GAN-based data augmentation for rare and exotic hadron searches in Pb--Pb collisions in ALICE

TL;DR

This paper explores the use of GANs to generate synthetic data for rare heavy-flavour hadron searches in ALICE, aiming to improve sensitivity and reduce computational costs in Pb-Pb collision analyses.

Contribution

It introduces the first application of generative models, specifically GANs, for augmenting data in heavy-flavour physics within the ALICE experiment.

Findings

GANs can produce realistic synthetic signals for rare hadrons.

Data augmentation with GANs reduces the need for extensive full simulations.

The approach enhances the sensitivity of rare signal detection.

Abstract

This work presents a feasibility study aimed at enhancing the reconstruction sensitivity for rare heavy-flavour hadrons in Pb-Pb collisions in the ALICE experiment, using the $\Xi_{c}^{+}$ baryon as a benchmark. The $\Xi_{c}^{+}$ baryon has a low rate of production and some complex decay topologies as for instance the decay $\Xi_{c}^{+} \rightarrow \Xi^{-} + \pi^{+} + \pi^{+}$ considered in this work. Traditional simulation workflows involving event embedding and full detector response are computationally expensive and statistically limited, especially for rare signals. This study represents the first exploration of generative models within the heavy-flavour programme of ALICE. It uses a dataset of reconstructed physics quantities, such as momenta, positions, and decay vertex coordinates of $\Xi_{c}^{+}$ decay products in Pb-Pb collisions as input features, derived from augmented 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 $\Xi_{c}^{+}$ 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 approach supports rare-signal extraction in computationally demanding analyses and opens the way to broader applications of generative models in the ALICE heavy-flavour programme.