BibTeX

@article{2503.07962v1,
Author        = {Benjamin Sluijter and Sascha Diefenbacher and Wahid Bhimji and Benjamin Nachman},
Title         = {Discriminative versus Generative Approaches to Simulation-based
  Inference},
Eprint        = {2503.07962v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {Most of the fundamental, emergent, and phenomenological parameters of
particle and nuclear physics are determined through parametric template fits.
Simulations are used to populate histograms which are then matched to data.
This approach is inherently lossy, since histograms are binned and
low-dimensional. Deep learning has enabled unbinned and high-dimensional
parameter estimation through neural likelihiood(-ratio) estimation. We compare
two approaches for neural simulation-based inference (NSBI): one based on
discriminative learning (classification) and one based on generative modeling.
These two approaches are directly evaluated on the same datasets, with a
similar level of hyperparameter optimization in both cases. In addition to a
Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR
Universe Challenge. We find that both the direct likelihood and likelihood
ratio estimation are able to effectively extract parameters with reasonable
uncertainties. For the numerical examples and within the set of hyperparameters
studied, we found that the likelihood ratio method is more accurate and/or
precise. Both methods have a significant spread from the network training and
would require ensembling or other mitigation strategies in practice.},
Year          = {2025},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2503.07962v1},
File          = {2503.07962v1.pdf}
}