The study of a new time reconstruction method for MRPC read out by waveform digitizer

Abstract

The measurement of the K ± production in the Semi-Inclusive Deep Inelastic Scattering (SIDIS) can provide further knowledge about the structure of nucleon, and thus it is purposed in the Solenoidal Large Intensity Device(SoLID) at Jefferson Lab(JLab). In this experiment, the identification of the kaons is planed to be accomplished with the Multi-gap Resistive Plate Chambers(MRPC), and the requirement for the time resolution is around 20 ps. This is very challenging for the present MRPC systems (typical resolution 60 ps), while in this paper, it is proved that the performance can be improved largely if the signal waveform is obtained and analyzed with a neural network method. In a cosmic ray experiment, the time resolution of a 6-gap 0.25mm-thick MRPC reaches 36 ps with this method, and a even better performance is expected with a thinner MRPC.

A detailed study on the intrinsic time resolution of the future MRPC detector

“…Details of implementation and the main idea of this network can be found in Ref. [13]. The result is reasonable, because algorithmically, ToT is just one specific model which is designed by the scientists and is proved to be useful.…”

“…In this section, some typical MRPCs [2][3][4][5] that are or will be used in several high energy physics experiments including ALICE [16], CBM [17], STAR [18] and BESIII [19] are explored. RefMRPC, a very thin-gap detector [9] and 2 prototypes THU1 and THU2 assembled in our laboratory [13] are also included. Tab.1 shows the inner geometry and the working electric field of these detectors and Fig.2 shows the quantitative contributions from the sources mentioned above.…”

“…Recently, a new algorithm based on several sets of the neural networks has been proposed [13] to further improve the detector resolution. Theoretical analysis of the network is quite difficult, but the intrinsic performance of this method can be estimated and explained by the simulation, which is also included in this paper.…”

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

“…Recently, analog-to-digital converters (ADC) with hundreds of sampling rates come onto the stage of nuclear detectors because of their flexibility and property of information preservation [6][7][8][9][10]. It also provides a great opportunity to apply novel machine learning algorithms to traditional problems in nuclear instrumentation.…”

Improving the time resolution of the MRPC detector using deep-learning algorithms

“…Based on the new system, we proposed a time reconstruction algorithm using deep learning and neural networks. A combined long short term memory (ComLSTM) network which is an extension of our previous work [9,10] is designed and implemented to improve the MRPC time resolution from the perspective of the software. The simulation data used to train the network are optimized so that the most useful information is passed into the network and extracted by it.…”