Speaker
Description
Physics programs at future colliders cover a wide range of diverse topics and set high demands for precise event reconstruction. Recent analyses have stressed the importance of accurate jet clustering in events with low boost and high jet multiplicity. This contribution present how machine learning can be applied to jet clustering while taking desired properties such as infrared and collinear safety into account. In our contribution, we benchmark a score-based ML model on ZHH ($HH \rightarrow b\bar{b}b\bar{b}$) events, which are important for studies of the Higgs self-interaction. The results are compared regarding efficiency/purity and key physics metrics such as the dijet mass resolution with conventional algorithms such as Durham jet clustering, showing competitive performance. Furthermore, we present a possible extension for a fully differentiable model based on the Gumbel softmax.
Significance
We present results on a score-based ML model for jet clustering that shows comparable performance to current methods (Durham) regarding di-jet mass resolutions and report on the efforts towards a fully differentiable model.
References
Master thesis @ TUM+DESY: PUBDB-2025-00387 (https://bib-pubdb1.desy.de/record/622550)
LCWS 2024: https://agenda.linearcollider.org/event/10134/contributions/54618/attachments/39713/62774/LCWS_ZHH_ILD.pdf (slides 15-19)
| Experiment context, if any | ILD |
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