Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe study of active geomagnetic shielding coils system for JUNO
126
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator (LS) detector for neutrino mass ordering and other neutrino physics research. The detector uses large-size $20$ inches photomultiplier tubes to detect photons from a liquid scintillator. The large PMTs are sensitive and easily affected such that the detection efficiency loses about 60$%$ under the geomagnetic field intensity ($sim$500 mG). It has a significantly negative effect on the detector performance, and a compensation system is necessary for geomagnetic field shielding. As permalloys are easily rusted in water, a better way for the geomagnetic shielding is to apply an active compensation coils system. The simulations show that a set of 32 circular coils can meet the experiment requirement. The residual magnetic field is less than 0.05 G in the Central Detector Photomultiplier Tube (CD-PMT) region (38.5-39.5 m in diameter). A prototype coil system with a 1.2 m was built to validate the simulation and the design. The measured data of prototype and simulation results are consistent with each other, and geomagnetic field intensity is effectively reduced by coils, verifying the shielding coils system design for JUNO. This study is expected to provide practical guidance for the PMT magnetic field shielding for future large-scale detector designs.
rate research
Read More
A Guide Tube Calibration System (GTCS) has been designed for the Jiangmen Underground Neutrino Observatory (JUNO), in order to measure the detector energy response near the outer radius of the active volume. Recently, a prototype system has been constructed and tested, and the calibration algorithm has also been studied to evaluate the risk when the simulation tuning and the error control fail. In this paper, we first report its construction and the performance tests in the lab. Then the influence on the global energy measurement caused by the simulation bias of GTCS is discussed, in order to make sure the algorithm is qualified.
Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine the neutrino mass hierarchy using a 20 kton liquid scintillator detector. To calibrate detector boundary effect, the Guide Tube Calibration System (GTCS) has been designed to deploy a radioactive source along a given longitude on the outer surface of the detector. In this paper, we studied the physics case of this system via simulation, which leads to a mechanical design.
The Jiangmen Underground Neutrino Observatory (JUNO) is a medium-baseline neutrino experiment under construction in China, with the goal to determine the neutrino mass hierarchy. The JUNO electronics readout system consists of an underwater front-end electronics system and an outside-water back-end electronics system. These two parts are connected by 100-meter Ethernet cables and power cables. The back-end card (BEC) is the part of the JUNO electronics readout system used to link the underwater boxes to the trigger system is connected to transmit the system clock and triggered signals. Each BEC is connected to 48 underwater boxes, and in total around 150 BECs are needed. It is essential to verify the physical layer links before applying real connection with the underwater system. Therefore, our goal is to build an automatic test system to check the physical link performance. The test system is based on a custom designed FPGA board, in order to make the design general, only JTAG is used as the interface to the PC. The system can generate and check different data pattern at different speeds for 96 channels simultaneously. The test results of 1024 continuously clock cycles are automatically uploaded to PC periodically. We describe the setup of the automatic test system of the BEC and present the latest test results.
A study on the use of a machine learning algorithm for the level 1 trigger decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino experiment in construction in China, with the main goal of determining the neutrino mass hierarchy. A large liquid scintillator (LS)volume will detect the electron antineutrinos issued from nuclear reactors. The LS detector is instrumented by around 20000 large photomultiplier tubes. The hit information from each PMT will be collected into a center trigger unit for the level 1 trigger decision. The current trigger algorithm used to select a neutrino signal event is based on a fast vertex reconstruction. We propose to study an alternative level 1 (L1) trigger in order to achieve a similar performance as the vertex fitting trigger but with less logic resources by using firmware implemented machine learning model at the L1 trigger level. We treat the trigger decision as a classification problem and train a Multi-Layer Perceptron (MLP)model to distinguish the signal events with an energy higher than a certain threshold from noise events. We use JUNO software to generate datasets which include 100K physics events with noise and 100K pure noise events coming from PMT dark noise.For events with energy higher than 100 keV, the L1 trigger based on the converged MLP model can achieve an efficiency higher than 99%. After the training performed on simulations,we successfully implemented the trained model into a Kintex 7FPGA. We present the technical details of the neural network development and training, as well as its implementation in the hardware with the FPGA programming. Finally the performance of the L1 trigger MLP implementation is discussed.
The Jiangmen Underground Neutrino Observatory (JUNO), a multi-purpose neutrino experiment, will use 20 kt liquid scintillator (LS). To achieve the physics goal of determining the neutrino mass ordering, 3$%$ energy resolution at 1 MeV is required. This puts strict requirements on the LS light yield and the transparency. Four LS purification steps have been designed and mid-scale plants have been built at Daya Bay. To examine the performance of the purified LS and find the optimized LS composition, the purified LS was injected to the antineutrino detector 1 in the experimental hall 1 (EH1-AD1) of the Daya Bay neutrino experiment. To pump out the original gadolinium loaded LS and fill the new LS, a LS replacement system has been built in EH1 in 2017. By replacing the Gd-LS with purified water, then replacing the water with purified LS, the replacement system successfully achieved the designed goal. Subsequently, the fluorescence and the wavelength shifter were added to higher concentrations via the replacement system. The data taken at various LS compositions helped JUNO determine the final LS cocktail. Details of the design, the construction, and the operation of the replacement system are reported in this paper.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
