DBNets

Dust Busters Nets - Ensemble of NNs trained to infer the mass of gap opening planets in protoplanetary discs

To install this library

Note: the current version of this tool (DBNets 1.0.0) has been developed and tested with tensorflow<=2.15> and keras 2. The newest keras 3 is not currently supported.

1. open a terminal

2. clone the repository with git clone https://github.com/dust-busters/DBNets.git

3. enter the new directory with cd DBNets

4. download all the lfs files with git lfs pull

5. install the library with pip install .

If you encounter some errors following the previous instructions, you can try to install the package in a python enviroment. To do that, you can follow the instructions below.

Install in a virtual enviroment

1. First create a new python enviroment with python3.10 -m venv <env_name>

2. activate the new enviroment source <env_name>/bin/activate

3. follow the above instructions to install DBNets in the new python enviroment

4. Enjoy!

To use the new enviroment within a jupyter-notebook, for instance for running the examples provided, create a new jupyter kernel using

python -m ipykernel install --name=<env_name>.

Once this is done, it is possible to select the new kernel from any jupyter-notebook.

Paper

Ruzza et al. (2024): https://doi.org/10.1051/0004-6361/202348421.

Aknowledgments

Computational resources have been provided by INDACO Core facility, which is a project of High Performance Computing at the Università degli Studi di Milano (https://www.unimi.it). This work has been supported by Fondazione Cariplo,grant n° 2022-1217, from the European Union’s Horizon Europe Research & Innovation Programme under the Marie Sklodowska-Curie grant agreement No.823823 (DUSTBUSTERS) and from the European Research Council (ERC) under grant agreement no. 101039651 (DiscEvol).