arXiv subjects

Ying-Hao Yu

Publications and source records attributed to Ying-Hao Yu.

2 recordsLinked to original sources

arXiv2026

Design and simulation of the High-Energy Proton Beam Telescope

A high-resolution beam telescope is essential for the precise characterization of silicon pixel sensors. As part of the CSNS-II upgrade project, a High-Energy Proton Beam Telescope (HEPTel) based on monolithic active pixel sensors (MAPS) has been designed for the forthcoming High-Energy Proton Experimental Station (HPES), which will provide 0.8 to 1.6 GeV single-particle proton beams. HEPTel consists of six ultra-thin telescope modules, with a material budget per module of about 0.061% X0. Simulated with a 1.6 GeV proton beam, the telescope is expected to achieve a resolution of about 1.83 micrometers. Additionally, a dedicated readout electronics system and a Data Acquisition (DAQ) system have been designed for HEPTel, based on which a preliminary test system was established for beam tests. The beam test results with 1.3 GeV electrons demonstrated a single-module resolution of about 5.77 micrometers, an overall telescope resolution of about 2.70 micrometers, and a detection efficiency above 99.5%. These results validate the HEPTel design and confirm its capability for forthcoming proton-beam experiments at HPES.

physics.ins-det↗

arXiv2017

Orthogonal Echo State Networks and stochastic evaluations of likelihoods

We report about probabilistic likelihood estimates that are performed on time series using an echo state network with orthogonal recurrent connectivity. The results from tests using synthetic stochastic input time series with temporal inference indicate that the capability of the network to infer depends on the balance between input strength and recurrent activity. This balance has an influence on the network with regard to the quality of inference from the short term input history versus inference that accounts for influences that date back a long time. Sensitivity of such networks against noise and the finite accuracy of network states in the recurrent layer are investigated. In addition, a measure based on mutual information between the output time series and the reservoir is introduced. Finally, different types of recurrent connectivity are evaluated. Orthogonal matrices show the best results of all investigated connectivity types overall, but also in the way how the network performance scales with the size of the recurrent layer.

cs.NE↗