Speaker
Description
Detector simulation and event reconstruction constitute the primary computational bottleneck in modern particle physics. To bypass these CPU-intensive processing chains, we present Parnassus (Particle-flow Neural Assisted Simulations), a state-of-the-art generative AI framework for end-to-end fast simulation. Utilizing conditional flow matching, Parnassus maps truth-level stable particles directly to reconstructed particle-flow objects, accurately capturing event-, jet-, and particle-level features within a fully automated, GPU-optimized Python workflow.
We demonstrate the versatility and generalization power of Parnassus across multiple distinct experimental environments, including CMS, ATLAS, and ALEPH (LEP). Parnassus serves both as a high-throughput GPU alternative to official experiment fast simulations and as an accessible tool for phenomenologists to test the models in a realistic environment. It proves that modern generative approaches generalize seamlessly across diverse geometries and collider eras, offering a scalable path forward for high-energy physics.