Identifying highly magnetized white dwarfs: A dimensionality reduction framework for estimating magnetic fields

Magnetic fields play a crucial role in compact object physics, particularly in white dwarfs (WDs), where high densities can sustain strong magnetic fields. Observations have revealed magnetized WDs (MWDs) with surface fields reaching approximately $10^9\rm\,G$, although high-field MWDs are fewer in number in current catalogs owing to their intrinsic faintness and limitations in conventional electromagnetic surveys. In this study, we apply unsupervised machine learning (ML) techniques to systematically analyze a sample of hydrogen-atmosphere (DA) WDs. Using Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for cluster identification, we classify distinct subpopulations within the DA WD sample. Each cluster exhibits unique intrinsic properties such as mass, surface gravity, temperature, and age. Our analysis further reveals that these subgroups effectively differentiate MWDs from non-magnetic or weakly magnetic counterparts. Moreover, utilizing a set of previously confirmed MWDs, we estimate the field strengths of all other MWDs lacking magnetic field measurements. This study underscores the effectiveness of ML-based approaches in astrophysical discovery, particularly detecting magnetized compact objects when direct measurements are unavailable.