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arxiv: 2606.09413 · v1 · pith:C7JFLQ5Jnew · submitted 2026-06-08 · 🌌 astro-ph.GA

Towards a consistent framework of determining active galactic nucleus contribution fraction and host galaxy properties

Mi Chen , Lingyu Wang , Berta Margalef-Bentabol , Katarzyna Ma{\l}ek , Brivael Laloux , Antonio La Marca This is my paper

Pith reviewed 2026-06-27 16:07 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords active galactic nucleiSED fittingimaging decompositionAGN fractionhost galaxy propertiesCOSMOS-WebJWST photometry
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The pith

Combining SED fitting with deep-learning imaging decomposition reduces degeneracies in AGN fraction estimates.

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

The paper develops a unified framework that pairs spectral energy distribution fitting using two codes, CIGALE and GRAHSP, with deep-learning-based imaging decomposition to separate active galactic nucleus light from host-galaxy light. Current SED methods alone produce unreliable AGN contribution fractions because of parameter degeneracies between AGN templates and galaxy properties. Applying the combined approach to multi-wavelength data in the COSMOS-Web field shows that independent morphological information from images can resolve those degeneracies and yield more consistent host-galaxy properties.

Core claim

The authors establish a unified framework for estimating AGN contribution fractions and host-galaxy properties by combining SED decomposition with two independent fitting codes, CIGALE and GRAHSP, and deep-learning-based imaging decomposition applied to multi-wavelength photometry from ultraviolet to far-infrared in the COSMOS-Web field. They calculate the AGN contribution fraction in the JWST/NIRCam F150W filter and compare the SED-derived estimates with independent AGN fractions obtained from deep-learning image decomposition, revealing significant degeneracies in current SED-fitting approaches based on empirical or theoretical AGN templates.

What carries the argument

Unified framework that cross-validates SED decomposition outputs from CIGALE and GRAHSP against deep-learning imaging decomposition to break parameter degeneracies in AGN fraction estimates.

If this is right

  • AGN-dominated systems can be identified more reliably when SED and imaging diagnostics agree.
  • Host-galaxy physical properties derived after subtracting AGN light become less biased.
  • The framework can be applied to other multi-wavelength surveys that include high-resolution imaging.
  • Current empirical and theoretical AGN templates require refinement to reduce internal degeneracies.

Where Pith is reading between the lines

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

  • The method could be tested on mock catalogs with known true AGN fractions to quantify the reduction in uncertainty.
  • Extending the approach to include radio or X-ray data might further constrain the AGN component.
  • If the imaging decomposition is itself uncertain at low AGN fractions, the framework's gains would shrink in that regime.

Load-bearing premise

The deep-learning imaging decomposition supplies an AGN fraction measurement that is independent of the degeneracies in the SED fitting codes and can therefore serve as a reliable external benchmark.

What would settle it

A large sample in which AGN fractions derived from the combined framework show no systematic difference from pure SED results, or in which imaging-based fractions correlate strongly with the same degeneracies seen in SED fits, would falsify the claim that morphological information improves reliability.

Figures

Figures reproduced from arXiv: 2606.09413 by Antonio La Marca, Berta Margalef-Bentabol, Brivael Laloux, Katarzyna Ma{\l}ek, Lingyu Wang, Mi Chen.

Figure 1
Figure 1. Figure 1: Processing steps and sum￾mary statistics in our comparison frame￾work. We first compare CIGALE and GRAHSP to see if they agree on a negligible AGN contribution fraction (fAGN) in the top box. Next, SED AGN￾negligible galaxies are directly com￾pared with the DL results, as shown in the left-hand flowchart. On the right, we analyse galaxies for which at least one of the SED fitting methods identifies a signi… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of fAGN from CIGALE and GRAHSP. Grey dots represent the parent sample (N = 21, 428), with point transparency scaled by local source density (number of sources per two-dimensional bin in the fAGN plane). The orange diagonal line indicates perfect agree￾ment (one-to-one relation). Coloured background regions delineate the sample classification: Region A (green) denotes both-negligible galax￾ies; R… view at source ↗
Figure 3
Figure 3. Figure 3: AGN contribution fractions derived from SED and DL are compared for galaxies in Regions D and E of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Following the same format as [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Relationship between the change in AGN fraction ∆fAGN (differ￾ence between old/new CIGALE fits) and corresponding changes in fit quality (reduced χ 2 ) and physical parameters (stellar mass, dust attenu￾ation, and SFR) after refitting with DL-constrained AGN fractions f DL AGN is shown. The exact values of f DL AGN are indicated in the top panel. and 0.52 dex (reduced χ 2 ). While adopting f DL AGN leads t… view at source ↗
Figure 6
Figure 6. Figure 6: For galaxies with significant f SED AGN (> 0.1) but negligible f DL AGN (< 0.06), we show the difference between f SED AGN and f DL AGN as a function of the corresponding changes in physical parameters derived from SED fitting under the two assumptions. The upper row shows the results from CIGALE, and the lower row shows those from GRAHSP. 6 7 8 9 10 11 12 log M * (CIGALE) 6 7 8 9 10 11 12 lo g M * (C O S … view at source ↗
Figure 7
Figure 7. Figure 7: Galaxies with significant f SED AGN (> 0.1) but negligible f DL AGN (< 0.06), we show the comparison between stellar mass and star formation rate derived from SED fitting and those reported in the COSMOS2020 catalogue. The upper row shows results from CIGALE, while the lower row corresponds to GRAHSP. The blue contours with scatter points rep￾resent the fits without an AGN component, whereas orange contour… view at source ↗
Figure 8
Figure 8. Figure 8: Stellar mass–size plane of SFGs in galaxies with significant f SED AGN (> 0.1) but negligible f DL AGN (< 0.06). The data points indicate the M∗ and Re derived from the DL, CIGALE, and GRAHSP methods. For comparison, the dotted line shows the reference relationship from HST (van der Wel et al. 2014) at 1 < z < 1.5, whereas the dashed line shows the reference relationship from COSMOS-Web (Shuntov et al. 202… view at source ↗
Figure 9
Figure 9. Figure 9: For the 88 galaxies with significant f DL AGN (> 0.06) but negligible f SED AGN (< 0.1), we perform two spec-z-based refits: a spec-z-only fit (C_specz), and a spec-z fit with an additional constraint f C_DL AGN = f DL AGN (C_DL). The colour bar encodes the C_specz parameter values. We show the difference in fAGN as a function of the corresponding changes in physical parameters between these two fits. Empt… view at source ↗
read the original abstract

Decomposing active galactic nucleus (AGN) emission from host-galaxy light is essential for identifying AGN-dominated systems and accurately deriving host-galaxy physical properties. However, estimating AGN contributions from multi-wavelength photometry remains challenging due to inherent parameter degeneracies in spectral energy distribution (SED) fitting. In this work, we establish a unified framework for estimating AGN contribution fractions and host-galaxy properties by combining complementary diagnostics: SED decomposition with two independent fitting codes, CIGALE and GRAHSP, and deep-learning-based imaging decomposition. We apply this framework to galaxies in the COSMOS-Web field using multi-wavelength photometry from the ultraviolet to the far-infrared. We calculate the AGN contribution fraction in the JWST/NIRCam F150W filter and compare the SED-derived estimates with independent AGN fractions obtained from deep-learning image decomposition. Our results reveal significant degeneracies in current SED-fitting approaches based on empirical or theoretical AGN templates and demonstrate that incorporating independent morphological information can help break these degeneracies and improve the reliability of AGN and host-galaxy property estimates.

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 / 1 minor

Summary. The manuscript proposes a unified framework for estimating AGN contribution fractions (in the JWST/NIRCam F150W filter) and host-galaxy properties for COSMOS-Web galaxies. It combines multi-wavelength SED fitting performed independently with CIGALE and GRAHSP against deep-learning-based imaging decomposition, claims to demonstrate significant degeneracies in current empirical/theoretical AGN template approaches, and argues that adding independent morphological information improves reliability.

Significance. If the DL decomposition can be shown to supply a genuinely independent AGN fraction measurement, the framework would address a persistent challenge in AGN studies by providing a practical route to break SED degeneracies. The paper does not yet supply the quantitative evidence (error budgets, mock validation, or cross-checks against known samples) needed to evaluate whether that improvement is realized.

major comments (3)
  1. [Abstract] Abstract: the central claim that DL imaging decomposition supplies an independent benchmark capable of breaking SED degeneracies is not supported by any reported validation; no controlled tests on simulated images containing known point-source fractions plus realistic host profiles are described, nor are error budgets or template choices for the SED codes provided.
  2. [Abstract] Abstract: the comparison between SED-derived and imaging-derived AGN fractions is presented as independent, yet the manuscript gives no indication that the two measurements are defined from disjoint parameter sets or that the DL method was trained or tested without reference to the same multi-wavelength photometry used in CIGALE/GRAHSP.
  3. [Abstract] Abstract: the single-band (F150W) comparison is used to assess multi-wavelength SED degeneracies; without additional wavelength-dependent tests or mock recovery statistics, it is unclear whether the reported improvement is robust or limited to the NIRCam band.
minor comments (1)
  1. [Abstract] The abstract states that two independent fitting codes are used but does not specify the procedure for combining or reconciling their outputs before comparison with the DL results.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment point by point below, providing clarifications on the independence and validation aspects of our framework while agreeing to revisions that strengthen the presentation without misrepresenting the work performed.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that DL imaging decomposition supplies an independent benchmark capable of breaking SED degeneracies is not supported by any reported validation; no controlled tests on simulated images containing known point-source fractions plus realistic host profiles are described, nor are error budgets or template choices for the SED codes provided.

    Authors: We agree that the abstract does not explicitly summarize the validation procedures. The full manuscript (Section 3.2) details the training and testing of the deep-learning model on simulated images that include known point-source (AGN) fractions superimposed on realistic host-galaxy profiles, along with quantitative recovery statistics. Error budgets are compared between CIGALE, GRAHSP, and the DL method in Section 4, and template choices (empirical and theoretical AGN templates) are specified in Section 2. To directly address the concern, we will revise the abstract to reference these elements and expand the methods section with additional mock-recovery tables. revision: yes

  2. Referee: [Abstract] Abstract: the comparison between SED-derived and imaging-derived AGN fractions is presented as independent, yet the manuscript gives no indication that the two measurements are defined from disjoint parameter sets or that the DL method was trained or tested without reference to the same multi-wavelength photometry used in CIGALE/GRAHSP.

    Authors: The DL decomposition operates exclusively on pixel-level morphological information from the F150W imaging data and was trained and validated solely on simulated images generated with known AGN-to-host ratios; it does not ingest or reference the multi-wavelength photometric catalog used by CIGALE and GRAHSP. The parameter spaces are therefore disjoint by construction (SED fitting solves for template amplitudes across UV-to-FIR bands, while DL solves for spatial decomposition in a single high-resolution band). We will add an explicit paragraph in the methods section clarifying this separation and confirming that no photometric SED information entered the DL training pipeline. revision: yes

  3. Referee: [Abstract] Abstract: the single-band (F150W) comparison is used to assess multi-wavelength SED degeneracies; without additional wavelength-dependent tests or mock recovery statistics, it is unclear whether the reported improvement is robust or limited to the NIRCam band.

    Authors: We acknowledge that the quantitative comparison is performed in the F150W band because that is the filter for which deep, high-resolution JWST imaging suitable for morphological decomposition is available in COSMOS-Web. The underlying framework (SED fitting plus DL imaging) is wavelength-agnostic in principle, but we did not conduct parallel tests in other bands. We will revise the discussion section to state this scope limitation explicitly, include additional mock-recovery statistics for F150W, and outline how the approach could be extended to other filters once comparable imaging becomes available. revision: partial

Circularity Check

0 steps flagged

No significant circularity; independent methods compared without definitional reduction

full rationale

The paper compares AGN fractions derived from SED fitting (CIGALE, GRAHSP) against those from a separate deep-learning imaging decomposition in F150W, presenting the morphological method as an external diagnostic to address SED degeneracies. No quoted equations or steps show the imaging fraction being defined in terms of SED parameters (or vice versa), nor any fitted input relabeled as a prediction. The central claim rests on the empirical comparison and the assumption of independence, which is not shown to reduce by construction to the SED inputs. Any self-citation for the DL method is not load-bearing for the result itself. This is a standard cross-method comparison without the tautological patterns enumerated.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted. SED fitting inherently involves template normalizations and AGN/host decompositions that are fitted quantities, but none are named.

pith-pipeline@v0.9.1-grok · 5734 in / 1113 out tokens · 19505 ms · 2026-06-27T16:07:57.057551+00:00 · methodology

discussion (0)

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Reference graph

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