Speaker
Description
The use of machine learning for collider data generation has become a significant area of study within particle physics. This interest arises from the increasing computational difficulties associated with traditional Monte Carlo simulation methods, especially in the context of future high-luminosity colliders. Representing collider data as particle clouds introduces several advantageous aspects, e.g. the intricate correlations present in particle clouds can be used as sensitive tests for the accuracy of a generative model in approximating and sampling the underlying probability density. The complexities are further amplified by variable particle cloud sizes, which necessitate the use of more sophisticated models.
In this study, we present a novel model that employs an attention-based aggregation mechanism to address these challenges. The model uses adversarial training, ensuring the generator and critic exhibit permutation equivariance and invariance respectively with respect to their input. A feature matching loss for the generator is also introduced to stabilize the training process. The proposed model competes favourably with the state-of-the-art on the JetNet150 dataset, whilst demonstrating a significantly reduced parameter count compared to other top-tier models. Additionally, the model is applied to CaloChallenge dataset 2 and 3, where it yields promising results.
