Speaker
Description
Simulating particle physics data is a crucial yet computationally expensive aspect of analyzing data at the LHC. Typically, in fast simulation methods, we rely on a surrogate calorimeter model to generate a set of reconstructed objects. This work demonstrates the potential to generate these reconstructed objects in a single step, effectively replacing both the calorimeter simulation and reconstruction steps. Our primary goal in this set-to-set generation is to accurately replicate the detector's resolution and the properties of the reconstructed objects.
Building on the success of our previous slot-attention-based model, we introduce two innovative approaches to improve this task and evaluate their performance using a more realistic dataset. This dataset incorporates a realistic detector simulation and a machine learning-based reconstruction algorithm.
In the first approach, we enhance the slot-attention mechanism with a state-of-the-art graph diffusion model. This entails starting with a noisy graph and progressively eliminating noise conditioned on the truth particle set, ultimately generating the reconstructed particles.
The second approach involves iterative graph refinement, directly converting the set of truth particles into the set of reconstructed objects. These approaches outperform our previous baseline in terms of both accuracy and the resolution of predicted particle properties.
