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Register a new userA fast simulation package for STCF detector
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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.
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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, development of the software dedicated for the FPCCD is necessary. We, therefore, started to develop the software for FPCCD. In this article, the status of the study is reported.
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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 energy levels is proposed. The LRDM could predict the leakage current level and the behavior of capacitance for irradiated LGAD sensor at -30 $^{circ}$C after irradiation fluence $rm Phi_{eq}=2.5 times 10^{15} ~n_{eq}/cm^{2}$.
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 accuracy for diverse metrics across a large range of input variables. We demonstrate a successful application of the transfer learning concept: we train the network to simulate showers for electrons from a reduced range of primary energies, we then train further for a five times larger range (the model could not train for the larger range directly). The same concept is extended to generate showers for other particles (photons and neutral pions) depositing most of their energies in electromagnetic interactions. In addition, the generation of charged pion showers is also explored, a more accurate effort would require additional data from other detectors not included in the scope of the current work. Our further contribution is a demonstration of using GAN-generated data for a practical application. We train a third-party network using GAN-generated data and prove that the response is similar to a network trained with data from the Monte Carlo simulation. The showers generated by GAN present accuracy within $10%$ of Monte Carlo for a diverse range of physics features, with three orders of magnitude speedup. The speedup for both the training and inference can be further enhanced by distributed training.
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