Abstract
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.
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Corrections were made to this article on 15 January 2024. Some figure permissions were updated.