NEMESIS aims to readjust the current classification scheme and its characteristic timescales so that it is concurrent with the most recent observational and theoretical constraints. To meet these goals NEMESIS will compile the largest, panchromatic dataset comprising of all young stellar objects in nearby star-forming regions, harnessing critical information that resides in data from space missions. It will reprocess and analyze this unique dataset with supervised and unsupervised machine learning algorithms, deep learning neural networks for object detection, clustering and regression analysis of images in order to advance the analysis and interpretation beyond the current state-of-the-art. Ultimately, NEMESIS brings big data techniques and hybrid machine learning methods to systematically analyze and interpret large data volumes in order to answer some of the most persisting questions, paving the path toward data intensive science applications in modern astrophysics.
NEMESIS is a EU Horizon 2020/SPACE-funded project (Grant Agreement No. 101004141) that was launched in March 2021 with a scheduled duration of 48 months. It represents a collaboration between the Institute for Astronomy at University of Vienna (coordinating institute), the University of Geneva, and the Konkoly Observatory. To stay updated about NEMESIS, follow us at nemesis.univie.ac.at