26-30 July 2021
Europe/Berlin timezone

Machine learning augmented probes of light Yukawa couplings from Higgs pair production

27 Jul 2021, 10:38


Parallel session talk Higgs Physics T09: Higgs Physics


Lina Alasfar (Humboldt Universität zu Berlin)


The production of Higgs pairs is one of the most anticipated channels to access at the High-Luminosity LHC. It allows for a measurement of the Higgs trilinear self-interaction. In this work we investigate the possibility to probe the Higgs trilinear coupling through the decomposition of the Higgs pair production into channels based on their topologies. We use interpretable machine learning based on cooperative game theory in order to distinguish them, simplifying the machine learning analysis flow. This procedure ultimately leads to a strong bound on the trilinear coupling. Moreover, we extend the analysis by including the quark-initiated channel, $q\bar{q}\to hh$, which is strongly suppressed in the Standard Model, in order to probe the Higgs coupling to light quarks. We perform a multivariate fit to simultaneously extract the trilinear Higgs and light quark-Higgs couplings. The fit results in a loosened bound on the trilinear coupling by $\sim 25$\% for models that do not have minimal flavour violation. Furthermore, a similar analysis is preformed for the 100 TeV FCC-hh. We discuss some motivated new physics scenarios where large modifications in the light quark-Higgs couplings are manifest.

Email lina.alasfar@desy.de
Collaboration / Activity Phenomenology
First author Lina Alasfar

Primary authors

Lina Alasfar (Humboldt Universität zu Berlin) Ramona Gröber (Humboldt-Universität zu Berlin) Christophe Grojean (DESY) Ayan Paul (T (Phenomenology)) Zhuoni Qian (DESY)

Presentation Materials