arXiv subjects

Yongqi Tan

Publications and source records attributed to Yongqi Tan.

4 recordsLinked to original sources

arXiv2024

Design of High-speed readout electronics for the DarkSHINE electromagnetic calorimeter

The DarkSHINE experiment aims to search for dark photons by measuring the energy loss of the electrons recoiled from fixed-target. Its electromagnetic calorimeter is primarily responsible for accurately reconstructing the energy of the recoil electrons and bremsstrahlung photons. The performance of the electromagnetic calorimeter is crucial, as its energy measurement precision directly determines the sensitivity to the search for dark photons. The DarkSHINE electromagnetic calorimeter uses LYSO crystals to form a fully absorptive electromagnetic calorimeter. It utilizes SiPMs to detect scintillation light in the crystals, and its readout electronics system deduces the deposited energy in the crystals by measuring the number of photoelectric signals generated by the SiPMs. The DarkSHINE electromagnetic calorimeter aims to operate at an event rate of 1-10 MHz, detecting energies ranging from 1 MeV to 1 GeV. To meet the requirements of high energy measurement precision, high event rate, and large dynamic range, we have researched and designed a readout electronics system based on dual-channel high-speed ADCs and a customized DAQ. The front-end amplification part of this system uses low-noise trans-impedance amplifiers to achieve high-precision waveform amplification. It successfully achieves a dynamic range up to a thousandfold through a double-gain readout scheme. The digital part uses 1 GSPS high-speed ADCs to achieve non-dead-time, high-precision waveform digitization. The DAQ part uses JESD204B high-speed serial protocol to read out the signal from ADC, and transmit it to PC software for processing and storage. Test results show a signal-to-noise ratio greater than 66 dBFS and an ENOB greater than 10.6 bits. Energy spectra measurements have been conducted using LYSO crystals and SiPMs, and an energy resolution of 5.96% at the 2.6 MeV gamma peak of Th-232 has been achieved.

physics.ins-det↗

arXiv2024

Front-end electronics development of large-area SiPM arrays for high-precision single-photon time measurement

TRopIcal DEep-sea Neutrino Telescope (TRIDENT) plans to incorporate silicon photomultipliers (SiPMs) with superior time resolution in addition to photomultiplier tubes (PMTs) into its detection units, namely hybrid Digital Optical Modules (hDOMs), to improve its angular resolution. However, the time resolution significantly degrades for large-area SiPMs due to the large detector capacitance, posing significant challenges for the readout electronics of SiPMs in hDOM. We analyzed the influences of series and parallel connections when constructing a large-area SiPM array and designed a series-parallel connection SiPM array with differential output. We also designed a high-speed pre-amplifier based on transformers (MABA-007159) and radio frequency amplifiers (BGA2803), and an analog multi-channel summing circuit based on operational amplifiers (LMH6629). We measured the single photon time resolution (SPTR) of a $4\times4$ SiPM (Hamamatsu S13360-3050PE) array ($12\times12~\mathrm{mm}^2$) of approximately 300 ps FWHM. This front-end readout design enables the large-area SiPM array to achieve high-precision single photon time measurement in one readout channel.

physics.ins-det↗

arXiv2021

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining elemental compositions of the soil, crust and rocks. Two new lunched missions, Chinese Tianwen 1 and American Perseverance, will further increase the number of LIBS instruments on Mars after the planned landings in spring 2021. Such unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data treatment. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock composition leading to the chemical matrix effect, and the difference in morphology between laboratory standard samples (in pressed pellet, glass or ceramics) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matric effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standard samples offering a good representation of various compositions of Mars rocks. The present work deals with the physical matrix effect which is still expecting a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific case of total alkali-silica (TAS) classification of natural rocks, the results show a significant improvement of the prediction capacity of pellet sample-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct classification rate of rocks increases from 33.3% with a machine learning model to 83.3% with a transfer learning model.

physics.app-ph↗

arXiv2020

Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction

Reservoir computing is a relatively recent computational paradigm that originates from a recurrent neural network and is known for its wide range of implementations using different physical technologies. Large reservoirs are very hard to obtain in conventional computers, as both the computation complexity and memory usage grow quadratically. We propose an optical scheme performing reservoir computing over very large networks potentially being able to host several millions of fully connected photonic nodes thanks to its intrinsic properties of parallelism and scalability. Our experimental studies confirm that, in contrast to conventional computers, the computation time of our optical scheme is only linearly dependent on the number of photonic nodes of the network, which is due to electronic overheads, while the optical part of computation remains fully parallel and independent of the reservoir size. To demonstrate the scalability of our optical scheme, we perform for the first time predictions on large spatiotemporal chaotic datasets obtained from the Kuramoto-Sivashinsky equation using optical reservoirs with up to 50 000 optical nodes. Our results are extremely challenging for conventional von Neumann machines, and they significantly advance the state of the art of unconventional reservoir computing approaches, in general.

physics.optics↗