NeuroStarMap: Neural Network encoding of Gaia’s distance ladder

NeuroStarMap aims at providing Neural Network (NN) tools for access to the Gaia catalogue source classes supporting the cosmic distance ladder materialization, namely Cepheids, RR Lyrae and eclipsing binaries. The tools are trained, tested and validated on Gaia DR3 objects, and are expected to be compatible (via update and upgrade) with the forthcoming DR4 and DR5 catalogue releases. The practical goal is the implementation of tools fed by suitable photometric and variability data, able to provide adequate estimate of the target distance, through its proxy, i.e. parallax, consistently with the direct Gaia determination. We discuss the available dataset characteristics, the filtering and pre-processing applied to ensure proper neural encoding, the NN model selection and the current status of dataset fitting. The proposed solution, labelled ParallaxPredictorMXL , is a heterogeneous combination of simpler regression models, providing the best match to the complex dataset information structure.