Speaker
Description
We present a Gradient-Boosted Decision Tree (BDT) for $\pi^0$ reconstruction in the $e^+e^- \to \pi^+\pi^-\pi^0\gamma$ process at the KLOE experiment. Trained on Monte Carlo events using ten kinematic variables and a series of preselection criteria (including $\chi^2 < 100$), the model is optimized via Bayesian hyperparameter search. On independent validation samples, the BDT achieves an AUC of 0.9989, a pair-level signal efficiency of 98\%, and a background rejection of 99\%. On the test set, optimization over aggregation strategies selects a threshold of 0.35 with the `mean' strategy, yielding an event-level signal efficiency (recall) of 99.7\%, a purity (precision) of 99.2\%, an accuracy of 98.9\%, and an F1 score of 0.99. Notably, the correct diphoton pair is identified in $>99.9\%$ of signal events, effectively removing combinatorial ambiguity. The tagger exhibits negligible overfitting, with a training--validation AUC gap of $5 \times 10^{-4}$. This MC-validated tagger is currently being deployed on real KLOE data. While the exact performance on data is subject to ongoing validation, these preliminary results promise substantial improvements over conventional cut-based selections.