Speaker
Description
Searching for rare B meson decays in heavy-ion collisions requires separating a small signal from a large and highly variable background. Machine-learning methods could improve this separation, but their training would rely largely on simulated signal and background samples that do not fully reproduce the real data. Differences in detector response, event multiplicity, and background composition can therefore lead to domain shift and poorly controlled selection biases.
Using an analysis based on conventional selection methods as a case study, I discuss where machine learning could improve sensitivity, why its application is particularly challenging in high-density collision data, and what validation would be needed before such methods could be used in a precision measurement.