Pith. sign in

REVIEW 3 major objections 5 minor 58 references

Early-type Galaxies on the Star Formation Main Sequence: Internal Star Formation Geometry Revealed with MaNGA and Their Environmental Origin

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read MS-early galaxies split into two populations differing in internal star-formation geometry and environment, implying two formation pathways.

desk verdict A solid observational split of MS early-types into two populations, with a strong environmental signal, but the classification's robustness to AGN masking needs to be shown before I'd fully buy the second pathway. read the letter →

arxiv 2507.16212 v1 pith:VOVMO37L submitted 2025-07-22 astro-ph.GA

Shuhei Koyama , Yusei Koyama , Takuji Yamashita , Masahiro Konishi , Kosuke Kushibiki , Kentaro Motohara This is my paper · ORCID
classification astro-ph.GA
keywords starformationmainsequenceearly-typegalaxiesintegralfieldspectroscopyMaNGASersicprofilegalaxyenvironmentevolutionresolved
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

The paper argues that galaxies on the star-formation main sequence are not a uniform population, even when they have the same total stellar mass and star-formation rate. Among early-type galaxies on the main sequence, it identifies two distinct subgroups: one with centrally concentrated star formation and no dominant stellar bulge that preferentially lives in dense, group-scale environments, and one with a prominent bulge, suppressed central star formation, and disk-like star formation that lives in environments like those of ordinary late-type galaxies. The proposed interpretation is that the first group is being shaped by environment-driven processes that push gas inward, while the second grows its center through internal secular evolution. If this is right, the global main-sequence position hides at least two formation channels, and spatially resolved maps are required to uncover them.

What carries the argument

The dividing tool is a comparison of two Sersic indices measured on the same galaxy: $n_{\rm SFR}$, from the profile of star-formation-rate surface density, and $n_*$, from the profile of stellar-mass surface density. The equality line $n_{\rm SFR} = n_*$ separates galaxies whose star formation is more centrally concentrated than their stars (MS-early_SF) from those whose stars are more concentrated than their star formation (MS-early_stellar). This structural classification is then checked against radial sSFR profiles, resolved main-sequence slopes, halo-mass offsets, and local density estimates to give the two populations their distinct physical interpretations.

What would settle it

Re-observe the 97 MS-early galaxies with an integral-field instrument whose point spread function resolves the central kiloparsec, and refit $n_{\rm SFR}$ while modeling AGN and composite spaxels instead of masking them. If a large share of the 63 MS-early_SF galaxies no longer show $n_{\rm SFR}>n_*$, or if their roughly 0.6 dex halo-mass and local-density offsets disappear once they are reclassified, the two-pathway interpretation fails.

Watch

Extended reading notes

Core claim

Using MaNGA's final data release, the authors classify 97 early-type galaxies that lie on the star-formation main sequence and fit PSF-convolved Sersic profiles to their spatially resolved star-formation-rate surface density ($\Sigma_{\rm SFR}$) and stellar-mass surface density ($\Sigma_*$) maps. They find that 63 of these galaxies have star formation more centrally concentrated than their stellar mass ($n_{\rm SFR} > n_*$); these 'MS-early_SF' galaxies show steep sSFR gradients, a steep resolved main-sequence slope of $1.42 \pm 0.03$, and halos about 0.6 dex more massive than expected at fixed stellar mass. The remaining 34 have the opposite pattern ($n_{\rm SFR} < n_*$), with bulges, suppressed central star formation, and star-formation profiles similar to late-type MS galaxies, plus environments statistically indistinguishable from late-type galaxies. The paper concludes that these are two different populations with two different origins: environmentally triggered central starbursts versus secular bulge growth in otherwise ordinary star-forming disks.

Load-bearing premise

The two-way split rests on the fitted Sersic indices: every early-type main-sequence galaxy must land on the correct side of the $n_{\rm SFR}=n_*$ line, even though the paper itself reports that some central star-forming regions are more compact than the MaNGA PSF and that AGN/composite masking can bias $n_{\rm SFR}$ downward.

Editorial extensions

If this is right

  • MS-early_SF galaxies sit about 0.6 dex above the stellar-mass-to-halo-mass relation and have local densities about 0.6 dex higher than MS-late galaxies, placing them in pair- or group-scale environments rather than clusters.
  • Group-scale environments can drive gas toward galaxy centers and trigger localized starburst-like star formation without pushing the whole galaxy off the main sequence.
  • MS-early_stellar galaxies are likely a later stage of ordinary disk evolution, representing the most bulge-dominated subset of the star-forming population rather than a separate formation channel.
  • Global stellar-mass and star-formation-rate measurements alone cannot identify evolutionary pathways; spatially resolved measurements are needed to separate at least two populations with similar global properties.
  • Resolved main-sequence slopes separate the two modes: roughly 1.4 for centrally concentrated starbursts versus roughly 0.8-0.9 for disk-like star formation.

Reading between the lines

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

  • Beyond the paper, the small number of MS-early_SF galaxies among about 1,700 main-sequence galaxies implies that this is a short-lived phase; comparing its observed abundance with simulated lifetimes of interaction-triggered central starbursts would test the proposed duty cycle.
  • Beyond the paper, a direct test follows from the proposed snapshot interpretation: MS-early_SF centers should contain very young stellar populations right now, so measuring light-weighted stellar ages in the central kiloparsec from the same MaNGA spectra could confirm or rule out a recent starburst.
  • Beyond the paper, applying the same $n_{\rm SFR}$ versus $n_*$ division to compact star-forming galaxies at higher redshift would test whether the dense-environment channel seen here is the same phenomenon operating earlier in cosmic time.
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

3 major / 5 minor

Summary. This paper uses spatially resolved MaNGA DR17 data to study galaxies on the star-forming main sequence (MS), focusing on the 97 galaxies classified as early-type by T-type and P_LTG. The authors fit PSF-convolved Sersic profiles to Sigma_SFR and Sigma_* maps and split the MS-early galaxies into 'MS-early_SF' (nSFR > nstar; 63 galaxies) and 'MS-early_stellar' (nstar > nSFR; 34 galaxies). They report that MS-early_SF galaxies have centrally enhanced sSFR profiles and steep resolved MS slopes, while MS-early_stellar galaxies resemble MS-late galaxies in profile shape. Using the Tinker group catalog and GEMA-VAC local densities, they find that MS-early_SF galaxies have significantly higher halo mass offsets at fixed stellar mass (KS p = 2.4e-19 relative to MS-late) and higher local densities (p = 0.002), whereas MS-early_stellar galaxies are environmentally similar to MS-late galaxies. The authors interpret the two subgroups as distinct evolutionary pathways: environmentally driven central star formation versus internal secular bulge growth.

Significance. If correct, the paper offers a useful demonstration that galaxies with similar global M* and SFR can have qualitatively different internal star formation geometries and environments, and that environment may differentiate these states. The analysis makes good use of public MaNGA DR17 products, uses a Bayesian MCMC fitting procedure with PSF convolution, and gives quantitative KS tests for the environmental comparisons; the halo mass offset result is statistically very strong. The AGN-exclusion appendix is a sensible robustness check for the profile comparisons. The central limitation is that the subgroup definition itself uses nSFR and nstar, so the later finding that the subgroups differ in nSFR-based geometry is partly a consistency check rather than an independent discovery; the genuinely independent evidence is environmental. That evidence needs to be protected from the AGN-masking bias that the authors acknowledge in Section 3.1, and the present appendix does not do so.

major comments (3)
  1. [Section 3.1 and Appendix A] The classification boundary nSFR = nstar is vulnerable to the AGN/composite masking bias, and the appendix does not test the environmental result. The paper states in Section 3.1 that AGN host galaxies tend to have lower nSFR values because central star-forming spaxels are removed, and Section 4.1 reports that roughly half of MS-early_stellar galaxies are AGN hosts. Since the mask removes central spaxels, this bias acts in exactly the direction that would move genuinely concentrated sources into MS-early_stellar. Appendix A re-computes only the sSFR radial profiles and the resolved MS for non-AGN galaxies; it does not re-derive the nSFR-nstar split or re-run the environment tests in Figures 12 and 13. I ask the authors to quantify the contamination: for example, reclassify the MS-early sample using only galaxies without central AGN/composite holes, or fill the masked regions with a PSF-based extrapolation, and then recompute the halo mass offset and local density comparisons. If even a small number of the 34 MS-early_stellar galaxies are misclassified, the claimed dichotomy and the environmental contrast could be distorted in either direction.
  2. [Sections 3.2 and 3.3] The differences in normalized sSFR radial profiles and resolved MS slopes between the two MS-early subgroups are partly constructed by the classification rather than independent confirmations. Because MS-early_SF is defined by nSFR > nstar, it is expected that their central sSFR is enhanced and their resolved MS slope is steeper than for MS-early_stellar. The paper should present these profile comparisons explicitly as characterizations that follow from the definition, and should rest the physical claim of two distinct populations primarily on the independent environmental measurements and on the stellar mass profile differences, rather than presenting the sSFR and resolved MS differences as separate discoveries.
  3. [Section 3.1] The treatment of unconstrained nSFR values (nSFR approaching 6) needs error awareness in the classification. For objects whose central star formation is more compact than the MaNGA PSF, the paper states that the exact nSFR values are difficult to constrain, and these objects are nevertheless placed in MS-early_SF. Figure 7 shows error bars, but the paper does not state how many MS-early galaxies lie within 1-2 sigma of the nSFR = nstar line. A simple uncertainty-aware robustness test, such as excluding objects whose nSFR and nstar are consistent within the 95% confidence interval and repeating the environment tests, would substantially strengthen the central claim.
minor comments (5)
  1. [Section 5] The summary lists 1587 MS-late galaxies, while Section 2.4 states 1583; please correct this inconsistency.
  2. [Section 3.1] The paper should state explicitly how many MS-early galaxies have nSFR pegged near the unconstrained value of 6, and should confirm that their nstar values are significantly lower, since these objects carry the strongest central concentration signal.
  3. [Section 3.3] The resolved MS slope comparison uses all spaxels pooled across galaxies, so the quoted formal uncertainties (e.g., 0.771 +/- 0.002 for MS-late) likely underestimate galaxy-to-galaxy variance; a per-galaxy slope distribution or a justification of pooling would be preferable.
  4. [Section 4.2] The KS p-values for the environment comparisons are quoted without discussion of multiple testing; since two diagnostics and two subgroups are considered, a brief statement about the adopted significance level would be helpful.
  5. [Section 2.2] The sample selection requires NSA ELLPETRO_BA > 0.5, which effectively excludes edge-on galaxies; the possible effect of this inclination cut on the morphological classification and on the measured Sersic indices should be mentioned.

Circularity Check

2 steps flagged · score 4.0 of 10

Internal-geometry subgroup findings restate the classification; environmental-origin result is independently grounded, so circularity is partial.

  1. self definitional [Section 3.1 (classification) and Section 5 (Summary item 1)]
    "Using the line where n∗ and nSFR are equal as a reference (broken line in Figure 7), we define the MS-early galaxies with n∗ > nSFR as the “MS-early stellar”, and those with n∗ < nSFR as the “MS-early SF”. ... The ”MS-early SF” galaxies show higher S´ ersic indices in ΣSFR than Σ∗, indicating centrally concentrated star formation."

    The subgroup label is assigned by the sign of nSFR − n∗. The summary’s first finding, that MS-early SF galaxies have higher ΣSFR than Σ∗ Sérsic indices, is the same inequality used to define the subgroup. It is a restatement of the classification criterion, not an independent empirical result. This definitional input is then used as the basis for the subsequent environment comparison.

  2. self definitional [Sections 3.2 and 3.3 (sSFR radial profiles and resolved main sequence)]
    "In contrast, MS-early SF galaxies show the opposite trend, with significantly enhanced sSFR in their center that rapidly decreases towards the outer region. This steep gradient is consistent with their high nSFR found in Section 3.1. ... the slope of the distribution appears steep. Orthogonal fitting reveals that the slope is 1.42 ± 0.03."

    nSFR and n∗ are Sérsic indices fit to the same ΣSFR and Σ∗ radial profiles, and the sSFR profile is the ratio of these two surface-density profiles. If nSFR > n∗, a centrally rising sSFR profile and a steep ΣSFR–Σ∗ resolved-main-sequence slope follow mathematically from the profile shapes. These quantities are therefore consistency checks of the classification input, not independent confirmations; the paper itself describes the profile as “consistent with their high nSFR”.

full rationale

The paper’s central claim has two components: an internal-geometry dichotomy and an environmental difference. The geometry component is partially circular: the two MS-early subgroups are defined by whether nSFR is larger or smaller than n∗, and the later “findings” that MS-early SF galaxies have centrally enhanced sSFR and a steeper resolved main sequence are direct consequences of that same index comparison. The paper does not present these as an independent prediction, but as consistent with the classification. The environmental component, however, is genuinely independent: the halo-mass comparison uses the external Tinker group catalog and local densities from GEMAVAC, neither of which enters the Sérsic fits or the subgroup definition. The denser-environment result for MS-early SF therefore does not reduce by construction to the fitted inputs. There is no load-bearing self-citation; the Koyama et al. (2013) citation is a standard external reference for the main sequence. A significant caveat is the paper’s own admission that AGN/composite masking lowers nSFR in AGN hosts, which could bias the nSFR < n∗ subgroup; the Appendix A robustness check repeats only the sSFR profiles and resolved main sequence, not the environment analysis, so the environmental result is not protected against this acknowledged systematic. This is a correctness risk rather than a circularity, and it does not by itself raise the circularity score.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper is an observational analysis that relies on standard astrophysical calibrations and public survey products; it introduces no new physical entities and no ad hoc physical parameters. The main hand-chosen settings are sample-selection thresholds and mass bins.

free parameters (3)
  • MS selection offset threshold = ±1 dex
    Galaxies within ±1 dex (approximately 3 sigma) of the best-fit MS relation are defined as MS galaxies; this hand-chosen threshold determines the 97 MS-early sample size.
  • Morphological classification thresholds = T-type <= 0, P_LTG < 0.1 (early); T-type > 0, P_LTG > 0.9 (late)
    Conservative hand-chosen thresholds to select clean early-type and late-type samples from the deep-learning morphology catalog.
  • Stellar mass bins for radial profiles = log(M/Msun) in [8.5, 9.75) and [9.75, 11.0)
    Hand-chosen mass bins used in the radial profile comparison; the choice could affect the high-mass group which contains most MS-early galaxies.
assumptions (4)
  • domain assumption Dust-corrected H-alpha luminosity traces SFR via the Kennicutt calibration.
    Section 2.3 converts dust-corrected H-alpha maps to Sigma_SFR using standard calibration; this is a well-established but model-dependent assumption.
  • domain assumption The Firefly stellar mass maps are accurate enough for Sersic fitting.
    Sigma_star maps are taken from the MaNGA Firefly VAC (Goddard et al. 2017; Neumann et al. 2022); any systematic errors in stellar population synthesis propagate to n_star and the subgroup classification.
  • domain assumption The Tinker 2020 group catalog halo masses are reliable.
    The environmental offset in halo mass at fixed stellar mass is the main independent evidence for the denser environments of MS-early_SF galaxies; inaccuracies in the group finder or halo mass assignment could bias this result.
  • domain assumption PSF convolution with a 2.54 arcsec Gaussian adequately models the MaNGA beam.
    The Sersic fitting relies on this PSF model; for compact central star formation the model is acknowledged to be inadequate, which is flagged as a red flag.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Early-type Galaxies on the Star Formation Main Sequence: Internal Star Formation Geometry Revealed with MaNGA and Their Environmental Origin." pith.science (2026). https://pith.science/paper/VOVMO37L

@misc{pith2026250716212,
  author       = {Pith},
  title        = {Pith review of: Early-type Galaxies on the Star Formation Main Sequence: Internal Star Formation Geometry Revealed with MaNGA and Their Environmental Origin},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VOVMO37L}},
  note         = {Machine review of arXiv:2507.16212}
}
read the original abstract

Star-forming galaxies on the main sequence (MS) are often regarded as a uniform population characterized by similar global star formation properties. However, there exists a diversity in galaxy morphologies at fixed stellar mass and SFR. In this study, using spatially-resolved properties from the MaNGA final data release, we classify MS galaxies into late-type (MS-late) and early-type (MS-early). In addition, we further divide the MS-early galaxies into two distinct subgroups based on their internal star formation and stellar mass distributions within the galaxies. The first group -- ``MS-early\_SF'' -- shows centrally concentrated star formation without prominent stellar bulges and resides preferentially in dense environments, suggesting environmentally-driven evolution. The second group -- ``MS-early\_stellar'' -- exhibits significant stellar bulges with suppressed central star formation, maintains disk-like star formation patterns, and inhabits environments similar to those of late-type galaxies, indicating evolution through internal secular processes. Our findings demonstrate that spatially-resolved observations play critical roles in revealing the diverse evolutionary pathways hidden within galaxies that share similar global properties.

Figures

Figures reproduced from arXiv: 2507.16212 by the authors.

Figure 1
Figure 1. The histogram of T-type from the MaNGA Mor￾phology Deep Learning DR17 catalog for the full sample (gray) and the samples classified as late-type (blue) and early-type (red) galaxies based on the T-type and P LTG values. the Data Reduction Pipeline (DRP; Law et al. 2016) and the Data Analysis Pipeline (DAP; Westfall et al. 2019). The DRP provides sky-subtracted and flux￾calibrated 3D spectra for each galaxy. The DAP … view at source ↗
Figure 2
Figure 2. An example of the resolved BPT diagram. The left panel shows the [N II]/Hα vs. [O III]/Hβ line ratios for individual spaxels, with the demarcation lines from Kauffmann et al. (2003) (dashed line) and Kewley et al. (2006) (dash-dotted line). Spaxels are classified as star-forming (blue), composite (green), or AGN (red) based on their locations relative to these lines. The right panel displays the spatial distribution… view at source ↗
Figure 3
Figure 3. The relation between total stellar mass (M⋆) and total star formation rate (SFR) for the morphology classified sample. Blue circles show late-type galaxies, while red circles show early-type galaxies. The black solid line indicates the best-fit star-formation main sequence relation. The dashed lines show the ±1 dex scatter around the main sequence re￾lation. an upper limit on the potential star formation activity th… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: SDSS gri color composite images for six randomly selected MS-late galaxies (left panels) and MS-early galaxies (right panels). Each image is 50′′× 50′′ in size, with the MaNGA footprint overlaid as a pink outline. 8.5 9.0 9.5 10.0 10.5 11.0 11.5 log M * [M ] 0.0 0.2 0.…
Figure 5
Figure 5. Figure 5: The relation between stellar mass and effective radius for the MS-late (blue circles), MS-early (red circles), and passive early-type (grey circles) galaxies. Lines show the best-fit linear relations for passive early-type (black dashed), MS-early (red dashed), and MS-…
Figure 6
Figure 6. Figure 6: Examples of S´ersic profile fitting for stellar mass surface density (Σ∗, middle) and star formation rate surface density (ΣSFR, right) maps of the same galaxy. The left panel shows the SDSS image of the galaxy. Black circles in the middle and right panels represent th…
Figure 7
Figure 7. Figure 7: Distribution of S´ersic indices derived from ΣSFR and Σ∗ maps. In the main panel, the blue contours and circles represent the distribution of MS-late galaxies, while red circles show MS-early galaxies. Error bars on the MS-early points indicate the 95% confidence inter…
Figure 8
Figure 8. Figure 8: Example of the radial profile calculation process for a single galaxy. The top left panel shows the SDSS image of the galaxy, while the top right panel displays the corresponding MaNGA elliptical coordinate map. The bottom panels present the derived radial profiles: th…
Figure 9
Figure 9. Figure 9: The mean sSFR radial profiles for the three galaxy groups: MS-late (light blue diamonds), MS-early SF (navy stars), and MS-early stellar (red squares). The error bars represent the 95% confidence intervals of the mean, assuming a t-distribution. The mean profiles are s…
Figure 10
Figure 10. Figure 10: The resolved main sequence (the relation between log Σ∗ and log ΣSFR) for the three galaxy groups. From left to right, the panels show the distributions for MS-late, MS-early SF, and MS-early stellar galaxies. In each panel, the contours represent the 2D histogram of …
Figure 11
Figure 11. Figure 11: The relation between stellar mass (M∗) and star formation rate (SFR). Gray circles show the full sample, while blue stars and red squares represent the MS-early SF and the MS-early stellar galaxies, respectively. Filled sym￾bols indicate AGN host galaxies, while open …
Figure 12
Figure 12. Figure 12: Left: The relation between stellar mass and halo mass. Gray circles show the full sample, while blue stars and red squares represent the MS-early SF and the MS-early stellar galaxies, respectively. Filled symbols indicate AGN host galaxies, while open symbols represen…
Figure 13
Figure 13. Figure 13: Cumulative distribution of local density (ηk) for the three groups. The light blue dotted line represents the MS-late galaxies, while the blue and red solid lines show the MS-early SF and the MS-early stellar galaxies, respectively. ues of ηk indicating higher local d…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references · 1 canonical work pages

  1. [1]

    2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    doi:10.3847/1538-4365/ac4414 2022

  2. [2]

    S., Ahumada, R., Almeida, A., et al

    Aguado, D. S., Ahumada, R., Almeida, A., et al. 2019, ApJS, 240, 23, doi: 10.3847/1538-4365/aaf651 Argudo-Fern´ andez, M., Verley, S., Bergond, G., et al. 2015, A&A, 578, A110, doi: 10.1051/0004-6361/201526016 Early-type Galaxies on the MS 15 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/2013...

    doi:10.3847/1538-4365/aaf651 2019

  3. [3]

    A., Phillips, M

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

    doi:10.1086/130766 1981

  4. [4]

    S., Wechsler, R

    Behroozi, P. S., Wechsler, R. H., & Conroy, C. 2013, ApJ, 770, 57, doi: 10.1088/0004-637X/770/1/57

    doi:10.1088/0004-637x/770/1/57 2013

  5. [5]

    2018, MNRAS, 477, 3014, doi: 10.1093/mnras/sty768

    Belfiore, F., Maiolino, R., Bundy, K., et al. 2018, MNRAS, 477, 3014, doi: 10.1093/mnras/sty768

    doi:10.1093/mnras/sty768 2018

  6. [6]

    F., van der Wel, A., Papovich, C., et al

    Bell, E. F., van der Wel, A., Papovich, C., et al. 2012, ApJ, 753, 167, doi: 10.1088/0004-637X/753/2/167

    doi:10.1088/0004-637x/753/2/167 2012

  7. [7]

    P., Jankowiak, M., et al

    Bingham, E., Chen, J. P., Jankowiak, M., et al. 2019, J. Mach. Learn. Res., 20, 28:1. http://jmlr.org/papers/v20/18-403.html

    2019

  8. [8]

    R., Bershady, M

    Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, AJ, 154, 28, doi: 10.3847/1538-3881/aa7567

    doi:10.3847/1538-3881/aa7567 2017

Show all 58 references
  1. [9]

    A., & Barnes, J

    Blumenthal, K. A., & Barnes, J. E. 2018, MNRAS, 479, 3952, doi: 10.1093/mnras/sty1605

    2018 doi

  2. [10]

    2006, PASP, 118, 517, doi: 10.1086/500691

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

    2006 doi

  3. [11]

    2018, JAX: composable transformations of Python+NumPy programs, 0.3.13

    Bradbury, J., Frostig, R., Hawkins, P., et al. 2018, JAX: composable transformations of Python+NumPy programs, 0.3.13. http://github.com/jax-ml/jax

    2018

  4. [12]

    A., Law, D

    Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7, doi: 10.1088/0004-637X/798/1/7

    2015 doi

  5. [13]

    2003, MNRAS, 342, 345, doi: 10.1046/j.1365-8711.2003.06541.x

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

    2003

  6. [14]

    A., Clayton, G

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

    1989 doi

  7. [15]

    H., S´ anchez-Gallego, J., et al

    Cherinka, B., Andrews, B. H., S´ anchez-Gallego, J., et al. 2019, AJ, 158, 74, doi: 10.3847/1538-3881/ab2634

    2019 doi

  8. [17]

    2007, ApJ, 670, 156, doi: 10.1086/521818 Dom ´ ınguez S´ anchez, H., Margalef, B., Bernardi, M., &

    Daddi, E., Dickinson, M., Morrison, G., et al. 2007, ApJ, 670, 156, doi: 10.1086/521818 Dom ´ ınguez S´ anchez, H., Margalef, B., Bernardi, M., &

    2007 doi

  9. [18]

    2022, MNRAS, 509, 4024, doi: 10.1093/mnras/stab3089

    Huertas-Company, M. 2022, MNRAS, 509, 4024, doi: 10.1093/mnras/stab3089

    2022 doi

  10. [19]

    1980, ApJ, 236, 351, doi: 10.1086/157753

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

    1980 doi

  11. [20]

    2007, A&A, 468, 33, doi: 10.1051/0004-6361:20077525

    Elbaz, D., Daddi, E., Le Borgne, D., et al. 2007, A&A, 468, 33, doi: 10.1051/0004-6361:20077525

    2007 doi

  12. [21]

    L., S´ anchez, S

    Ellison, S. L., S´ anchez, S. F., Ibarra-Medel, H., et al. 2018, MNRAS, 474, 2039, doi: 10.1093/mnras/stx2882

    2018 doi

  13. [22]

    Fabian, A. C. 2012, ARA&A, 50, 455, doi: 10.1146/annurev-astro-081811-125521

    2012 doi

  14. [23]

    M., Parker, L

    Foster, L. M., Parker, L. C., Gwyn, S., et al. 2025, ApJ, 982, 120, doi: 10.3847/1538-4357/adb8dc

    2025 doi

  15. [24]

    2017, MNRAS, 466, 4731, doi: 10.1093/mnras/stw3371

    Goddard, D., Thomas, D., Maraston, C., et al. 2017, MNRAS, 466, 4731, doi: 10.1093/mnras/stw3371

    2017 doi

  16. [25]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332, doi: 10.1086/500975

    2006 doi

  17. [26]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357–362, doi: 10.1038/s41586-020-2649-2

    2020 doi

  18. [27]

    Harrison, C. M. 2017, Nature Astronomy, 1, 0165, doi: 10.1038/s41550-017-0165

    2017 doi

  19. [28]

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

    2007 doi

  20. [29]

    M., Tremonti, C., et al

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

    2003

  21. [30]

    Kennicutt, Robert C., J., Tamblyn, P., & Congdon, C. E. 1994, ApJ, 435, 22, doi: 10.1086/174790

    1994 doi

  22. [31]

    C., & Evans, N

    Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531, doi: 10.1146/annurev-astro-081811-125610

    2012 doi

  23. [33]

    Koleva, M., Prugniel, P., De Rijcke, S., & Zeilinger, W. W. 2011, MNRAS, 417, 1643, doi: 10.1111/j.1365-2966.2011.19057.x

    2011

  24. [34]

    2004, ARA&A, 42, 603, doi: 10.1146/annurev.astro.42.053102.134024

    Kormendy, J., & Kennicutt, Robert C., J. 2004, ARA&A, 42, 603, doi: 10.1146/annurev.astro.42.053102.134024

    2004

  25. [35]

    2013, MNRAS, 434, 423, doi: 10.1093/mnras/stt1035

    Koyama, Y., Smail, I., Kurk, J., et al. 2013, MNRAS, 434, 423, doi: 10.1093/mnras/stt1035

    2013 doi

  26. [36]

    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

    2001

  27. [37]

    R., Cherinka, B., Yan, R., et al

    Law, D. R., Cherinka, B., Yan, R., et al. 2016, AJ, 152, 83, doi: 10.3847/0004-6256/152/4/83

    2016 doi

  28. [38]

    2011, MNRAS, 410, 166, doi: 10.1111/j.1365-2966.2010.17432.x

    Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166, doi: 10.1111/j.1365-2966.2010.17432.x

    2011

  29. [39]

    2020, MNRAS, 496, 2962, doi: 10.1093/mnras/staa1489

    Maraston, C., Hill, L., Thomas, D., et al. 2020, MNRAS, 496, 2962, doi: 10.1093/mnras/staa1489

    2020 doi

  30. [40]

    L., Krawczyk, C., Shamsi, S., et al

    Masters, K. L., Krawczyk, C., Shamsi, S., et al. 2021, MNRAS, 507, 3923, doi: 10.1093/mnras/stab2282

    2021 doi

  31. [41]

    M., Cortese, L., Croom, S

    Medling, A. M., Cortese, L., Croom, S. M., et al. 2018, MNRAS, 475, 5194, doi: 10.1093/mnras/sty127

    2018 doi

  32. [42]

    L., et al

    Moreno, J., Torrey, P., Ellison, S. L., et al. 2015, MNRAS, 448, 1107, doi: 10.1093/mnras/stv094

    2015 doi

  33. [43]

    P., Somerville, R

    Moster, B. P., Somerville, R. S., Maulbetsch, C., et al. 2010, ApJ, 710, 903, doi: 10.1088/0004-637X/710/2/903

    2010 doi

  34. [44]

    B., & Abraham, R

    Nair, P. B., & Abraham, R. G. 2010, ApJS, 186, 427, doi: 10.1088/0067-0049/186/2/427

    2010 doi

  35. [45]

    2022, MNRAS, 513, 5988, doi: 10.1093/mnras/stac1260 16 Koyama et al

    Neumann, J., Thomas, D., Maraston, C., et al. 2022, MNRAS, 513, 5988, doi: 10.1093/mnras/stac1260 16 Koyama et al. pandas development team, T. 2020, pandas-dev/pandas: Pandas, latest, Zenodo, doi: 10.5281/zenodo.3509134

    2022 doi

  36. [46]

    2021, MNRAS, 502, 5508, doi: 10.1093/mnras/stab449

    Parikh, T., Thomas, D., Maraston, C., et al. 2021, MNRAS, 502, 5508, doi: 10.1093/mnras/stab449

    2021 doi

  37. [47]

    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

    2010 doi

  38. [48]

    2019, arXiv preprint arXiv:1912.11554 Planck Collaboration, Aghanim, N., Akrami, Y., et al

    Phan, D., Pradhan, N., & Jankowiak, M. 2019, arXiv preprint arXiv:1912.11554 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

    2019 arXiv

  39. [49]

    D., Smith, R

    Rawle, T. D., Smith, R. J., & Lucey, J. R. 2010, MNRAS, 401, 852, doi: 10.1111/j.1365-2966.2009.15722.x

    2010

  40. [50]

    S., Li, N., et al

    Ren, J., Liu, F. S., Li, N., et al. 2024, ApJ, 969, 4, doi: 10.3847/1538-4357/ad4117

    2024 doi

  41. [51]

    Sachdeva, S., Saha, K., & Singh, H. P. 2017, ApJ, 840, 79, doi: 10.3847/1538-4357/aa6c61

    2017 doi

  42. [52]

    Salim, S., Boquien, M., & Lee, J. C. 2018, ApJ, 859, 11, doi: 10.3847/1538-4357/aabf3c

    2018 doi

  43. [53]

    C., Janowiecki, S., et al

    Salim, S., Lee, J. C., Janowiecki, S., et al. 2016, ApJS, 227, 2, doi: 10.3847/0067-0049/227/1/2

    2016 doi

  44. [54]

    2024, A&A, 684, A166, doi: 10.1051/0004-6361/202347522 San Roman, I., Cenarro, A

    Salvador, D., Cerulo, P., Valenzuela, K., et al. 2024, A&A, 684, A166, doi: 10.1051/0004-6361/202347522 San Roman, I., Cenarro, A. J., D ´ ıaz-Garc ´ ıa, L. A., et al. 2018, A&A, 609, A20, doi: 10.1051/0004-6361/201630313 S´ anchez-Bl´ azquez, P., Peletier, R. F., Jim´ enez-Vi...

    2024

  45. [55]

    Tinker, J. L. 2020, arXiv e-prints, arXiv:2007.12200, doi: 10.48550/arXiv.2007.12200 —. 2021, ApJ, 923, 154, doi: 10.3847/1538-4357/ac2aaa van der Wel, A., Franx, M., van Dokkum, P. G., et al. 2014, ApJ, 788, 28, doi: 10.1088/0004-637X/788/1/28

    2020 doi arXiv

  46. [56]

    E., et al

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

    2020 doi

  47. [57]

    B., Cappellari, M., Bershady, M

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

    2019 doi

  48. [58]

    M., Maraston, C., Goddard, D., Thomas, D., & Parikh, T

    Wilkinson, D. M., Maraston, C., Goddard, D., Thomas, D., & Parikh, T. 2017, MNRAS, 472, 4297, doi: 10.1093/mnras/stx2215

    2017 doi

  49. [59]

    W., Lintott, C

    Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835, doi: 10.1093/mnras/stt1458

    2013 doi

  50. [60]

    M., van der Wel, A., et al

    Wuyts, S., F¨ orster Schreiber, N. M., van der Wel, A., et al. 2011, ApJ, 742, 96, doi: 10.1088/0004-637X/742/2/96 Early-type Galaxies on the MS 17 0.0 0.5 1.0 1.5 radius [Re] 0.4 0.2 0.0 0.2 0.4 log sSFR (Normalized) low mass (8.5 log M* < 9.75) 0.0 0.5 1.0 1.5 radius [Re] hi...

    2011 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.