BibTeX

@article{2507.02032v1,
Author        = {Aishik Ghosh and Maximilian Griese and Ulrich Haisch and Tae Hyoun Park},
Title         = {Neural simulation-based inference of the Higgs trilinear self-coupling
  via off-shell Higgs production},
Eprint        = {2507.02032v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {One of the forthcoming major challenges in particle physics is the
experimental determination of the Higgs trilinear self-coupling. While efforts
have largely focused on on-shell double- and single-Higgs production in
proton-proton collisions, off-shell Higgs production has also been proposed as
a valuable complementary probe. In this article, we design a hybrid neural
simulation-based inference (NSBI) approach to construct a likelihood of the
Higgs signal incorporating modifications from the Standard Model effective
field theory (SMEFT), relevant background processes, and quantum interference
effects. It leverages the training efficiency of matrix-element-enhanced
techniques, which are vital for robust SMEFT applications, while also
incorporating the practical advantages of classification-based methods for
effective background estimates. We demonstrate that our NSBI approach achieves
sensitivity close to the theoretical optimum and provide expected constraints
for the high-luminosity upgrade of the Large Hadron Collider. While we
primarily concentrate on the Higgs trilinear self-coupling, we also consider
constraints on other SMEFT operators that affect off-shell Higgs production.},
Year          = {2025},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2507.02032v1},
File          = {2507.02032v1.pdf}
}