Speaker
Description
This poster will present a feasibility study aimed at enhancing the reconstruction sensitivity for rare and \mbox{exhibits} heavy-flavour hadrons in Pb–Pb collisions in the \mbox{ALICE} experiment, using the $\Xi_{\mathrm{c}}^{+}$ baryon as a benchmark. The $\Xi_{\mathrm{c}}^{+} \rightarrow \Xi^{-} + \pi^{+} + \pi^{+}$ exhibits complex decay topology and low production rates, making its analysis in high-multiplicity environments particularly challenging. Traditional simulation workflows involving event embedding and full detector response are computationally expensive and statistically limited, especially for rare signals.
Our goal will be to produce a dataset of reconstructed physics quantities of $\Xi_{\mathrm{c}}^{+}$ decay products and their secondary vertices in Pb–Pb collisions, starting from augmented data from ALICE Monte Carlo simulations.
Such features will serve as a training set for Generative Adversarial Networks (GANs) designed to generate statistically significant synthetic signal samples without the need for additional full simulations. While $\Xi_{\mathrm{c}}^{+}$ serves as a benchmark, the broader objective is to enable searches for exotic heavy-flavour hadrons or other exotic states with complex decay patterns.
By leveraging GAN-based augmentation, this study is the first attempt in exploiting generative models into the heavy-flavour program of ALICE, supporting rare signal reconstruction in computationally demanding analysis scenarios.
