Conveners
Event Generation and Detector Simulation
- Andrea Rizzi (Universita & INFN Pisa (IT))
In this talk we present two recent proposals to use neural networks to improve Monte Carlo sampling and exploration of simulations that employ CPU expensive calculations. The main idea in the discussed methods is to employ a neural network to distinguish points likely to yield relevant results. This is achieved by training the neural network with previously obtained points that have been...
Monte Carlo simulations are an essential tool for data analysis in particle physics. Simulated events are typically produced alongside weights, that redistribute the production rate of a simulated process across the phase space. The presence of latent degrees of freedom can lead to a distribution of weights with negative values, often complicating analyses, especially if they involve machine...
With the increasing size of the machine learning (ML) model and vast datasets, the foundation model has transformed how we apply ML to solve real-world problems. Multimodal language models like chatGPT and Llama have expanded their capability to specialized tasks with common pre-train. Similarly, in high-energy physics (HEP), common tasks in the analysis face recurring challenges that demand...
Detailed event simulation at the LHC is taking a large fraction of computing budget. CMS developed an end-to-end ML based simulation framework, called FlashSim, that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. We show how this approach achieves a high degree of accuracy, not just on basic kinematics but on the complex...
