Heidelberg 2022 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 98: Experimental Methods (general) 4

T 98.3: Vortrag

Donnerstag, 24. März 2022, 16:50–17:05, T-H29

BGNet: A neural network for beam background prediction for SuperKEKB — •Yannik Buch, Ariane Frey, and Benjamin Schwenker — II. Physikalisches Institut, Georg-August-Universität Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Deutschland

In pursuit to understanding the observed CP-violation in our universe, the Belle II detector investigates the b-sector by measuring the decays of the Υ(4S) resonance. These resonances are produced by the SuperKEKB accelerator at KEK in Tsukuba, Japan. The goal of SuperKEKB is to achieve an instantaneous luminosity of 6.5×10³⁵cm⁻²s⁻¹. Currently, a luminosity of 5×10³⁴cm⁻²s⁻¹ is reached, showing that considerable improvements to the beam focusing and increases of the ring currents are still necessary. A key component to achieve the design luminosity is the nano beam scheme. At the same time, however, the Belle II detector must not be damaged or its performance compromised by extensive radiation and hit rates.

The beam backgrounds at Belle II are mostly composed of storage backgrounds, luminosity-based backgrounds and injection backgrounds of both rings due to the top-up injection scheme. BGNet is trained to predict the overall hit rates and their composition in terms of background source for various Belle II sub-detectors. The input data for BGNet are 1Hz time series of variables describing the state of the SuperKEKB accelerator. This helps to monitor and mitigate the beam backgrounds during future operation.