pith. sign in

arxiv: 2606.28489 · v1 · pith:MXHPG3JOnew · submitted 2026-06-26 · 🌌 astro-ph.GA · astro-ph.CO

pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web

Pith reviewed 2026-06-30 01:00 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords galaxy size-mass relationquiescent galaxiesbulge-dominated galaxiesAGN feedbackCOSMOS-Webstellar massmorphologysize evolution
0
0 comments X

The pith

Quiescent and bulge-dominated galaxies exhibit size-mass relation breaks at different stellar masses, indicating separate timescales for quenching and structural transformation.

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

The paper examines how galaxy sizes scale with stellar mass and redshift using data from COSMOS-Web and a generative model for mass and star-formation estimates. It finds that splitting galaxies by star-formation activity or by morphology leads to different size-mass and size-redshift relations. Specifically, both the quiescent and bulge-dominated populations follow double-power-law size-mass relations, but the breaks occur at different masses. This points to quenching and bulge growth happening at different stages, linked to peaks in AGN activity at those mass scales. The work also shows that scatter in sizes depends on morphology rather than star-formation rate.

Core claim

By combining pop-cosmos estimates with COSMOS-Web measurements for nearly 100,000 galaxies, the analysis reveals that the quiescent size-mass relation breaks at approximately 10^10.7 solar masses, matching the mass where AGN infrared torus luminosity fraction peaks in transitioning galaxies. The bulge-dominated relation breaks at 10^11.1 solar masses, aligning with the halo mass for peak AGN-driven baryonic redistribution. These offset pivots demonstrate that quenching and structural transformation proceed on distinct timescales.

What carries the argument

Double-power law breaks in the size-mass relations, with the quiescent break at lower mass than the bulge-dominated break.

If this is right

  • The sSFR-based and morphology-based classifications yield different slopes, intercepts, and scatter in the size relations.
  • Intrinsic scatter in galaxy sizes depends on structural morphology but not on specific star-formation rate.
  • Morphology-dependent trends in size evolution are only recoverable from space-based imaging.
  • The alignment of pivot masses with AGN features traces the progression of AGN feedback from quenching to structural change.

Where Pith is reading between the lines

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

  • If the different pivot masses hold, it suggests AGN feedback operates in stages, first suppressing star formation and later driving structural changes at higher masses.
  • This separation could help refine models of how galaxies connect to their dark matter halos through morphology.
  • Similar mass-dependent breaks might be testable in hydrodynamical simulations that include detailed AGN feedback prescriptions.

Load-bearing premise

The pop-cosmos generative model trained on COSMOS2020 delivers unbiased stellar mass and specific star-formation rate estimates that permit clean splits by star-formation activity and morphology.

What would settle it

Observing that the break masses for the quiescent and bulge-dominated size-mass relations are identical, or that they fail to coincide with the reported AGN luminosity and halo mass scales, would undermine the conclusion of distinct timescales.

Figures

Figures reproduced from arXiv: 2606.28489 by Anik Halder, Benedict Van den Bussche, Boris Leistedt, Daniel J. Mortlock, Gurjeet Jagwani, Hiranya V. Peiris, Joel Leja, Madalina N. Tudorache, Sinan Deger, Stephen Thorp.

Figure 1
Figure 1. Figure 1: The joint distribution of pop-cosmos inferred stellar mass, 𝑀ˆ ∗, and measured angular half-light radius 𝜃ˆ 50 (left) and physical half-light radius 𝑅ˆ 50 (right) for the COSMOS2025 galaxies. used to construct 𝑁𝑈𝑉𝑟𝐽 diagrams in Section 5. Throughout this work, we use parameter estimates and uncertainties based on the pop-cosmos SED fits: posterior median, 𝑚ˆ ∗ ≡ log10 (𝑀ˆ ∗ / M⊙), and standard deviation, 𝜎… view at source ↗
Figure 2
Figure 2. Figure 2: Size–mass relation in five different redshift bins in the spatially crossmatched COSMOS-Web sample. Cells are shaded by number of galaxies and are only shaded when they contain more than 3 galaxies. The single-power law fit to the size–mass relation is shown by the grey line. The shaded area shows the intrinsic scatter combined with the credible interval (16th and 84th percentile) on the median fit. The me… view at source ↗
Figure 3
Figure 3. Figure 3: Half-light radius 𝑅ˆ 50 (physical size) vs. stellar mass 𝑀ˆ ∗ split into five redshift bins. Cells are shaded by median sSFR (top), bulge-to-total ratio (from the F115W filter for 𝑧 < 2 and from the F150W filter for 𝑧 > 2, middle) and Sérsic index 𝑛 (bottom). The cells are only shaded when they contain more than 7 galaxies. The grey shaded area shows the pop-cosmos mass completeness limit evaluated at the … view at source ↗
Figure 4
Figure 4. Figure 4: Size–mass relation in five different redshift bins, split by star-formation activity. A single-power law is fit to the star-forming galaxies, defined as having log10 (sSFR/ yr−1 ) > −11.0 (top row); a double-power law is fit to the quiescent galaxies with log10 (sSFR/ yr−1 ) ≤ −11.0 (bottom row). Cells are shaded by number of galaxies and are only shaded when they contain more than 3 galaxies. The shaded a… view at source ↗
Figure 5
Figure 5. Figure 5: Size–mass relation in five different redshift bins for the sample, separated by the bulge-to-total ratio computed from the F115W filter (for galaxies between 0.0 < 𝑧 < 2.0) and the F150W filter (for galaxies between 2.0 < 𝑧 < 3.0). The sample is split into disc-dominated (B/T ≤ 0.2, top), intermediate (0.2 < B/T < 0.6, middle) and bulge-dominated (0.6 ≤ B/T, bottom) galaxies. Cells are shaded by number of … view at source ↗
Figure 6
Figure 6. Figure 6: Size–mass relation in five different redshift bins, split by Sérsic index. The sample is split into late- (top row) and early-type (bottom row) galaxies at Sérsic index 𝑛 = 2.5. A single-power law fit is performed for the late-type sample, whilst a double-power law fit is performed for the early-type galaxies - with the exception of the lowest redshift bin, where we show the single-power law fit. The shade… view at source ↗
Figure 7
Figure 7. Figure 7: The size–redshift relation for the four different cases: full sample (top left), star-forming/quiescent split (top right), B/T split (bottom left) and Sérsic index split (bottom right). For the star-forming/quiescent classification, the sample is split at log10 (sSFR/yr−1 ) < −11. For the bulge-to-total ratio classification, the sample is split into disc-dominated (B/T ≤ 0.2), intermediate (0.2 < B/T < 0.6… view at source ↗
Figure 8
Figure 8. Figure 8: Contaminant fraction between the sSFR and morphology classification – i.e. how many disc- (B/T ≤ 0.2) and bulge-dominated (B/T ≥ 0.6) galaxies are in the star-forming and quiescent samples compared to the full samples – in each of the five redshift bins. law for the bulge-dominated population, this is not true in the early￾type population. Bulge-to-total ratio should not be used as a proxy for star-formati… view at source ↗
Figure 9
Figure 9. Figure 9: Stellar mass versus redshift, colour-coded by bulge-to-total ratio measured from F115W for the galaxies between 0.0 < 𝑧 < 2.0 and F150W for the galaxies between 2.0 < 𝑧 < 3.0 (top row) and HSC-I (bottom row), shown separately for three sSFR bins. The space-based B/T recovers a clear correlation between B/T, stellar mass, and redshift that is washed out in the ground-based measurement, demonstrating that th… view at source ↗
Figure 10
Figure 10. Figure 10: Rest-frame 𝑁𝑈𝑉𝑟 𝐽 colour-colour diagram, colour-coded by the bulge-to-total ratio for the whole sample (top) and for the galaxies classified as quiescent by the 𝑁𝑈𝑉𝑟 𝐽 criterion, but classified as star-forming by their sSFR (sSFR > 10−11yr−1 , bottom). The black line shows the separation between star-forming and quiescent galaxies according to the rest-frame 𝑁𝑈𝑉𝑟 𝐽 colour criterion. show much weaker size … view at source ↗
read the original abstract

Galaxy sizes are correlated with stellar mass and redshift, as characterised by size scaling relations. The inferred forms of these scaling relations are sensitive to how galaxies are classified -- either by their star formation activity (e.g. specific star-formation rate, sSFR) or by their morphology markers (e.g. bulge-to-total ratio, S\'{e}rsic index). We combine stellar mass and sSFR estimates from pop-cosmos (a generative model trained on COSMOS2020 Spitzer IRAC $\textit{Ch.1} <26$) with size and morphology measurements from COSMOS-Web, obtaining $99,369$ galaxies. By investigating the size-mass and the size-redshift relations, we show that: (i) the sSFR/morphology splits give quantitatively different slopes, intercepts, and intrinsic scatter behaviour; (ii) intrinsic scatter depends on structural morphology but not on sSFR, which constrains the galaxy-halo connection; (iii) the quiescent and bulge-dominated size-mass relations both show double-power law breaks, but at different pivot masses, indicating that quenching and structural transformation occur on different time-scales; (iv) the morphology-dependent trends are only recoverable from space-based imaging. Further, the quiescent pivot mass $M_{\ast} \sim 10^{10.7}~\mathrm{M}_{\odot}$ coincides with the mass scale at which AGN (infrared torus) bolometric luminosity fraction peaks in transitioning galaxies, while the bulge-dominated pivot mass $M_{\ast} \sim 10^{11.1}~\mathrm{M}_{\odot}$ coincides with the halo mass at which AGN-driven baryonic redistribution peaks, tracing the interval over which AGN feedback ramps from quenching onset to structural transformation.

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

3 major / 2 minor

Summary. The manuscript analyzes size-mass and size-redshift relations for 99,369 galaxies in COSMOS-Web, combining stellar masses and sSFR from the pop-cosmos generative model (trained on COSMOS2020 IRAC Ch.1 < 26) with size and Sérsic index measurements. It reports that sSFR-based and morphology-based classifications produce quantitatively different scaling relations, that intrinsic scatter depends on structural morphology but not sSFR, and that both the quiescent (sSFR-selected) and bulge-dominated (morphology-selected) size-mass relations exhibit double-power-law breaks, but at distinct pivot masses (~10^10.7 M⊙ vs ~10^11.1 M⊙). These are interpreted as indicating that quenching and structural transformation occur on different timescales, with the quiescent pivot coinciding with the AGN infrared torus luminosity fraction peak and the bulge pivot with the halo mass scale of AGN-driven baryonic redistribution.

Significance. If the pop-cosmos classifications prove robust against mass-dependent bias, the separation of pivot masses would provide a useful empirical constraint on the relative timing of quenching and morphological transformation, and on the mass scales where AGN feedback transitions from onset to structural impact. The large sample size, the explicit comparison of sSFR versus morphology splits, and the demonstration that morphology-dependent trends require space-based imaging are strengths. The data-driven approach avoids internal circularity in the reported trends.

major comments (3)
  1. [Methods (pop-cosmos application and sample construction)] The central claim that the quiescent and bulge-dominated populations exhibit double-power-law breaks at different pivot masses (and therefore on different timescales) is load-bearing on the assumption that pop-cosmos stellar-mass and sSFR posteriors produce uncontaminated classifications when intersected with COSMOS-Web sizes and Sérsic indices. The manuscript does not report mass-dependent validation tests (e.g., purity/completeness as a function of M* or comparison against independent mass/sSFR catalogs) that would rule out correlated scatter shifting the apparent break locations.
  2. [Results (size-mass relations and double-power-law fits)] The reported pivot masses (M* ~ 10^10.7 M⊙ for quiescent, M* ~ 10^11.1 M⊙ for bulge-dominated) and the post-hoc AGN-coincidence arguments rest on the double-power-law functional form and the fitting procedure, yet no details are provided on the likelihood, error treatment, or robustness checks against redshift cuts, magnitude limits, or alternative functional forms.
  3. [Results (intrinsic scatter analysis)] The claim that intrinsic scatter depends on structural morphology but not on sSFR (constraining the galaxy-halo connection) requires that the morphology and sSFR classifications are orthogonal; any mass-dependent contamination between the two would induce spurious differences in scatter trends.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by a one-sentence statement of the sample selection cuts (redshift range, magnitude limits) and the precise functional form adopted for the double-power-law size-mass relation.
  2. [Figures (size-mass panels)] Figure captions for the size-mass relations should explicitly state the number of galaxies in each classification bin and whether the plotted points are medians or individual measurements.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important areas for strengthening the robustness of our conclusions. We address each major comment below and will incorporate revisions to provide the requested validations, methodological details, and checks.

read point-by-point responses
  1. Referee: [Methods (pop-cosmos application and sample construction)] The central claim that the quiescent and bulge-dominated populations exhibit double-power-law breaks at different pivot masses (and therefore on different timescales) is load-bearing on the assumption that pop-cosmos stellar-mass and sSFR posteriors produce uncontaminated classifications when intersected with COSMOS-Web sizes and Sérsic indices. The manuscript does not report mass-dependent validation tests (e.g., purity/completeness as a function of M* or comparison against independent mass/sSFR catalogs) that would rule out correlated scatter shifting the apparent break locations.

    Authors: We agree that mass-dependent validation is necessary to support the pivot-mass separation. Although pop-cosmos was validated in its original work, the current manuscript lacks explicit tests against mass. In revision we will add purity/completeness versus M*, cross-comparisons to independent catalogs, and a quantitative assessment of how classification contamination could shift the fitted breaks. revision: yes

  2. Referee: [Results (size-mass relations and double-power-law fits)] The reported pivot masses (M* ~ 10^10.7 M⊙ for quiescent, M* ~ 10^11.1 M⊙ for bulge-dominated) and the post-hoc AGN-coincidence arguments rest on the double-power-law functional form and the fitting procedure, yet no details are provided on the likelihood, error treatment, or robustness checks against redshift cuts, magnitude limits, or alternative functional forms.

    Authors: The manuscript indeed omits these fitting details. We will expand the methods section to specify the likelihood, error propagation, and robustness tests (redshift/magnitude cuts, alternative functional forms). These additions will directly support the reported pivot locations and AGN-coincidence discussion. revision: yes

  3. Referee: [Results (intrinsic scatter analysis)] The claim that intrinsic scatter depends on structural morphology but not on sSFR (constraining the galaxy-halo connection) requires that the morphology and sSFR classifications are orthogonal; any mass-dependent contamination between the two would induce spurious differences in scatter trends.

    Authors: We acknowledge that non-orthogonality could affect the scatter comparison. In revision we will quantify the mass-dependent overlap between the two classifications and test whether the morphology-dependent scatter trend remains after accounting for cross-contamination, thereby clarifying the constraint on the galaxy-halo connection. revision: partial

Circularity Check

0 steps flagged

Minor self-citation to pop-cosmos model; central size-mass fits remain independent

full rationale

The paper applies a pre-trained generative model (pop-cosmos, trained on COSMOS2020) to obtain stellar masses and sSFRs, then fits size-mass relations to independent COSMOS-Web size and Sérsic data. The double-power-law breaks and distinct pivot masses (~10^10.7 and ~10^11.1 M⊙) are obtained by direct fitting to the classified samples; no equation or step reduces these fitted quantities to the model inputs by construction. The citation to pop-cosmos is a single self-citation that is not load-bearing for the reported trends, which are falsifiable against the external imaging data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the accuracy of the pop-cosmos stellar-mass and sSFR estimates and on the reliability of COSMOS-Web structural measurements for classification.

axioms (2)
  • domain assumption pop-cosmos provides unbiased stellar mass and sSFR estimates for the COSMOS-Web sample
    Used to obtain the 99,369 galaxies and to perform the sSFR-based splits
  • domain assumption COSMOS-Web imaging yields accurate size and morphology measurements suitable for structural classification
    Source of the size data and bulge-to-total / Sersic index splits

pith-pipeline@v0.9.1-grok · 5902 in / 1406 out tokens · 45475 ms · 2026-06-30T01:00:01.226596+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

300 extracted references · 259 canonical work pages · 128 internal anchors

  1. [1]

    Correlations between Nebular Emission and the Continuum Spectral Shape in SDSS Galaxies

    Correlations between Nebular Emission and the Continuum Spectral Shape in SDSS Galaxies. , keywords =. doi:10.1088/0004-6256/141/4/133 , archivePrefix =. 1110.1877 , primaryClass =

  2. [2]

    arXiv e-prints , keywords =

    pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches. arXiv e-prints , keywords =

  3. [3]

    and Ralchenko,

    Kramida, A. and Ralchenko,. 2024 , month = nov, doi =

  4. [4]

    , keywords =

    Discovering the building blocks of dark matter halo density profiles with neural networks. , keywords =. doi:10.1103/PhysRevD.105.103533 , archivePrefix =. 2203.08827 , primaryClass =

  5. [5]

    , keywords =

    Representation learning approach to probe for dynamical dark energy in matter power spectra. , keywords =. doi:10.1103/PhysRevD.110.023514 , archivePrefix =. 2310.10717 , primaryClass =

  6. [6]

    , keywords =

    CDM and early dark energy in latent space: A data-driven parametrization of the CMB temperature power spectrum. , keywords =. doi:10.1103/PhysRevD.111.083537 , archivePrefix =. 2502.09810 , primaryClass =

  7. [7]

    Proceedings of the 9th Python in Science Conference , title =

    McKinney, Wes , doi =. Proceedings of the 9th Python in Science Conference , title =. 2010 , publisher =

  8. [8]

    and Mellema, Garrelt and Mao, Yi and Iliev, Ilian T

    Eliminating error in the chemical abundance scale for extragalactic H II regions. , keywords =. doi:10.1111/j.1365-2966.2012.21145.x , archivePrefix =. 1203.5021 , primaryClass =

  9. [9]

    Monthly Notices of the Royal Astronomical Society , author=

    Inferring physical properties of galaxies from their emission-line spectra , volume=. Monthly Notices of the Royal Astronomical Society , author=. 2017 , month=feb, pages=. doi:10.1093/mnras/stw2836 , number=

  10. [10]

    Color and Stellar Population Gradients in Passively Evolving Galaxies at z~2 from HST/WFC3 Deep Imaging in the Hubble Ultra Deep Field

    Color and Stellar Population Gradients in Passively Evolving Galaxies at z -0.5ex 2 from HST/WFC3 Deep Imaging in the Hubble Ultra Deep Field. , keywords =. doi:10.1088/0004-637X/735/1/18 , archivePrefix =. 1101.0843 , primaryClass =

  11. [11]

    , keywords =

    The Effects of Dust on Broadband Color Gradients in Elliptical Galaxies. , keywords =. doi:10.1086/177044 , adsurl =

  12. [12]

    Galaxy Masses

    Galaxy masses. Rev.\ Modern Phys. , keywords =. doi:10.1103/RevModPhys.86.47 , archivePrefix =. 1309.3276 , primaryClass =

  13. [13]

    , keywords =

    Spectral Classification of Emission-Line Galaxies. , keywords =. doi:10.1086/191166 , adsurl =

  14. [14]

    Theoretical Evolution of Optical Strong Lines across Cosmic Time

    Theoretical Evolution of Optical Strong Lines across Cosmic Time. , keywords =. doi:10.1088/0004-637X/774/2/100 , archivePrefix =. 1307.0508 , primaryClass =

  15. [15]

    , keywords =

    [OIII]/[NII] as an abundance indicator at high redshift. , keywords =. doi:10.1111/j.1365-2966.2004.07591.x , archivePrefix =. astro-ph/0401128 , primaryClass =

  16. [16]

    , keywords =

    De-noising of galaxy optical spectra with autoencoders. , keywords =. doi:10.1093/mnras/stad2709 , archivePrefix =. 2309.02315 , primaryClass =

  17. [17]

    Measuring the Physical Conditions in High-Redshift Star-Forming Galaxies: Insights from KBSS-MOSFIRE

    Measuring the Physical Conditions in High-redshift Star-forming Galaxies: Insights from KBSS-MOSFIRE. , keywords =. doi:10.3847/1538-4357/aae1a5 , archivePrefix =. 1711.08820 , primaryClass =

  18. [18]

    Characterizing Extreme Emission-line Galaxies. I. A Four-zone Ionization Model for Very High-ionization Emission. , keywords =. doi:10.3847/1538-4357/ac141b , archivePrefix =. 2105.12765 , primaryClass =

  19. [19]

    Starbursts in barred spiral galaxies. IV. On young bars and the formation of abundance gradients. , keywords =. doi:10.48550/arXiv.astro-ph/0001286 , archivePrefix =. astro-ph/0001286 , primaryClass =

  20. [20]

    , keywords =

    The impact of the nitrogen-to-oxygen ratio on ionized nebula diagnostics based on [NII] emission lines. , keywords =. doi:10.1111/j.1365-2966.2009.15145.x , archivePrefix =. 0905.4621 , primaryClass =

  21. [21]

    , keywords =

    Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning. , keywords =. doi:10.3847/1538-3881/ac4039 , archivePrefix =. 2112.03425 , primaryClass =

  22. [22]

    , keywords =

    Interpreting the detection of anomalies in SDSS spectra. , keywords =. doi:10.1051/0004-6361/202556339 , archivePrefix =. 2510.05235 , primaryClass =

  23. [23]

    , keywords =

    Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder. , keywords =. doi:10.1093/mnras/stag010 , archivePrefix =. 2506.17376 , primaryClass =

  24. [24]

    Very Strong Emission-Line Galaxies in the WISP Survey and Implications for High-Redshift Galaxies

    Very Strong Emission-line Galaxies in the WFC3 Infrared Spectroscopic Parallel Survey and Implications for High-redshift Galaxies. , keywords =. doi:10.1088/0004-637X/743/2/121 , archivePrefix =. 1109.0639 , primaryClass =

  25. [25]

    The Stellar Populations and Evolution of Lyman Break Galaxies

    The Stellar Populations and Evolution of Lyman Break Galaxies. , keywords =. doi:10.1086/322412 , archivePrefix =. astro-ph/0105087 , primaryClass =

  26. [26]

    Improving the full spectrum fitting method: accurate convolution with Gauss-Hermite functions

    Improving the full spectrum fitting method: accurate convolution with Gauss-Hermite functions. , keywords =. doi:10.1093/mnras/stw3020 , archivePrefix =. 1607.08538 , primaryClass =

  27. [27]

    Optical-IR Spectral Energy Distributions of z>2 Lyman Break Galaxies

    Optical-Infrared Spectral Energy Distributions of Z > 2 Lyman Break Galaxies. , keywords =. doi:10.1086/300291 , archivePrefix =. astro-ph/9712216 , primaryClass =

  28. [28]

    , keywords =

    The BPT Diagram in Cosmological Galaxy Formation Simulations: Understanding the Physics Driving Offsets at High Redshift. , keywords =. doi:10.3847/1538-4357/ac43b8 , archivePrefix =. 2201.03564 , primaryClass =

  29. [29]

    Quantifying correlations between galaxy emission lines and stellar continua

    Quantifying correlations between galaxy emission lines and stellar continua. , keywords =. doi:10.1093/mnras/stv2986 , archivePrefix =. 1601.02417 , primaryClass =

  30. [30]

    J.\ Open Source Software , keywords =

    pysersic: A Python package for determining galaxy structural properties via Bayesian inference, accelerated with jax. J.\ Open Source Software , keywords =. doi:10.21105/joss.05703 , archivePrefix =. 2306.05454 , primaryClass =

  31. [31]

    An ultra-deep near-infrared spectrum of a compact quiescent galaxy at z=2.2

    An Ultra-Deep Near-Infrared Spectrum of a Compact Quiescent Galaxy at z = 2.2. , keywords =. doi:10.1088/0004-637X/700/1/221 , archivePrefix =. 0905.1692 , primaryClass =

  32. [32]

    MegaMorph -- multi-wavelength measurement of galaxy structure: S\'ersic profile fits to galaxies near and far

    MegaMorph - multiwavelength measurement of galaxy structure. S \'e rsic profile fits to galaxies near and far. , keywords =. doi:10.1093/mnras/stt1320 , archivePrefix =. 1307.4996 , primaryClass =

  33. [33]

    MegaMorph - multi-wavelength measurement of galaxy structure: complete S\'ersic profile information from modern surveys

    MegaMorph - multiwavelength measurement of galaxy structure: complete S \'e rsic profile information from modern surveys. , keywords =. doi:10.1093/mnras/sts633 , archivePrefix =. 1212.3332 , primaryClass =

  34. [34]

    , keywords =

    GALAPAGOS-2/GALFITM/GAMA - Multi-wavelength measurement of galaxy structure: Separating the properties of spheroid and disk components in modern surveys. , keywords =. doi:10.1051/0004-6361/202142935 , archivePrefix =. 2204.05907 , primaryClass =

  35. [35]

    Detailed Structural Decomposition of Galaxy Images

    Detailed Structural Decomposition of Galaxy Images. , keywords =. doi:10.1086/340952 , archivePrefix =. astro-ph/0204182 , primaryClass =

  36. [36]

    J.\ Open Source Software , keywords =

    PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology. J.\ Open Source Software , keywords =. doi:10.21105/joss.04475 , adsurl =

  37. [37]

    Distributions of Galaxy Spectral Types in the Sloan Digital Sky Survey

    Distributions of Galaxy Spectral Types in the Sloan Digital Sky Survey. , keywords =. doi:10.1086/422429 , archivePrefix =. astro-ph/0407061 , primaryClass =

  38. [38]

    , keywords =

    Optical spectra of IRAS ``warm'' galaxies. , keywords =. doi:10.1086/131676 , adsurl =

  39. [39]

    Using Strong Lines to Estimate Abundances in Extragalactic HII Regions and Starburst Galaxies

    Using Strong Lines to Estimate Abundances in Extragalactic H II Regions and Starburst Galaxies. , keywords =. doi:10.1086/341326 , archivePrefix =. astro-ph/0206495 , primaryClass =

  40. [40]

    , keywords =

    The host galaxies and classification of active galactic nuclei. , keywords =. doi:10.1111/j.1365-2966.2006.10859.x , archivePrefix =. astro-ph/0605681 , primaryClass =

  41. [41]

    Theoretical Modeling of Starburst Galaxies

    Theoretical Modeling of Starburst Galaxies. , keywords =. doi:10.1086/321545 , archivePrefix =. astro-ph/0106324 , primaryClass =

  42. [42]

    , keywords =

    Classification parameters for the emission-line spectra of extragalactic objects. , keywords =. doi:10.1086/130766 , adsurl =

  43. [43]

    , journal =

    Galaxy morphology in rich clusters: implications for the formation and evolution of galaxies. , journal =. doi:10.1086/157753 , adsurl =

  44. [44]

    Dust Absorption And The Ultraviolet Luminosity Density At z \approx 3 As Calibrated By Local Starburst Galaxies

    Dust Absorption and the Ultraviolet Luminosity Density at z -0.5ex 3 as Calibrated by Local Starburst Galaxies. , keywords =. doi:10.1086/307523 , archivePrefix =. astro-ph/9903054 , primaryClass =

  45. [45]

    Distribution of mutual information from complete and incomplete data , url =

    Marcus Hutter and Marco Zaffalon , doi =. Distribution of mutual information from complete and incomplete data , url =. Comput.\ Statistics Data Analysis , keywords =

  46. [46]

    , keywords =

    Classification of radio sources through self-supervised learning. , keywords =. doi:10.1051/0004-6361/202554735 , archivePrefix =. 2503.19111 , primaryClass =

  47. [47]

    and Welling, Max , title =

    Kingma, Diederik P. and Welling, Max , title =. 2019 , volume =. doi:10.1561/2200000056 , journal =

  48. [48]

    2013 , issue_date =

    Bengio, Yoshua and Courville, Aaron and Vincent, Pascal , title =. 2013 , issue_date =. doi:10.1109/TPAMI.2013.50 , journal =

  49. [49]

    doi:10.1038/323533a0 , adsurl =

    Learning representations by back-propagating errors , journal =. doi:10.1038/323533a0 , adsurl =

  50. [50]

    , keywords =

    Dimensionality Reduction of SDSS Spectra with Variational Autoencoders. , keywords =. doi:10.3847/1538-3881/ab9644 , archivePrefix =. 2002.10464 , primaryClass =

  51. [51]

    ArXiv e-prints , keywords =

    A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection. ArXiv e-prints , keywords =. doi:10.48550/arXiv.2312.16687 , archivePrefix =. 2312.16687 , primaryClass =

  52. [52]

    Autoencoding Galaxy Spectra. II. Redshift Invariance and Outlier Detection. , keywords =. doi:10.3847/1538-3881/ace100 , archivePrefix =. 2302.02496 , primaryClass =

  53. [53]

    Machine-assisted discovery of relationships in astronomy

    Machine-assisted discovery of relationships in astronomy. , keywords =. doi:10.1093/mnras/stt329 , archivePrefix =. 1302.5129 , primaryClass =

  54. [54]

    Differential Galaxy Evolution in Cluster and Field Galaxies at z=0.3

    Differential Galaxy Evolution in Cluster and Field Galaxies at z -0.5ex 0.3. , keywords =. doi:10.1086/308056 , archivePrefix =. astro-ph/9906470 , primaryClass =

  55. [55]

    Wilk, M. B. and Gnanadesikan, R. , title = ". Biometrika , volume =. 1968 , month =. doi:10.1093/biomet/55.1.1 , url =

  56. [56]

    Analysis of a complex of statistical variables into principal components , volume =

    Harold Hotelling , journal =. Analysis of a complex of statistical variables into principal components , volume =. 1933 , doi =

  57. [57]

    Relations Between Two Sets of Variates , urldate =

    Harold Hotelling , journal =. Relations Between Two Sets of Variates , urldate =. 1936 , doi =

  58. [58]

    , journal =

    Kolmogorov, A.N. , journal =

  59. [59]

    Smirnov , title =

    N. Smirnov , title =. Ann.\ Math.\ Statistics , number =. 1948 , doi =

  60. [60]

    Scandinavian Actuarial J

    Harald Cram\'er , title =. Scandinavian Actuarial J. , volume =. 1928 , publisher =

  61. [61]

    arXiv e-prints , keywords =

    Practical Guidance for Bayesian Inference in Astronomy. arXiv e-prints , keywords =. doi:10.48550/arXiv.2302.04703 , archivePrefix =. 2302.04703 , primaryClass =

  62. [62]

    , keywords =

    Testing the consistency of dust laws in SN Ia host galaxies: a BAYESN examination of Foundation DR1. , keywords =. doi:10.1093/mnras/stab2849 , archivePrefix =. 2102.05678 , primaryClass =

  63. [63]

    , keywords =

    A hierarchical Bayesian SED model for Type Ia supernovae in the optical to near-infrared. , keywords =. doi:10.1093/mnras/stab3496 , archivePrefix =. 2008.07538 , primaryClass =

  64. [64]

    Type Ia Supernova Light Curve Inference: Hierarchical Bayesian Analysis in the Near Infrared

    Type Ia Supernova Light-Curve Inference: Hierarchical Bayesian Analysis in the Near-Infrared. , keywords =. doi:10.1088/0004-637X/704/1/629 , archivePrefix =. 0908.0536 , primaryClass =

  65. [65]

    Type Ia Supernova Light Curve Inference: Hierarchical Models in the Optical and Near Infrared

    Type Ia Supernova Light Curve Inference: Hierarchical Models in the Optical and Near-infrared. , keywords =. doi:10.1088/0004-637X/731/2/120 , archivePrefix =. 1011.5910 , primaryClass =

  66. [66]

    Do spectra improve distance measurements of Type Ia supernovae?

    Do spectra improve distance measurements of Type Ia supernovae?. , keywords =. doi:10.1051/0004-6361/201015792 , archivePrefix =. 1012.0005 , primaryClass =

  67. [67]

    , keywords =

    On the Application of Bayesian Leave-one-out Cross-validation to Exoplanet Atmospheric Analysis. , keywords =. doi:10.3847/1538-3881/acab67 , archivePrefix =. 2212.03872 , primaryClass =

  68. [68]

    , keywords =

    First semi-empirical test of the white dwarf mass-radius relationship using a single white dwarf via astrometric microlensing. , keywords =. doi:10.1093/mnras/stac3532 , archivePrefix =. 2206.01814 , primaryClass =

  69. [69]

    arXiv e-prints , keywords =

    Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis. arXiv e-prints , keywords =. doi:10.48550/arXiv.2310.03713 , archivePrefix =. 2310.03713 , primaryClass =

  70. [70]

    arXiv e-prints , keywords =

    Bringing 2D Eclipse Mapping out of the Shadows with Leave-one-out Cross-validation. arXiv e-prints , keywords =. doi:10.48550/arXiv.2310.03733 , archivePrefix =. 2310.03733 , primaryClass =

  71. [71]

    arXiv e-prints , keywords =

    Pareto Smoothed Importance Sampling. arXiv e-prints , keywords =. doi:10.48550/arXiv.1507.02646 , archivePrefix =. 1507.02646 , primaryClass =

  72. [72]

    Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

    Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics & Computing , keywords =. doi:10.1007/s11222-016-9696-4 , archivePrefix =. 1507.04544 , primaryClass =

  73. [73]

    Prospects for resolving the Hubble constant tension with standard sirens

    Prospects for Resolving the Hubble Constant Tension with Standard Sirens. , keywords =. doi:10.1103/PhysRevLett.122.061105 , archivePrefix =. 1802.03404 , primaryClass =

  74. [74]

    , keywords =

    Dark Energy Survey year 1 results: Joint analysis of galaxy clustering, galaxy lensing, and CMB lensing two-point functions. , keywords =. doi:10.1103/PhysRevD.100.023541 , archivePrefix =. 1810.02322 , primaryClass =

  75. [75]

    , keywords =

    General framework for cosmological dark matter bounds using N -body simulations. , keywords =. doi:10.1103/PhysRevD.103.043526 , archivePrefix =. 2007.13751 , primaryClass =

  76. [76]

    , keywords =

    Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference. , keywords =. doi:10.3847/1538-4365/ac9583 , archivePrefix =. 2207.05819 , primaryClass =

  77. [77]

    , keywords =

    Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models. , keywords =. doi:10.3847/1538-4365/ac9d99 , archivePrefix =. 2207.07673 , primaryClass =

  78. [78]

    , keywords =

    PopSED: Population-level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation. , keywords =. doi:10.3847/1538-3881/ad0be4 , archivePrefix =. 2309.16958 , primaryClass =

  79. [79]

    Harris and K

    Charles R. Harris and K. Jarrod Millman and St. Array programming with. 2020 , month = sep, journal =. doi:10.1038/s41586-020-2649-2 , publisher =

  80. [80]

    and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and

    Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and. Nature Methods , year =

Showing first 80 references.