Speaker
Description
Neural Simulation-Based Inference (NSBI) is an emerging class of statistical methods that harness the power of modern deep learning to perform inference directly from high-dimensional data. These techniques have already demonstrated significant sensitivity gains in precision measurements across several domains, outperforming traditional approaches that rely on low-dimensional summaries. This talk will focus on the practical application of NSBI in LHC analyses, highlighting recent progress and ongoing efforts in the field. We will explore the challenges of scaling NSBI workflows to complex, high-dimensional parameter spaces, and discuss strategies developed to address these issues. We will also present progress towards the development of a computationally efficient and practical NSBI analysis framework tailored for use in high-energy physics experiments.
Significance
The presentation will cover the development of a new end-to-end tool that can be widely used by LHC collaborations to perform NSBI measurements. It will also cover new techniques that enable scaling workflow to LHC measurements.
| Experiment context, if any | ATLAS, CMS |
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