Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds. Since the rise of machine learning in the last decade, it is now standard for analyses to employ multivariate ML classifiers trained on simulation to distinguish signal and background. Such classifiers significantly increase the statistical power of the analysis, but come with a huge loss of interpretability and render reliable background estimation difficult. Additionally, because they collapse the high-dimensional space into a single observable, fits to classifier scores are sub-optimal for the simultaneous estimation of multiple parameters, a crucial drawback in many present and future LHC measurements. In this talk we introduce an alternative approach, where instead of dimensionality reduction through classification, a generative ML model is instead trained to learn the signal and background distributions in the high-dimensional space. The background generative model can be trained directly on data, reducing reliance on simulation as compared to previous 'simulation based inference' approaches. These generative models are then used in place of traditional histograms in a template fit to extract the relevant parameters.
Systematic uncertainties on these models can be parameterized by standard template morphing methods and profiling over bootstrapped ensembles. We show that this approach can offer comparable or better sensitivity to the classifier-based approach, while being much more robust and interpretable.
