I used a lot of different tools and strategies to make this paper easily reproducible at different levels. There's Docker container for the highest level of reproducibility, and package versions are managed with renv. The data used in the paper is hosted on Zenodo to avoid long queue times when downloading from the Climate Data Store and future-proof for when it goes away and checksumed before using it.
Most of the material is available through Jupyter notebooks in GitHub, and it should be easy to reproduce with the help of Binder. With the notebooks, you could experiment with different parameters to the ones analyzed in the paper. It also contains a large dataset of physical parameters of galaxies analysed in this work. We expect this work to be easily reproducible in the steps described in the repository.
The current code is written in Torch, which is no longer actively maintained. Since deep learning in nanophotonics is an area of active interest (e.g. for the design of new metamaterials), it is important to update the code to use a more modern deep learning library such as tensorflow/keras
We propose a simple method to retrieve optical constants from single optical transmittance measurements, in particular in the fundamental absorption region. The construction of needed envelopes is arbitrary and will depend on the user. However, the method should still be robust and deliver similar results.