2025ApJ...986...36L

Accreting massive black holes (MBHs, with M_• > 10³M_⊙) are known for their panchromatic emission, spanning from radio to gamma rays. While MBHs accreting at significant fractions of their Eddington rate are readily detectable, those accreting at much lower rates in radiatively inefficient modes often go unnoticed, blending in with other astrophysical sources. This challenge is particularly relevant for gas-starved MBHs in external galaxies and those possibly wandering in the Milky Way. We present HOLESOM (HOLESOM is publicly available at: https://github.com/valentinalatorre/holesom), a machine learning-powered tool based on the self-organizing maps (SOMs) algorithm, specifically designed to identify slowly accreting MBHs using sparse photometric data. Trained on a comprehensive set of ∼20,000 spectral energy distributions, HOLESOM can (i) determine if the photometry of a source is consistent with slowly accreting MBHs and (ii) estimate its black hole mass and Eddington ratio, including uncertainties. We validate HOLESOM through extensive tests on synthetic data and real-world cases, including Sagittarius A^⋆ (Sgr A^⋆), demonstrating its effectiveness in identifying slowly accreting MBHs. Additionally, we derive analytical relations between radio and X-ray luminosities to further constrain physical parameters. The primary strength of HOLESOM lies in its ability to accurately identify MBH candidates, which can then be targeted for follow-up photometric and spectroscopic observations. Fast and scalable, HOLESOM offers a robust framework for automatically scanning large multiwavelength data sets, making it a valuable tool for unveiling hidden MBH populations in the local Universe.