Overview This repository contains data, code, and model weights for reproducing the main results of the paper, Insights on Galaxy Evolution from Interpretable Sparse Feature Networks (see arXiv preprint). Specifically, we provide data files (images-sdss.tar.gz and galaxies.csv), a snapshot of the code base (sparse-feature-networks v1.0.0), and model weights (resnet18-topk_4-metallicity.pth, resnet18-topk_4-bpt_lines.pth). These are described in detail below. Data galaxies.csv is the main galaxy sample after we have issued the cuts described in the paper (250,224 rows). We include 30 columns queried from the SDSS galSpecInfo, galSpecLine, and galSpecExtra tables: objID (int64) DR7ObjID (int64) specObjID (int64) ra (float32) dec (float32) z (float32) zErr (float32) velDisp (float32) velDispErr (float32) modelMag_u (float32) modelMag_g (float32) modelMag_r (float32) modelMag_i (float32) modelMag_z (float32) petroMag_r (float32) petroR50_r (float32) petroR90_r (float32) bptclass (int32) oh_p50 (float32) lgm_tot_p50 (float32) sfr_tot_p50 (float32) nii_6584_flux (float32) nii_6584_flux_err (float32) h_alpha_flux (float32) h_alpha_flux_err (float32) oiii_5007_flux (float32) oiii_5007_flux_err (float32) h_beta_flux (float32) h_beta_flux_err (float32) reliable (int32) images-sdss.tar.gz is a compressed directory containing 250,224 image cutouts from the DESI Legacy Imaging Surveys viewer. Each cutout was generated using the RESTful call http://legacysurvey.org/viewer/cutout.jpg?ra={ra}&dec={dec}&pixscale=0.262&layer=sdss&size=160 where the ra and dec are directly taken from galaxies.csv. Each image is name using the format {objID}.jpg, again taken from galaxies.csv. Code The code is a snapshot of https://github.com/jwuphysics/sparse-feature-networks at v1.0.0. After unpacking the images and moving them into the ./data directory, the directory structure should look like: ./ ├── data/ │ ├── images-sdss/ │ └── galaxies.csv ├── model/ ├── results/ └── src/ ├── config.py ├── dataloader.py ├── model.py ├── main.py └── trainer.py In order to run the analysis and reproduce the main results of the paper, you must create the software environment first: pip install torch fastai numpy pandas matplotlib cmasher tqdm and then simply run python src/main.py. Models The trained model weghts (resnet18-topk_4-metallicity.pth, resnet18-topk_4-bpt_lines.pth) are provided here for reproducing the exact results from the paper. These are compatible with the ResNet18TopK class defined in src/model.py, and the weights can be stored in the ./model directory. Alternatively, you can train your own models (i.e. by using the functions defined in src/trainer.py) and save them natively with Pytorch.