Jay Sandesara
I am a postdoctoral researcher at the University of Wisconsin-Madison, affiliated with the Data Science Institute and the Department of Physics. My research focuses on the intersection of modern statistical tools and high-energy physics.
Sessions
Simulation-Based Inference (SBI) is a powerful class of machine learning based methods for statistical inference, with applications in many scientific domains including particle physics, cosmology and astrophysics. While demonstrating significant promise in small-scale studies over the last 10 years, a full-scale deployment for a particle physics experiment remained elusive due to the computational challenges involved. Using novel ideas, modern distributed computing resources, and tools like JAX and Tensorflow, we built the first end-to-end SBI workflow exclusively using the Scientific Python ecosystem that is scalable for measurements at the Large Hadron Collider (LHC). The new techniques were used to measure the lifetime of the Higgs boson with the ATLAS experiment at the LHC with unprecedented precision.
In this talk, I will present the many challenges encountered while scaling the new analysis method to a full-scale measurement and demonstrate how the power, versatility and the rich set of tools in the Scientific Python ecosystem played a critical role in overcoming them.