  In the attempt to explain possible data anomalies from collider experiments
in terms of New Physics (NP) models, computationally expensive scans over their
parameter spaces are typically required in order to match theoretical
predictions to experimental observations. Under the assumption that anomalies
seen at a mass of about 95 GeV by the Large Electron-Positron (LEP) and Large
Hadron Collider (LHC) experiments correspond to a NP signal, which we attempt
to interpret as a spin-0 resonance in the $(B-L)$ Supersymmetric Standard Model
($(B-L)$SSM), chosen as an illustrative example, we introduce a novel Machine
Learning (ML) approach based on a multi-objective active search method, called
b-CASTOR, able to achieve high sample efficiency and diversity, due to the use
of probabilistic surrogate models and a volume based search policy,
outperforming competing algorithms, such as those based on Markov-Chain Monte
Carlo (MCMC) methods.
