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arxiv: 2606.01297 · v1 · pith:PMHKRXVOnew · submitted 2026-05-31 · 🌌 astro-ph.GA

Inside-Out vs. Outside-In Quenching of MaNGA Galaxies: Dependence on Stellar Mass and Environment

Zi-Hua Ho , Lihwai Lin , Hung-Yu Jian , Bau-Ching Hsieh , Carlos L\'opez-Cob\'a , S. F. S\'anchez , Wen-Yen Wu , Shuai Feng
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Shiyin Shen
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Pith reviewed 2026-06-28 16:53 UTC · model grok-4.3

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keywords galaxy quenchinginside-out quenchingoutside-in quenchingstar formation diagnosticsstellar mass dependenceenvironmental effectsspatially resolved observationsquenched area fraction
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The pith

The choice of observational tracer for quenched regions changes whether galaxies are classified as quenching inside-out or outside-in.

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

This paper compares four common ways to identify regions where star formation has stopped or is stopping in galaxies and shows that each way leads to a different conclusion about the spatial pattern of quenching. A classification method that uses the fraction of the galaxy that is quenched and how concentrated that quenched part is reveals that specific star formation rate gives roughly equal numbers of inside-out and outside-in cases. In contrast, the strength of the 4000 angstrom break and low-ionization emission lines favor inside-out quenching, while post-starburst signatures appear in a different part of the classification space. The tendency for inside-out quenching grows stronger in more massive galaxies, whereas outside-in patterns appear more often in lower-mass satellite galaxies, and the link to the mass of the surrounding halo is weaker. These findings matter because they suggest each tracer captures quenching at different stages, so combining them builds a fuller picture of how star formation ends across a galaxy.

Core claim

Galaxies are classified into inside-out and outside-in quenching using the location on the plane defined by the fraction of quenched area and the concentration of that area. The specific star formation rate diagnostic yields comparable numbers of each mode. The 4000 angstrom break and low-ionization emission line diagnostics strongly favor inside-out patterns. Post-starburst regions occupy a distinct area on the plane. Across most diagnostics the inside-out fraction rises with stellar mass while outside-in is more common at low mass especially for satellites, with weaker and less consistent trends with halo mass.

What carries the argument

The non-parametric classification on the Fq-Cq plane, where Fq is the fraction of the quenched area and Cq is the concentration of the quenched area, used to distinguish inside-out from outside-in quenching modes.

Load-bearing premise

The classification using the fraction and concentration of quenched areas assigns the correct inside-out or outside-in mode for each tracer without being affected by other physical processes or selection effects.

What would settle it

Finding that the mass and environment trends in quenching mode are identical when using any of the four tracers in the same set of galaxies would indicate that the differences are not real.

Figures

Figures reproduced from arXiv: 2606.01297 by Bau-ching Hsieh, Carlos L\'opez-Cob\'a, Hung-Yu Jian, Lihwai Lin, S. F. S\'anchez, Shiyin Shen, Shuai Feng, Wen-Yen Wu, Zi-Hua Ho.

Figure 1
Figure 1. Figure 1: Distributions of sSFR (upper panel) and Dn4000 index (lower panel) for spaxels within 1.5 Re. In both panels, the x-axis represents the stellar mass surface density (Σ∗) of the spaxels. The color scale indicates the number of spaxels per bin. The white dashed lines indicate the threshold values adopted in our criteria to separate quenched and non-quenched spaxels. To ensure the robustness of the galaxy-lev… view at source ↗
Figure 2
Figure 2. Figure 2: Maps of three representative galaxies illustrating the spatial distribution of spaxels selected by different quenching criteria. The leftmost column shows the SDSS 3-colour composite images of the galaxies, with the MaNGA field of view indicated by the hexagon. The remaining columns display the corresponding spaxel maps classified using the sSFR, Dn4000, PSB, and LI(N)ER criteria, respectively. In each spa… view at source ↗
Figure 3
Figure 3. Figure 3: Spaxel-level overlap between four quenching diagnostics: sSFR, Dn4000, PSB, and LI(N)ER. Bars represent the total number of spaxels selected by each individual criterion as well as their inclusive intersections, where each combination includes all spaxels satisfying the corresponding set of criteria, regardless of whether additional criteria are also met. The matrix below indicates the corresponding combin… view at source ↗
Figure 4
Figure 4. Figure 4: Fq–Cq plane color-coded by the global log(sSFR/yr−1 ) of galaxies in the Full Sample (from left to right, top to bottom: sSFR, Dn4000, PSB, and LI(N)ER). The global sSFR values are taken from the Pipe3D DR17 catalog. The x-axis is Fq, the fraction of selected area in units of percentage, such that higher Fq corresponds to a larger selected area, while the y-axis is Cq, the concentration of the selected reg… view at source ↗
Figure 5
Figure 5. Figure 5: Fractions of galaxies classified into different spatial quenching modes under each criterion for the Full Sample. Blue bars represent inside-out quenching, red bars outside-in quenching, and dark red bars fully quenched galaxies. Galaxies with fewer than six quenched spaxels are labeled as “Unclassified” (gray). For the LI(N)ER criterion, inside-out quenching domi￾nates (12% ±0.3%), with a negligible outsi… view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of galaxies in the Fq–Cq plane for Subsample A, shown separately for the three quenching criteria: sSFR (left), Dn4000 (middle), and LI(N)ER (right). Subsample A consists of galaxies that simultaneously satisfy the sSFR, Dn4000, and LI(N)ER quenching criteria at the galaxy level (i.e., each contains at least six quenched spaxels under each diagnostic). The shaded regions indicate the inside-ou… view at source ↗
Figure 7
Figure 7. Figure 7: Galaxy fractions of spatial quenching classifications for Subsample A under the three quenching criteria. Each bar is nor￾malized by the total number of galaxies in Subsample A and shows the fractions classified as inside-out (blue), outside-in (pink), fully quenched (red). Across all three diagnostics, Subsample A is pre￾dominantly composed of inside-out quenched galaxies, with the outside-in fraction rem… view at source ↗
Figure 8
Figure 8. Figure 8: Fq–Cq plane color-coded by log(sSFR/yr−1 ) of Subsample B (from left to right, top to bottom: sSFR, Dn4000, PSB, and LI(N)ER). The x-axis represents Fq, the fraction of selected area, where a higher Fq indicates a larger area identified by the corresponding criterion. The y-axis represents Cq, the concentration of the selected area, with lower Cq indicating a more centrally concentrated spatial distributio… view at source ↗
Figure 9
Figure 9. Figure 9: Quenching mode fractions for galaxies that satisfy all cri￾teria. The y-axis indicates the fraction of galaxies exhibiting each quenching mode, normalized by the total number of overlapping galaxies (172 galaxies). Blue bars represent inside-out quenching, red bars correspond to outside-in quenching, and dark red bars are fully quenched galaxies. For the sSFR criterion, the inside-out and outside-in quench… view at source ↗
Figure 10
Figure 10. Figure 10: Fractions of galaxies classified as inside-out (top row) and outside-in (bottom row) as a function of halo mass for three quenching criteria (from left to right: sSFR, Dn4000, and LI(N)ER). Colored lines represent central galaxies log(M)cen > 10.5 (red) and satellite galaxies in different stellar-mass bins: 9 < log(M)sat ≤ 10 (yellow), 10 < log(M)sat ≤ 11 (green), and log(M)sat > 11 (blue). PSB features (… view at source ↗
read the original abstract

Galaxy quenching, the cessation of star formation, can proceed in spatially distinct ways, commonly described as inside-out or outside-in. However, the inferred quenching pattern depends strongly on how quenched or quenching regions are defined observationally. We utilize a sample of approximately 10,000 galaxies from the Mapping Nearby Galaxies at APO (MaNGA) DR17 survey to systematically compare four widely used diagnostics of star formation suppression: specific star formation rate (sSFR), the 4000 {\AA} break (Dn4000), post-starburst (PSB), and low-ionization (nuclear) emission-line region (LI(N)ER) emission, to examine how tracer choice influences the inferred spatial quenching pattern. Using the non-parametric method developed by Lin et al. (2019), we classify galaxies into inside-out and outside-in quenching modes based on the location on the plane of the fraction of the quenched area (Fq) and the concentration of quenched area (Cq). We find that the sSFR criterion yields comparable proportions of galaxies classified as inside-out and outside-in, while Dn4000 and LI(N)ER diagnostics strongly favor inside-out patterns. Because PSB traces a distinct transitional phase, PSB-selected spaxels occupy a different region of the Fq-Cq plane. Across most diagnostics, the fraction of galaxies classified as inside-out increases with stellar mass, while outside-in patterns are more common in lower-mass systems, especially among satellites. In contrast, the dependence of quenching mode on halo mass is weaker and less consistent across diagnostics. These differences show that the tracers probe complementary stages and timescales of star-formation suppression, and together provide a more complete view of spatially resolved quenching.

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

1 major / 1 minor

Summary. The manuscript analyzes ~10,000 MaNGA DR17 galaxies to compare four diagnostics of star-formation suppression (sSFR, Dn4000, PSB, LI(N)ER) for classifying inside-out versus outside-in quenching modes via the non-parametric Fq-Cq plane method of Lin et al. (2019). It reports that sSFR yields roughly equal inside-out and outside-in fractions, Dn4000 and LI(N)ER strongly favor inside-out, PSB occupies a distinct region of the plane, inside-out fractions increase with stellar mass across most diagnostics, and outside-in is more common in lower-mass satellites, while halo-mass dependence is weaker; the differences are interpreted as evidence that the tracers probe complementary stages and timescales of quenching.

Significance. If the Fq-Cq classifications prove robust across tracers, the work demonstrates that quenching-mode inferences are sensitive to the choice of diagnostic and that multi-tracer analyses can provide a more complete view of spatially resolved quenching, with potential implications for distinguishing environmental and mass-driven processes in galaxy evolution models.

major comments (1)
  1. [Abstract] The central claim that differences across the four diagnostics demonstrate complementary stages and timescales rests on the assumption that the Lin et al. (2019) Fq-Cq classification assigns quenching mode independently of tracer-specific physics. The abstract provides no indication that the method was re-validated or tested with mocks for sSFR (recent SF), Dn4000 (age), PSB (post-burst), and LI(N)ER (ionization), each of which weights different stellar-population or emission properties; without such checks, apparent differences in Fq-Cq occupancy could arise from how quenched spaxels are defined rather than distinct evolutionary stages.
minor comments (1)
  1. [Abstract] The abstract reports trends but omits quantitative fractions, uncertainties, or sample-selection details (e.g., mass range, redshift cuts, or spaxel S/N thresholds) that would allow immediate assessment of the reported proportions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on our manuscript. We address the concern about validation of the Fq-Cq method below and agree that the abstract would benefit from clarification on this point.

read point-by-point responses

  1. Referee: [Abstract] The central claim that differences across the four diagnostics demonstrate complementary stages and timescales rests on the assumption that the Lin et al. (2019) Fq-Cq classification assigns quenching mode independently of tracer-specific physics. The abstract provides no indication that the method was re-validated or tested with mocks for sSFR (recent SF), Dn4000 (age), PSB (post-burst), and LI(N)ER (ionization), each of which weights different stellar-population or emission properties; without such checks, apparent differences in Fq-Cq occupancy could arise from how quenched spaxels are defined rather than distinct evolutionary stages.

    Authors: We agree that the abstract does not explicitly note re-validation of the Fq-Cq method for each tracer. The Lin et al. (2019) classification is non-parametric and determines quenching mode solely from the spatial distribution metrics Fq and Cq once quenched spaxels have been identified; it does not depend on the underlying stellar-population or emission physics of the tracer. We applied the published method consistently, using each diagnostic's standard criteria to flag quenched regions. The resulting differences in Fq-Cq occupancy are therefore expected to reflect the distinct timescales each tracer is sensitive to. Nevertheless, to address the referee's concern we will revise the abstract to state that the method is applied as validated in Lin et al. (2019) and add a short discussion paragraph noting the lack of new mock tests while emphasizing that the observed mass and environment trends are robust across tracers. This constitutes a partial revision. revision: partial

Circularity Check

1 steps flagged

Minor self-citation of Fq-Cq classification method; central results independent of fitted inputs

specific steps
  1. self citation load bearing [Abstract]
    "Using the non-parametric method developed by Lin et al. (2019), we classify galaxies into inside-out and outside-in quenching modes based on the location on the plane of the fraction of the quenched area (Fq) and the concentration of quenched area (Cq)."

    The load-bearing classification step cites prior work by an overlapping author (Lihwai Lin). However, because the method itself is external to this manuscript and the paper reports new applications to different tracers on independent survey data, the citation does not reduce the observed differences to a definition or fit constructed within the present analysis.

full rationale

The paper applies the non-parametric Fq-Cq classification from Lin et al. (2019) to MaNGA DR17 data across four diagnostics and reports empirical differences in inside-out vs. outside-in fractions. This constitutes one minor self-citation (Lihwai Lin is a co-author), but the classification is a previously published method applied to public survey data rather than a fit or definition internal to this work. No equations reduce any reported fraction or trend to a parameter defined by the same dataset, and the central claim rests on observed differences rather than a self-referential loop. This matches the expected low-circularity outcome for papers that reuse established external methods.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract only; no free parameters, invented entities, or additional axioms are stated in the provided text.

axioms (1)
  • domain assumption The Fq-Cq plane from Lin et al. (2019) correctly separates inside-out from outside-in quenching for each diagnostic.
    Central classification step invoked throughout the abstract.

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discussion (0)

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