Speaker
Description
Modern high-energy physics analyses rely heavily on large-scale Monte Carlo (MC)
simulations for machine-learning training, efficiency corrections, and systematic
studies. For rare-signal workflows, obtaining sufficiently large reconstructed-level
signal samples often require computationally expensive MC campaigns with large
CPU and storage demands.
This work explores the use of Generative Adversarial Networks (GANs) for
reconstructed-level data augmentation in the ALICE experiment at CERN. The
proposed approach learns the multi-dimensional distribution of reconstructed observables directly from MC and generates statistically consistent synthetic signal
samples for downstream analysis workflows.
The framework is validated through feature-distribution comparisons, correlation
studies, Machine-learning-based classification, and signal extraction tests. The generated samples show good agreement with standard MC while significantly reducing
the marginal cost of producing large reconstructed-level datasets. The method provides a complementary generative layer within the simulation-to-analysis workflow
and demonstrates the potential of AI-driven augmentation for scalable MC-statistics
production in future rare-signal analyses.