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Recent spectra of protoplanetary disks around very low-mass stars (VLMS), captured by the Mid-InfraRed Instrument (MIRI) on board the James Webb Space Telescope (JWST), reveal a rich carbon chemistry. Current interpretations of these spectra are based on 0D slab models and provide valuable estimates for molecular emission temperatures and column densities in the innermost disk. However, the established fitting procedures and simplified models are challenged by the many overlapping gas features. We aim to simultaneously determine the molecular and the dust composition of the disk around the VLMS Sz28 in a Bayesian way. We model the JWST/MIRI spectrum of Sz28 up to $17\,\rm \mu m$ using the Dust Continuum Kit with Line emission from Gas (DuCKLinG). Systematically excluding different molecules from the Bayesian analysis allows for an evidence determination of all investigated molecules and isotopologues. We continue by examining the emission conditions and locations of all molecules, analysing the differences to previous 0D slab fitting, and analysing the dust composition. We find very strong Bayesian evidence for the presence of C2H2, HCN, C6H6, CO2, HC3N, C2H6, C3H4, C4H2, and CH4 in the JWST/MIRI spectrum of Sz28. Additionally, we identify CH3 and find tentative indications for NH3. There is no evidence for water in the spectrum. However, we show that column densities of up to $2\times10^{17}\,\rm cm^{-2}$ could be hidden in the observational noise if assuming similar emission conditions of water as the detected hydrocarbons. Contrary to previous 0D slab results, a C4H2 quasi-continuum is robustly identified. We expect some of the stated differences to previous 0D slab fitting results to arise from an updated data reduction of the spectrum, but also due to the different modelling process. The latter reason underpins the need for more advanced models and fitting procedures., Comment: accepted by Astronomy & Astrophysics

The Mid-InfraRed Instrument (MIRI) on board the James Webb Space Telescope (JWST) probes the chemistry and dust mineralogy of the inner regions of protoplanetary disks. The observed spectra are unprecedented in their detail, complicating interpretations which are mainly based on manual continuum subtraction and 0D slab models. We investigate the physical conditions under which the gas emits in protoplanetary disks. Based on MIRI spectra, we apply a full Bayesian analysis that provides the posterior distributions of dust and molecular properties. For doing so, we introduce the Dust Continuum Kit with Line emission from Gas (DuCKLinG), a model describing the molecular line emission and the dust continuum simultaneously without large computational cost. The dust model is based on work by Juhasz et al. (2009, 2010). The molecular emission is based on LTE slab models, but with radial gradients in column densities and temperatures. The model is compared to observations using Bayesian analysis. We benchmark this model to a complex thermo-chemical ProDiMo model and fit the MIRI spectrum of GWLup. We find that the retrieved molecular conditions from DuCKLinG fall within the true values from ProDiMo. The column densities retrieved by Grant et al. (2023) fall within the retrieved ranges in this study for all examined molecules (CO2, H2O, HCN, and C2H2). Similar overlap is found for the temperatures with only the temperature range of HCN not including the previously found value. This discrepancy may be due to the simultaneous fitting of all molecules compared to the step-by-step fitting of the previous study. There is statistically significant evidence for radial temperature and column density gradients for H2O and CO2 compared to the constant temperature and column density assumed in the 0D slab models. Additionally, HCN and C2H2 emit from a small region with near constant conditions., Comment: accepted by Astronomy & Astrophysics

ABRIDGED. The analysis of spectral energy distributions (SEDs) of protoplanetary disks to determine their physical properties is known to be highly degenerate. Hence, a Bayesian analysis is required to obtain parameter uncertainties and degeneracies. The challenge here is computational speed, as one radiative transfer model requires a couple of minutes to compute. We performed a Bayesian analysis for 30 well-known protoplanetary disks to determine their physical disk properties, including uncertainties and degeneracies. To circumvent the computational cost problem, we created neural networks (NNs) to emulate the SED generation process. We created two sets of radiative transfer disk models to train and test two NNs that predict SEDs for continuous and discontinuous disks. A Bayesian analysis was then performed on 30 protoplanetary disks with SED data collected by the DIANA project to determine the posterior distributions of all parameters. We ran this analysis twice, (i) with old distances and additional parameter constraints as used in a previous study, to compare results, and (ii) with updated distances and free choice of parameters to obtain homogeneous and unbiased model parameters. We evaluated the uncertainties in the determination of physical disk parameters from SED analysis, and detected and quantified the strongest degeneracies. The NNs are able to predict SEDs within 1ms with uncertainties of about 5% compared to the true SEDs obtained by the radiative transfer code. We find parameter values and uncertainties that are significantly different from previous values obtained by $\chi^2$ fitting. Comparing the global evidence for continuous and discontinuous disks, we find that 26 out of 30 objects are better described by disks that have two distinct radial zones. Also, we created an interactive tool that instantly returns the SED predicted by our NNs for any parameter combination., Comment: 40 pages, 22 figures, the online tool is available at https://tillkaeufer.github.io/sedpredictor