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A Super Tau Charm Facility (STCF) is one of the major options for the accelerator-based high energy project in China in the post-BEPCII era, and its R&D program is underway. The proposed STCF will span center of mass energies ($sqrt{s}$) ranging from 2 to 7 GeV with a peaking luminosity above $0.5times 10^{35}$ cm$^{-2}$s$^{-1}$ at $sqrt{s}=4.0$ GeV, and will provide a unique platform for tau-charm physics and hadron physics. In order to evaluate the physical potential capabilities and optimize the detector design, a fast simulation package has been developed. This package takes as inputs the response of physical objects in each sub-system of the detector including resolution, efficiency as well as related variables for the kinematic fit and the secondary vertex reconstruction algorithm. It can flexibly adjust the responses of each sub-detector system and is a critical tool for the STCF R&D program.
We are developing the vertex detector with a fine pixel CCD (FPCCD) for the international linear collider (ILC), whose pixel size is $5 times 5$ $mu$m$^{2}$. To evaluate the performance of the FPCCD vertex detector and optimize its design, developmen
The Fast Tracker (FTK) is a proposed upgrade to the ATLAS trigger system that will operate at full Level-1 output rates and provide high quality tracks reconstructed over the entire detector by the start of processing in Level-2. FTK solves the combi
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment designed to measure the neutrino mass hierarchy using a central detector (CD), which contains 20 kton liquid scintillator (LS) surrounded by about 17,000 phot
We report a precise TCAD simulation for low gain avalanche detector (LGAD) with calibration by secondary ion mass spectroscopy (SIMS). The radiation model - LGAD Radiation Damage Model (LRDM) combines local acceptor degeneration with global deep ener
We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating scientific data while retaining a high level of accura