Speaker
Description
Precision detector calibration is a fundamental prerequisite for deploying realistic Simulation-Based Inference (SBI) in high-energy physics, particularly for maximizing sensitivity in Effective Field Theory (EFT) measurements. Recent advances in probabilistic machine learning enable unbinned likelihood fits that extract continuous, multidimensional calibrations simultaneously. This talk introduces Tag & Flow, a novel extension of the classic Tag-and-Probe efficiency calibration method currently under development within the CMS collaboration. Tag-and-Flow models detector efficiencies as continuous multidimensional functions while rigorously capturing their associated uncertainties. I will outline the methodology and discuss both the prospects and challenges of integrating this approach into SBI pipelines for future physics analyses
| Presentation type. | Talk |
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