  26. [26]

    L., & Songaila, A

    Cowie, L. L., & Songaila, A. 1977, Nature, 266, 501, doi: 10.1038/266501a0 den Brok, M., Carollo, C. M., Erroz-Ferrer, S., et al. 2020, MNRAS, 491, 4089, doi: 10.1093/mnras/stz3184

    work page doi:10.1038/266501a0 1977

  27. [27]

    , keywords =

    Dressler, A. 1980, ApJ, 236, 351, doi: 10.1086/157753

    work page doi:10.1086/157753 1980

  28. [28]

    W., de Gasperin, F., Shimwell, T

    Edler, H. W., de Gasperin, F., Shimwell, T. W., et al. 2023, A&A, 676, A24, doi: 10.1051/0004-6361/202346458

    work page doi:10.1051/0004-6361/202346458 2023

  29. [29]

    and Congiu, Enrico and Groves, Brent and Ho, I

    Emsellem, E., Schinnerer, E., Santoro, F., et al. 2022, A&A, 659, A191, doi: 10.1051/0004-6361/202141727

    work page doi:10.1051/0004-6361/202141727 2022

  30. [30]

    M., den Brok, M., et al

    Erroz-Ferrer, S., Carollo, C. M., den Brok, M., et al. 2019, MNRAS, 484, 5009, doi: 10.1093/mnras/stz194

    work page doi:10.1093/mnras/stz194 2019

  31. [31]

    Hunter, D. A. 1996, AJ, 111, 2265, doi: 10.1086/117961

    work page doi:10.1086/117961 1996

  32. [32]

    , archivePrefix = "arXiv", eprint =

    Flores-Fajardo, N., Morisset, C., Stasi´ nska, G., & Binette, L. 2011, MNRAS, 415, 2182, doi: 10.1111/j.1365-2966.2011.18848.x

    work page doi:10.1111/j.1365-2966.2011.18848.x 2011

  33. [33]

    2016, MNRAS, 455, 2028, doi: 10.1093/mnras/stv2400

    Fossati, M., Fumagalli, M., Boselli, A., et al. 2016, MNRAS, 455, 2028, doi: 10.1093/mnras/stv2400

    work page doi:10.1093/mnras/stv2400 2016

  34. [34]

    T., Boselli, A., et al

    Fossati, M., Mendel, J. T., Boselli, A., et al. 2018, A&A, 614, A57, doi: 10.1051/0004-6361/201732373

    work page doi:10.1051/0004-6361/201732373 2018

  35. [35]

    A., et al

    Fraser-McKelvie, A., van de Sande, J., Gadotti, D. A., et al. 2024, arXiv e-prints, arXiv:2411.03430, doi: 10.48550/arXiv.2411.03430

    work page doi:10.48550/arxiv.2411.03430 2024

  36. [36]

    2025, nGIST: The new Galaxy Integral-field Spectroscopy Tool, Astrophysics Source Code Library, record ascl:2507.015

    Fraser-McKelvie, A., van de Sande, J., Brown, T., et al. 2025, nGIST: The new Galaxy Integral-field Spectroscopy Tool, Astrophysics Source Code Library, record ascl:2507.015

    2025

  37. [37]

    Giovanelli, R., & Haynes, M. P. 1983, AJ, 88, 881, doi: 10.1086/113376

    work page doi:10.1086/113376 1983

  38. [38]

    , year = 1972, month = aug, volume =

    Gunn, J. E., & Gott, III, J. R. 1972, ApJ, 176, 1, doi: 10.1086/151605

    work page doi:10.1086/151605 1972

  39. [39]

    P., & Giovanelli, R

    Haynes, M. P., & Giovanelli, R. 1986, ApJ, 306, 466, doi: 10.1086/164357

    work page doi:10.1086/164357 1986

  40. [40]

    P., Giovanelli, R., & Chincarini, G

    Haynes, M. P., Giovanelli, R., & Chincarini, G. L. 1984, ARA&A, 22, 445, doi: 10.1146/annurev.aa.22.090184.002305 MAUVE–MUSE: Ionization and Kinematic Signatures in Virgo43

    work page doi:10.1146/annurev.aa.22.090184.002305 1984

  41. [41]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007

  42. [42]

    Merloni, S

    Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055, doi: 10.1111/j.1365-2966.2003.07154.x

    work page doi:10.1111/j.1365-2966.2003.07154.x 2003

  43. [43]

    Kenney, J. D. P., & Koopmann, R. A. 1999, AJ, 117, 181, doi: 10.1086/300683

    work page doi:10.1086/300683 1999

  44. [44]

    J., Dopita, M

    Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121, doi: 10.1086/321545

    work page Pith review doi:10.1086/321545 2001

  45. [45]

    A., & Kenney, J

    Koopmann, R. A., & Kenney, J. D. P. 2004, ApJ, 613, 866, doi: 10.1086/423191

    work page doi:10.1086/423191 2004

  46. [46]

    A., Kenney, J

    Koopmann, R. A., Kenney, J. D. P., & Young, J. 2001, ApJS, 135, 125, doi: 10.1086/323532

    work page doi:10.1086/323532 2001

  47. [47]

    2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

    Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

    work page doi:10.1046/j.1365-8711.2001.04022.x 2001

  48. [48]

    2018, ApJL, 866, L10, doi: 10.3847/2041-8213/aae4d9

    Lee, B., & Chung, A. 2018, ApJL, 866, L10, doi: 10.3847/2041-8213/aae4d9

    work page doi:10.3847/2041-8213/aae4d9 2018

  49. [49]

    K., Sandstrom, K

    Leroy, A. K., Sandstrom, K. M., Lang, D., et al. 2019, ApJS, 244, 24, doi: 10.3847/1538-4365/ab3925

    work page doi:10.3847/1538-4365/ab3925 2019

  50. [50]

    2020, A&A, 644, A161, doi: 10.1051/0004-6361/202039020

    Longobardi, A., Boselli, A., Fossati, M., et al. 2020, A&A, 644, A161, doi: 10.1051/0004-6361/202039020

    work page doi:10.1051/0004-6361/202039020 2020

  51. [51]

    P., Cˆ ot´ e, P., et al

    Mei, S., Blakeslee, J. P., Cˆ ot´ e, P., et al. 2007, ApJ, 655, 144, doi: 10.1086/509598

    work page doi:10.1086/509598 2007

  52. [52]

    J., et al

    Molina, M., Eracleous, M., Barth, A. J., et al. 2018, ApJ, 864, 90, doi: 10.3847/1538-4357/aad5ed

    work page doi:10.3847/1538-4357/aad5ed 2018

  53. [53]

    1996, Nature, 379, 613, doi: 10.1038/379613a0

    Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996, Nature, 379, 613, doi: 10.1038/379613a0

    work page doi:10.1038/379613a0 1996

  54. [54]

    2016, A&A, 587, A108, doi: 10.1051/0004-6361/201527021

    Nehlig, F., Vollmer, B., & Braine, J. 2016, A&A, 587, A108, doi: 10.1051/0004-6361/201527021

    work page doi:10.1051/0004-6361/201527021 2016

  55. [55]

    2022, MNRAS, 511, 5180, doi: 10.1093/mnras/stac345

    Pedrini, A., Fossati, M., Gavazzi, G., et al. 2022, MNRAS, 511, 5180, doi: 10.1093/mnras/stac345

    work page doi:10.1093/mnras/stac345 2022

  56. [56]

    W., Reissl, S., Rahner, D., et al

    Pellegrini, E. W., Reissl, S., Rahner, D., et al. 2020, MNRAS, 498, 3193, doi: 10.1093/mnras/staa2555

    work page doi:10.1093/mnras/staa2555 2020

  57. [57]

    J., Kovaˇ c, K., et al

    Peng, Y.-j., Lilly, S. J., Kovaˇ c, K., et al. 2010, ApJ, 721, 193, doi: 10.1088/0004-637X/721/1/193

    work page doi:10.1088/0004-637x/721/1/193 2010

  58. [58]

    and Moretti, Alessia and Gullieuszik, Marco and Fritz, Jacopo and Jaff

    Poggianti, B. M., Moretti, A., Gullieuszik, M., et al. 2017, ApJ, 844, 48, doi: 10.3847/1538-4357/aa78ed

    work page doi:10.3847/1538-4357/aa78ed 2017

  59. [59]

    The Astronomical Journal , author =

    Price-Whelan, A. M., Sip˝ ocz, B. M., G¨ unther, H. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    work page doi:10.3847/1538-3881/aabc4f 2018

  60. [60]

    A., Kewley, L

    Rich, J. A., Kewley, L. J., & Dopita, M. A. 2011, ApJ, 734, 87, doi: 10.1088/0004-637X/734/2/87

    work page doi:10.1088/0004-637x/734/2/87 2011

  61. [61]

    2022, A&A, 658, A188, doi: 10.1051/0004-6361/202141907

    Santoro, F., Kreckel, K., Belfiore, F., et al. 2022, A&A, 658, A188, doi: 10.1051/0004-6361/202141907

    work page doi:10.1051/0004-6361/202141907 2022

  62. [62]

    2013, A&A, 558, A43, doi: 10.1051/0004-6361/201322062

    Singh, R., van de Ven, G., Jahnke, K., et al. 2013, A&A, 558, A43, doi: 10.1051/0004-6361/201322062

    work page doi:10.1051/0004-6361/201322062 2013

  63. [63]

    M., Manrique, A., Garc´ ıa-G´ omez, C., et al

    Solanes, J. M., Manrique, A., Garc´ ıa-G´ omez, C., et al. 2001, ApJ, 548, 97, doi: 10.1086/318672

    work page doi:10.1086/318672 2001

  64. [64]

    Tonnesen, S., & Bryan, G. L. 2012, MNRAS, 422, 1609, doi: 10.1111/j.1365-2966.2012.20737.x van Driel, W., & van Woerden, H. 1989, A&A, 225, 317

    work page doi:10.1111/j.1365-2966.2012.20737.x 2012

  65. [65]

    2010, Monthly Notices of the Royal Astronomical Society, 408, 1181, doi: 10.1111/j.1365-2966.2010.17197.x

    Vazdekis, A., S´ anchez-Bl´ azquez, P., Falc´ on-Barroso, J., et al. 2010, MNRAS, 404, 1639, doi: 10.1111/j.1365-2966.2010.16407.x

    work page doi:10.1111/j.1365-2966.2010.16407.x 2010

  66. [66]

    Veilleux, S., & Osterbrock, D. E. 1987, ApJS, 63, 295, doi: 10.1086/191166

    work page doi:10.1086/191166 1987

  67. [67]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    work page doi:10.1038/s41592-019-0686-2 2020

  68. [68]

    2008a, A&A, 491, 455, doi: 10.1051/0004-6361:200810432

    Vollmer, B., Braine, J., Pappalardo, C., & Hily-Blant, P. 2008a, A&A, 491, 455, doi: 10.1051/0004-6361:200810432

    work page doi:10.1051/0004-6361:200810432

  69. [69]

    2013, A&A, 553, A116, doi: 10.1051/0004-6361/201321163

    Vollmer, B., Soida, M., Beck, R., et al. 2013, A&A, 553, A116, doi: 10.1051/0004-6361/201321163

    work page doi:10.1051/0004-6361/201321163 2013

  70. [70]

    2008b, A&A, 483, 89, doi: 10.1051/0004-6361:20078139

    Vollmer, B., Soida, M., Chung, A., et al. 2008b, A&A, 483, 89, doi: 10.1051/0004-6361:20078139

    work page doi:10.1051/0004-6361:20078139

  71. [71]

    2021, A&A, 645, A121, doi: 10.1051/0004-6361/202038507

    Vollmer, B., Fossati, M., Boselli, A., et al. 2021, A&A, 645, A121, doi: 10.1051/0004-6361/202038507

    work page doi:10.1051/0004-6361/202038507 2021

  72. [72]

    Waskom, M. L. 2021, Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

    work page doi:10.21105/joss.03021 2021

  73. [73]

    B., et al., 2024, @doi [ ] 10.1093/mnras/stae898 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.1968W 530, 1968

    Watts, A. B., Cortese, L., Catinella, B., et al. 2024, MNRAS, 530, 1968, doi: 10.1093/mnras/stae898

    work page doi:10.1093/mnras/stae898 2024

  74. [74]

    and Palsa, Ralf and Streicher, Ole and Bacon, Roland and Urrutia, Tanya and Wisotzki, Lutz and Conseil, Simon and Husemann, Bernd and Jarno, Aur

    Weilbacher, P. M., Palsa, R., Streicher, O., et al. 2020, A&A, 641, A28, doi: 10.1051/0004-6361/202037855 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. St´ efan van der Walt & Jarrod Millman, 56 – 61, doi: 10.25080/Majora-92bf1922-00a

    work page doi:10.1051/0004-6361/202037855 2020

  75. [75]

    and Cappellari, Michele and Bershady, Matthew A

    Westfall, K. B., Cappellari, M., Bershady, M. A., et al. 2019, AJ, 158, 231, doi: 10.3847/1538-3881/ab44a2

    work page doi:10.3847/1538-3881/ab44a2 2019

  76. [76]

    Yoon, H., Chung, A., Smith, R., & Jaff´ e, Y. L. 2017, ApJ, 838, 81, doi: 10.3847/1538-4357/aa6579

    work page doi:10.3847/1538-4357/aa6579 2017

  77. [77]

    2012, ApJ, 749, 43, doi: 10.1088/0004-637X/749/1/43

    Yoshida, M., Yagi, M., Komiyama, Y., et al. 2012, ApJ, 749, 43, doi: 10.1088/0004-637X/749/1/43

    work page doi:10.1088/0004-637x/749/1/43 2012

  78. [78]

    D., et al

    Zabel, N., Brown, T., Wilson, C. D., et al. 2022, ApJ, 933, 10, doi: 10.3847/1538-4357/ac6e68

    work page doi:10.3847/1538-4357/ac6e68 2022

  79. [79]

    2017, MNRAS, 466, 3217, doi: 10.1093/mnras/stw3308

    Zhang, K., Yan, R., Bundy, K., et al. 2017, MNRAS, 466, 3217, doi: 10.1093/mnras/stw3308

    work page doi:10.1093/mnras/stw3308 2017