  We present a new Machine Learning-based multivariate analysis method for the
selection of time-correlated hits in the tagging system and devices used to
detect particles in the final state at the bremsstrahlung-based tagged photon
facilities. This method can be applied instead of the widely used sampling and
subtraction of the time-uncorrelated background, in particular at experiments
aiming for high precision, where the subtraction of the time-uncorrelated
background leads to increased uncertainties. Moreover, the identification of
events with Machine Learning algorithms allows to preserve the information
about correlations of kinematic variables in the final state, which can be
advantageous for further phenomenological analyses of the experimental results.
