25-29 January 2016
Bormio, Italy
Europe/Berlin timezone

Study of Improved $K_S^0$ Detection for the Belle~II Detector

25 Jan 2016, 17:06
3m
Bormio, Italy

Bormio, Italy

Poster Hadron Physics Monday Afternoon

Speaker

Mr Leonard Koch (JLU Giessen)

Description

In the near future, the Belle~II experiment at the SuperKEKB accelerator at KEK in Tsukuba, Japan, will start operation at a luminosity a factor $40$ higher than its predecessor experiment, Belle. The physics program includes the search for physics beyond the Standard Model of particle physics by the investigation of $CP$ violating processes and rare $B$ meson decays. Many important decay channels involve $K_S^0$ mesons. The detector features two layers of silicon pixel cells closest to the interaction point surrounded by four layers of double sided silicon strip detectors. Due to the high backgoround level, the expected occupancy of the Pixel Detector reaches up to $3\,\%$ requiring an online data reduction system: Using the four layers of strip detectors and the surrounding detectors, the online reconstructed tracks of charged particles are extrapolated to the pixelated layers, where Regions of Interest (ROIs) are defined around the intercepts. Only the pixel data inside these ROIs are stored. Thus, particles creating an insufficient number of hits in the outer detectors are not reconstructed and subsequntly no regions of interest are created, resulting in the loss of the related hits in the Pixel Detector. The particles creating a sufficient number of hits in all six layers, but not in the outer four, are lost as well. In this contribution, an online tracking algorithm is presented focusing on the reconstruction of charged pions from displaced vertices, the characteristic decay topology of $K_S^0$ mesons. All six layers are used, in order to prevent the pixel data of the above mentioned particles to be lost. The algorithm is based on the fast Hough transform applied to the hits mapped onto the conformal plane. The amount of background is reduced by a two stage neural network filtering system.

Primary author

Mr Leonard Koch (JLU Giessen)

Co-authors

Presentation Materials

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