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We present a new clustering method, Significance Mode Analysis (SigMA), toextract co-spatial and co-moving stellar populations from large-scale surveyssuch as ESA Gaia. The method studies the topological properties of the densityfield in the multidimensional phase space. We validate SigMA on simulatedclusters and find that it outperforms competing methods, especially in caseswhere many clusters are closely spaced. We apply the new method to Gaia DR3data of the closest OB association to Earth, Scorpio-Centaurus (Sco-Cen), andfind more than 13,000 co-moving young objects, with about 19% of these having asub-stellar mass. SigMA finds 37 co-moving clusters in Sco-Cen. These clustersare independently validated by their narrow HRD sequences and, to a certainextent, by their association with massive stars too bright for Gaia, henceunknown to SigMA. We compare our results with similar recent work and find thatthe SigMA algorithm recovers richer populations, is able to distinguishclusters with velocity differences down to about 0.5 km s$^{-1}$, and reachescluster volume densities as low as 0.01 sources/pc$^3$. The 3D distribution ofthese 37 coeval clusters implies a larger extent and volume for the Sco-Cen OBassociation than typically assumed in the literature. Additionally, we find theassociation to be more actively star-forming and dynamically more complex thanpreviously thought. We confirm that the star-forming molecular clouds in theSco-Cen region, namely, Ophiuchus, L134/L183, Pipe Nebula, Corona Australis,Lupus, and Chamaeleon, are part of the Sco-Cen The application of SigMA toSco-Cen demonstrates that advanced machine learning tools applied to the superbGaia data allows to construct an accurate census of the young populations, toquantify their dynamics, and to reconstruct the recent star formation historyof the local Milky Way.

Significance Mode Analysis (SigMA) for hierarchical structures. An application to the Sco-Cen OB association