FPGA implementation of neural network accelerator for pulse information extraction in high energy physics

Chen, Junling; Ai, Pengcheng; Wang, Hui; Fang, Ni; Xu, Deli; Gong, Qi; Yang, Yuan-Kang

doi:10.1007/s41365-020-00756-z

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…It achieved significantly better results than curve fitting. The signal processing neural network was validated by a field programmable gate array [13] and an application specific integrated circuit (ASIC) [14]. Furthermore in ref.…”

Section: Jinst 17 P02032mentioning

confidence: 99%

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

Ai¹,

Deng²,

Wang³

et al. 2022

J. Inst.

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Characterizing uncertainty is a common issue in nuclear measurement and has important implications for reliable physical discovery. Traditional methods are either insufficient to cope with the heterogeneous nature of uncertainty or inadequate to perform well with unknown mathematical models. In this paper, we propose using multi-layer convolutional neural networks for empirical uncertainty estimation and feature extraction of nuclear pulse signals. This method is based on deep learning, a recent development of machine learning techniques, which learns the desired mapping function from training data and generalizes to unseen test data. Furthermore, ensemble learning is utilized to estimate the uncertainty originating from trainable parameters of the network and to improve the robustness of the whole model. To evaluate the performance of the proposed method, simulation studies, in comparison with curve fitting, investigate extensive conditions and show its universal applicability. Finally, a case study is made using data from a NICA-MPD electromagnetic calorimeter module exposed to a test beam at DESY, Germany. The uncertainty estimation method successfully detected out-of-distribution samples and also achieved good accuracy in time and energy measurements.

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Section: Jinst 17 P02032mentioning

confidence: 99%

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

Ai¹,

Deng²,

Wang³

et al. 2022

J. Inst.

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show abstract

“…Besides, one-dimensional CNNs have been applied to pulse timing for upgrades of calorimeters in ALICE experiment [9]. Digital logic of the neural network accelerator has been implemented in [10,19].…”

Section: Related Workmentioning

confidence: 99%

Neural network-featured timing systems for radiation detectors: performance evaluation based on bound analysis

Deng

Wang

et al. 2021

J. Inst.

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Waveform sampling systems are used pervasively in the design of front end electronics for radiation detection. The introduction of new feature extraction algorithms (eg. neural networks) to waveform sampling has the great potential to substantially improve the performance and enrich the capability. To analyze the limits of such algorithms and thus illuminate the direction of resolution optimization, in this paper we systematically simulate the detection procedure of contemporary radiation detectors with an emphasis on pulse timing. Neural networks and variants of constant fraction discrimination are studied in a wide range of analog channel frequency and noise level. Furthermore, we propose an estimation of multivariate Cramér Rao lower bound within the model using intrinsic-extrinsic parametrization and prior information. Two case studies (single photon detection and shashlik-type calorimeter) verify the reliability of the proposed method and show it works as a useful guideline when assessing the abilities of various feature extraction algorithms.

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“…In recent years, with the development of deep learning, most scholars began to pay attention to its application in the nuclear field [5,6,7,8,9,10]. Chen Jun-Ling et al [11] used neural network to recover the saturated signal waveform obtained from high-energy particles. Islami rad et al [12] and Taheri et al [13] trained different neural networks to identify the position of ray interaction through energy spectrum.…”

Section: Introductionmentioning

confidence: 99%

Research on restoration method of nuclear pulse current signal of semiconductor detector based on artificial neural networks

Zhang

Qin

Zhang

et al. 2021

J. Phys.: Conf. Ser.

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The charge pulse generated by semiconductor detector caused by nuclear event carries nuclide and nuclear reaction information, but the amplified charge pulse amplitude is obviously weak and the noise is so large. Aiming at the difficulty of obtaining the charge signal pulse generated by the detector, a method for recovering the nuclear pulse current signal of semiconductor detector is proposed. Pulse recovery is divided into two parts: pulse shape recovery and pulse amplitude recovery. Point at the pulse shape, a shape recognition network of nuclear pulse current signal based on deep learning is proposed. For pulse amplitude,it can be obtained by Mexican straw hat wavelet forming algorithm. This algorithm can eliminate the baseline fluctuation caused by pulse stacking. The proposed shape recognition network of nuclear pulse current signal is composed of classifier and regressor. The classifier is used to judge whether the data contains a complete rising edge. The data containing the complete rising edge is sent to the regressor for prediction, so as to obtain the parameters related to the current pulse shape. The precision, recall and F-Measure of the classifier in classifying the test set are 98.88%, 98.05% and 98.33%, respectively. The average absolute error of the regressor in predicting the parameters related to the current pulse shape is about 9 ns. The experimental results show that the proposed method can recover the shape and amplitude of the current signal.

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FPGA implementation of neural network accelerator for pulse information extraction in high energy physics

Cited by 11 publications

References 20 publications

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

Neural network-featured timing systems for radiation detectors: performance evaluation based on bound analysis

Research on restoration method of nuclear pulse current signal of semiconductor detector based on artificial neural networks

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