Bonn 2020 – wissenschaftliches Programm
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HK 60.5: Vortrag
Freitag, 3. April 2020, 12:30–12:45, J-HS H
Neural network-based analysis of the nucleus-antiproton annihilation for the PUMA project — •Yuki Kubota1, Yohei Ono2, and Alexandre Obertelli1 for the PUMA collaboration — 1Institut für Kernphysik, Technische Universität Darmstadt, Germany — 2The Open University of Japan, Japan
One of the most fascinating quantum phenomena in Nature is the occurrence of neutron skins and halos in atomic nuclei. The PUMA (antiProton Unstable Matter Annihilation) project aims at determining the neutron over proton densities at the surface of the short-lived nuclei by means of the nucleus-antiproton annihilation. The annihilation is followed by pion emission. The reconstruction of the total charge of the emitted pions allows for the determination of the annihilated particles: 0 in the case of proton and -1 in the case of neutron. However, the primordial pions may re-interact with the residual nucleus before being detected so that the total charge information is distorted. Thus the event-by-event basis identification is not possible.
We are developing the statistical analysis by using the neural network to determine the neutron-to-proton annihilation ratio from the multiplicity and total charge of charged pions. The typical uncertainty of less than 5% can be achieved with the 103 annihilation events, which was drastically improved by two orders of magnitude better than the previous study . An overview of the analysis as well as a method to reduce the systematic uncertainty coming from the model dependence are presented.
 M. Wada and Y. Yamazaki, AIP Conf. Proc. 793, 233 (2005).