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DBNets2.0 simulation-based inference for planet-induced dust substructures in protoplanetary discs

A Ruzza, G Lodato, GP Rosotti, PJ Armitage - arXiv preprint arXiv …, 2025 - arxiv.org
Computer Science paper astro-ph.EP Suggest

… In this work, we developed a simulation-based inference pipeline for the analysis of protoplanetary discs’ observations of the dust thermal emission. From the morphology …

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BibTeX

@article{2506.11200v1,
Author        = {A. Ruzza and G. Lodato and G. P. Rosotti and P. J. Armitage},
Title         = {DBNets2.0: simulation-based inference for planet-induced dust
  substructures in protoplanetary discs},
Eprint        = {2506.11200v1},
DOI           = {10.1051/0004-6361/202554401},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.EP},
Abstract      = {Dust substructures in protoplanetary discs can be signatures of embedded
young planets whose detection and characterisation would provide a better
understanding of planet formation. Traditional techniques used to link
substructures' morphology to the properties of putative embedded planets
present several limitations that the use of deep learning methods has partly
overcome. In our previous work, we developed DBNets, a tool exploiting an
ensemble of Convolutional Neural Networks (CNNs) to estimate the mass of
putative planets in disc dust substructures. This inference problem, however,
is degenerate as planets of different masses could produce the same rings and
gaps if other physical disc properties were different. In this paper, we
address this issue improving our simulation-based inference pipeline to
estimate the full posterior distribution for the planet mass and three
additional disc properties: the disc $\alpha$-viscosity, the scale height and
the dust Stokes number. We also address some minor issues of our previous tool.
The new pipeline involves a CNN that summarises the input images in a set of
summary statistics, followed by an ensemble of normalising flows that model the
inferred posterior for the target properties. We tested our pipeline on a
dedicated set of synthetic observations using the TARP test and standard
metrics, demonstrating its accuracy and precision. Additionally, we use the
results obtained on the test set to study the degeneracies between pairs of
parameters. Finally, we apply the developed pipeline to a set of 49 gaps in 34
protoplanetary discs' continuum observations. The results show typically low
values of $\alpha$-viscosity, disc scale heights, and planet masses, with 83%
of them being lower than 1M$_J$. These low masses are consistent with the
non-detections of these putative planets in direct imaging surveys. Our tool is
publicly available.},
Year          = {2025},
Month         = {Jun},
Note          = {A&A 700, A190 (2025)},
Url           = {http://arxiv.org/abs/2506.11200v1},
File          = {2506.11200v1.pdf}
}

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