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arxiv: 2606.26332 · v1 · pith:RGHAEWBJnew · submitted 2026-06-24 · 🌌 astro-ph.EP · astro-ph.GA· astro-ph.IM· astro-ph.SR

DiskMINT-GARDEN: Self-consistent Models to Estimate Disk Masses

Dingshan Deng , Uma Gorti , Ilaria Pascucci , Maxime Ruaud This is my paper

Pith reviewed 2026-06-26 01:22 UTC · model grok-4.3

classification 🌌 astro-ph.EP astro-ph.GAastro-ph.IMastro-ph.SR
keywords protoplanetary disksdisk mass estimationALMA observationsCO chemistrygrain-surface reactionsmachine learning inferenceradiative transferhydrostatic disk models
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The pith

Self-consistent models with grain-surface chemistry estimate protoplanetary disk gas masses from ALMA data in agreement with dynamical estimates.

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

The paper introduces DiskMINT-GARDEN, a grid of models built on DiskMINT that self-consistently links hydrostatic disk structure, radiative transfer, and a reduced CO chemical network including freeze-out and grain-surface reactions. A machine-learning regression model is trained on the grid's synthetic ALMA observables to infer gas mass, dust-to-gas ratio, and disk size from continuum and C18O line data. Applied to archival observations of 34 disks, the inferred gas masses match those from dynamical and HD-based methods. The models show that grain-surface conversion of CO to CO2 accounts for what other codes required as large-scale depletion, indicating that extant data show little chemical processing from disk evolution.

Core claim

DiskMINT-GARDEN is a grid spanning stellar mass 0.1-2.0 solar masses, gas disk mass 10^-5 to 0.1 times stellar mass, dust-to-gas ratio 0.003-0.1, and radius 10-300 au. It generates synthetic ALMA observables using DiskMINT's coupling of hydrostatic structure, continuum and line transfer, and reduced CO chemistry with freeze-out, grain-surface conversion to CO2, and isotope-selective photodissociation. A trained regression model maps observed dust continuum and C18O fluxes back to the input parameters. For 34 disks the resulting gas masses agree with independent dynamical and HD estimates, and the implemented chemistry reproduces the data without invoking additional evolutionary depletion.

What carries the argument

The DiskMINT-GARDEN model grid together with its trained machine-learning regression model that maps synthetic ALMA observables to gas mass, dust-to-gas ratio, and characteristic radius.

If this is right

  • Gas masses inferred from continuum and C18O data using the trained model agree with dynamical and HD estimates across the 34-disk sample.
  • Grain-surface chemistry implemented in DiskMINT accounts for the CO depletion previously required by codes such as DALI.
  • Extant ALMA data indicate little additional chemical processing from disk evolutionary processes beyond the included reactions.

Where Pith is reading between the lines

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

  • The regression model enables rapid mass estimates for future large ALMA disk surveys once trained on the grid.
  • If the reduced network remains sufficient across more disks, models of disk evolution can omit strong ad-hoc depletion factors.
  • Extending the grid to additional molecular lines would test whether the current chemistry fully captures the observables.

Load-bearing premise

The reduced CO chemical network with freeze-out, grain-surface conversion to CO2, and isotope-selective photodissociation is sufficient to match observed fluxes without extra evolutionary depletion mechanisms.

What would settle it

A disk whose gas mass measured directly by HD lines or dynamical methods differs substantially from the DiskMINT-GARDEN inference would falsify the claim that the network suffices.

Figures

Figures reproduced from arXiv: 2606.26332 by Dingshan Deng, Ilaria Pascucci, Maxime Ruaud, Uma Gorti.

Figure 1
Figure 1. Figure 1: Relationship between the gas disk mass Mgas and the C 18O (2-1) line luminosity, with the inferred C 18O line optical depth encoded in the curve transparency. Each panel corresponds to a different stellar mass (M⋆ = 0.1, 0.3, 0.5, 0.7, 1.0, and 2.0 M⊙). Colored curves show grid models with different characteristic radii Rc, while shaded bands indicate the variation with dust-to-gas ratio ε (see Appendix C … view at source ↗
Figure 2
Figure 2. Figure 2: Comparison between gas disk masses inferred from DiskMINT-GARDEN and dynamical estimates. Filled cir￾cles show targets with reliable dynamical Mgas, while open circles mark unreliable dynamical measurements (C. Lon￾garini et al. 2025); left-pointing arrows indicate dynamical upper limits. Error bars denote uncertainties in both axes. The black dashed line shows y = x, and black dotted lines indicate factor… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between gas disk masses inferred from DiskMINT-GARDEN and DALI estimates. Notations follow [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of Mgas inferred with DiskMINT-GARDEN and independent literature measurements. For each disk, Mgas inferred from the DiskMINT-GARDEN regression model are shown as orange stars, while gray squares indicate gas masses derived from DALI-based chemical modeling by L. Trapman et al. (2025b,a) and blue circles show dynamical gas mass estimates by C. Longarini et al. (2025); G. Lodato et al. (2023); P.… view at source ↗
read the original abstract

We present DiskMINT-GARDEN, a grid of self-consistent models together with a fast, open source inference tool for disk masses. The grid is built on DiskMINT, a tool which couples hydrostatic disk structure, continuum/line radiative transfer, and a reduced CO chemical network including freeze-out, grain-surface conversion, and isotope-selective photodissociation. DiskMINT-GARDEN model grid spans a large range of stellar mass ($0.1-2.0\,M_\odot$), gas disk mass ($10^{-5}-10^{-1}\,M_\star$), dust-to-gas ratio ($0.003-0.1$), and characteristic radius ($10-300\,{\rm au}$), and provides synthetic ALMA observables. We train a machine-learning regression model to infer the disk mass, dust-to-gas mass ratio, and disk size from the dust continuum and $\mathrm{C^{18}O}$ line observations. Applying DiskMINT-GARDEN to archival ALMA data of 34 disks, we find gas masses in good agreement with dynamical and HD-based estimates. Comparing our results with estimates from chemical modeling using DALI, we find that their need for large-scale elemental or CO depletion can be accounted for by grain-surface chemistry implemented in DiskMINT, with CO conversion to CO$_2$ being one of the main reactions. Therefore, extant data suggest little chemical processing due to disk evolutionary processes.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper presents DiskMINT-GARDEN, a grid of self-consistent models built on DiskMINT that couples hydrostatic disk structure, continuum/line radiative transfer, and a reduced CO chemical network (freeze-out, grain-surface conversion to CO2, isotope-selective photodissociation). Synthetic ALMA observables are generated across stellar mass 0.1-2.0 M_sun, gas mass 10^{-5}-10^{-1} M_star, dust-to-gas 0.003-0.1, and radius 10-300 au. An ML regressor is trained to infer gas mass, dust-to-gas ratio, and size from dust continuum and C18O line data. Application to archival ALMA observations of 34 disks yields gas masses in agreement with dynamical and HD-based estimates; the authors conclude that grain-surface chemistry accounts for apparent depletion previously requiring ad-hoc factors in DALI models, implying little additional evolutionary chemical processing.

Significance. If the claims hold, the work supplies an open-source, fast inference tool for disk masses that incorporates self-consistent chemistry rather than post-hoc depletion factors. The reported agreement with independent dynamical/HD masses for 34 disks, together with the attribution of CO depletion to the included grain-surface reactions, would reduce reliance on evolutionary depletion mechanisms and provide a reproducible framework for interpreting ALMA data on protoplanetary disks.

major comments (2)
  1. [Abstract] Abstract (final sentence) and results on 34-disk sample: the central claim that the reduced CO network (freeze-out + grain-surface CO→CO2 + isotope-selective photodissociation) suffices without additional evolutionary depletion is load-bearing, yet the manuscript provides no explicit test (e.g., comparison of χ^{2} or line fluxes) showing that adding extra depletion mechanisms does not improve the match to the same ALMA data.
  2. [ML regression] ML regressor training (grid and inference sections): because the regressor is trained exclusively on synthetic observables generated from the DiskMINT grid, the reported agreement with external dynamical/HD masses must be accompanied by quantitative out-of-sample metrics (cross-validation R^{2}, bias, and performance on parameter combinations outside the training grid) to confirm the agreement is not an artifact of model dependence.
minor comments (2)
  1. [Model grid] The parameter ranges and sampling of the model grid are stated but the number of models and the exact hyperparameter choices for the ML regressor are not listed; these details are needed for reproducibility.
  2. [Results] Figure captions for the application to the 34 disks should explicitly state which ALMA bands/lines were used as inputs to the regressor and whether any post-processing (e.g., beam convolution) was applied uniformly.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and recommendation of minor revision. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final sentence) and results on 34-disk sample: the central claim that the reduced CO network (freeze-out + grain-surface CO→CO2 + isotope-selective photodissociation) suffices without additional evolutionary depletion is load-bearing, yet the manuscript provides no explicit test (e.g., comparison of χ^{2} or line fluxes) showing that adding extra depletion mechanisms does not improve the match to the same ALMA data.

    Authors: We agree that an explicit test comparing fits with and without additional depletion would strengthen the claim. In the revised manuscript we will add such a comparison for the 34-disk sample, reporting χ² values and line flux residuals for models that include extra evolutionary depletion factors versus the baseline DiskMINT-GARDEN grid. This will show whether the grain-surface reactions already provide an adequate match. revision: yes

  2. Referee: [ML regression] ML regressor training (grid and inference sections): because the regressor is trained exclusively on synthetic observables generated from the DiskMINT grid, the reported agreement with external dynamical/HD masses must be accompanied by quantitative out-of-sample metrics (cross-validation R^{2}, bias, and performance on parameter combinations outside the training grid) to confirm the agreement is not an artifact of model dependence.

    Authors: The agreement with independent dynamical and HD masses (derived without reference to our chemical network or radiative transfer) already provides evidence that the results are not an artifact of model dependence. Nevertheless, to address the request directly we will add quantitative out-of-sample metrics, including cross-validation R², bias, and performance on held-out parameter combinations, to the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper constructs a model grid using DiskMINT (with its reduced CO network), generates synthetic ALMA observables, trains an ML regressor to map observables back to parameters, and applies the regressor to real archival data. Inferred masses are then compared to independent dynamical and HD-based estimates. This external validation step, plus the direct comparison to DALI results, keeps the central claims (network sufficiency and lack of extra evolutionary depletion) falsifiable against real observations rather than reducing to the model's own inputs by construction. No self-definitional equations, fitted inputs renamed as predictions, or load-bearing self-citations appear in the provided text.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard disk-modeling assumptions plus the specific reduced chemical network; no new particles or forces are introduced, and the spanned parameter ranges function as the primary free inputs rather than fitted constants.

free parameters (1)
  • ML regression hyperparameters
    The regression model is trained on the synthetic grid; exact coefficients or regularization choices are not stated in the abstract.
axioms (2)
  • domain assumption Hydrostatic equilibrium determines the vertical disk structure
    Invoked in DiskMINT to couple structure and radiative transfer (abstract).
  • domain assumption The reduced CO chemical network captures the dominant processes affecting C18O emission
    Includes freeze-out, grain-surface conversion, and isotope-selective photodissociation (abstract).

pith-pipeline@v0.9.1-grok · 5808 in / 1563 out tokens · 26729 ms · 2026-06-26T01:22:22.514103+00:00 · methodology

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Reference graph

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