Erlangen 2026 – scientific programme

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T: Fachverband Teilchenphysik

T 91: Methods in Particle Physics V

T 91.2: Talk

Friday, March 20, 2026, 09:15–09:30, KH 00.020

Jet energy resolution in future e⁺e⁻ Higgs factory experiments with ML and 5D calorimetry — •Bohdan Dudar and Lucia Masetti — Johannes Gutenberg-Universität Mainz, Mainz, Germany

The Pandora particle-flow algorithm (PFA) remains one of the best tools for event reconstruction aiming at an excellent jet energy resolution for future e⁺e⁻ collider experiments. Moreover, the rapid development of picosecond-timing sensors and their potential implementation in the calorimeter would allow for developing a new PFA with timing information, with improved performances in shower separation and particle tracking. Yet, this needs to be integrated in a full PFA reconstruction framework.

In this study, we examine the potential impact of timing in calorimetry on jet energy resolution, using an approach entirely based on machine learning. We develop an energy regression neural network (NN) with and without time information, and compare our results to the Pandora PFA. We use beam-background-free MC samples of Z→ qq (q=u,d,s) reconstructed with the International Large Detector (ILD) in full simulation.

Keywords: particle flow; 5D calorimetry; timing; MLPF; e+e- future Higgs factories