New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
This work tackles resource constraints for future experimental facilities in particle physics, but it appears incremental as it reviews ongoing efforts rather than presenting new results.
The paper addresses the computational cost challenge of high energy physics detector simulation by exploring surrogate models using machine learning and differentiable programming as scalable solutions, as discussed in the 2021 Particle Physics Community Planning Exercise.
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').