Abstract

The European FP7 project DIANA has performed a coherent analysis of a large set of observational data of protoplanetary disks by means of thermo-chemical disk models. The collected data include extinction-corrected stellar UV and X-ray input spectra (as seen by the disk), photometric fluxes, low and high resolution spectra, interferometric data, emission line fluxes, line velocity profiles and line maps, which probe the dust, polycyclic aromatic hydrocarbons (PAHs) and the gas in these objects. We define and apply a standardized modeling procedure to fit these data by state-of-the-art modeling codes (ProDiMo, MCFOST, MCMax), solving continuum and line radiative transfer (RT), disk chemistry, and the heating and cooling balance for both the gas and the dust. 3D diagnostic RT tools (e.g., FLiTs) are eventually used to predict all available observations from the same disk model, the DIANA-standard model. Our aim is to determine the physical parameters of the disks, such as total gas and dust masses, the dust properties, the disk shape, and the chemical structure in these disks. We allow for up to two radial disk zones to obtain our best-fitting models that have about 20 free parameters. This approach is novel and unique in its completeness and level of consistency. It allows us to break some of the degeneracies arising from pure Spectral Energy Distribution (SED) modeling. In this paper, we present the results from pure SED fitting for 27 objects and from the all inclusive DIANA-standard models for 14 objects. Our analysis shows a number of Herbig Ae and T Tauri stars with very cold and massive outer disks which are situated at least partly in the shadow of a tall and gas-rich inner disk. The disk masses derived are often in excess to previously published values, since these disks are partially optically thick even at millimeter wavelength and so cold that they emit less than in the Rayleigh–Jeans limit. We fit most infrared to millimeter emission line fluxes within a factor better than 3, simultaneously with SED, PAH features and radial brightness profiles extracted from images at various wavelengths. However, some line fluxes may deviate by a larger factor, and sometimes we find puzzling data which the models cannot reproduce. Some of these issues are probably caused by foreground cloud absorption or object variability. Our data collection, the fitted physical disk parameters as well as the full model output are available to the community through an online database (http://www.univie.ac.at/diana).

Highlights

  • The European FP7-SPACE project DIANA1 analyzed multi-wavelength and multi-kind observational data about protoplanetary disks by using a standardized modeling approach, in order to learn more about the physico-chemical state of the birthplaces of extra-solar planets, their evolution, and the pre-conditions for planet formation

  • The full results of our spectral energy distribution (SED)-fitting models are available at http://www-star.st-and.ac.uk/∼pw31/DIANA/SEDfit and the full results of the DIANA-standard models are available at http://www-star.st-and.ac.uk/∼pw31/DIANA/DIANAstandard

  • Details about the content of these files can be found in Appendix A. We offer these results to the community for further analysis, and as starting points to interpret other or maybe to predict new observations

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Summary

Introduction

The European FP7-SPACE project DIANA1 analyzed multi-wavelength and multi-kind observational data about protoplanetary disks by using a standardized modeling approach, in order to learn more about the physico-chemical state of the birthplaces of extra-solar planets, their evolution, and the pre-conditions for planet formation. In order to place our efforts into context, we first review the state-of-the-art of fitting disk observations by modeling. Previous studies have applied a wealth of different disk modeling approaches and fitting techniques, often tailored towards one particular object or a fresh set of observations from a particular new instrument for a couple of disk sources. A single model typically runs faster than a few CPU-min, such that χ2 minimization, e.g. in form of genetic algorithms, and sometimes Monte Carlo Markov Chain (MCMC) techniques can be applied

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