IRIS imaging algorithm posterior samples for the DSHARP survey + Score Model weights.

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples. Our code is open source and freely available at https://github.com/EnceladeCandy/IRIS. This dataset includes 250 posterior samples for each protoplanetary disk from the DSHARP survey and the neural network weights for the different score-based models used in this work (ADD LINK TO ARXIV PAPER). Search for the notebook "3_zenodo_data.ipynb" in the tutorials folder of the github repository to better understand the structure of the dataset. Each protoplanetary disk has an associated .h5 file containing the posterior samples obtained with the VE PROBES prior. The RU Lup disk has different .h5 files, obtained with different priors. The filename contains the prior used. The HD 143006 disk has different .h5 files obtained with the VP SKIRT prior but different scaling factor. The scaling factor is in the filename in each case (or can be retrieved inside the .h5) file.The Elias 24 disk has different .h5 files,  obtained by changing the sampling procedure with the VE PROBES prior. The sampler ("euler" or "pc") is indicated in the filename. The specific parameters for the pc sampler can be found with the sampling_params key in the dataset. For the Euler sampler, the number of steps was fixed at 4000 steps.