190922Z/Dagdist-MLP: v1.0.1

MagDist-MLP code associated with the manuscript "Stellar parameters and distances for Cool giants from LAMOST DR9 low-resolution spectra." This version includes the neural network architecture and model weights. Contains Python notebook files (.ipynb) and training data (.fits). Environment: The code is written in Python 3.9 and requires libraries including PyTorch, NumPy, and Pandas to run. Usage: The script MagDist-MLP.ipynb estimates stellar absolute magnitudes using a Multi-Layer Perceptron (MLP) network based on stellar parameters: metallicity [M/H], effective temperature Teff, and surface gravity log(g). Relation to the article: The outputs of this code are used to derive absolute magnitudes for Bayesian distance inference in the paper.