High-energy nuclear physics meets machine learning

He, W.; Ma, Yugang; Pang, Long-Gang; Song, Huichao; Zhou, Kai

doi:10.1007/s41365-023-01233-z

Cited by 40 publications

(4 citation statements)

References 168 publications

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“…The first and second stages constitute the FE part, the third stage makes the BE part, and the fourth stage contains the DAQ software. The first stage contains 3122 ASICs [491], which include 2844 MuPix [496,497] sensors and 278 MuTRiGs [498]. The second stage consists of 114 front-end boards, which receive the data from the first stage via LVDS links with a speed up to 1.25 Gbps.…”

Section: Trigger-less Readout Systemmentioning

confidence: 99%

Advances in nuclear detection and readout techniques

He,

Niu,

Wang

et al. 2023

NUCL SCI TECH

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Abstract“A Craftsman Must Sharpen His Tools to Do His Job,” said Confucius. Nuclear detection and readout techniques are the foundation of particle physics, nuclear physics, and particle astrophysics to reveal the nature of the universe. Also, they are being increasingly used in other disciplines like nuclear power generation, life sciences, environmental sciences, medical sciences, etc. The article reviews the short history, recent development, and trend of nuclear detection and readout techniques, covering Semiconductor Detector, Gaseous Detector, Scintillation Detector, Cherenkov Detector, Transition Radiation Detector, and Readout Techniques. By explaining the principle and using examples, we hope to help the interested reader underst and this research field and bring exciting information to the community.

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Section: Trigger-less Readout Systemmentioning

confidence: 99%

Advances in nuclear detection and readout techniques

He,

Niu,

Wang

et al. 2023

NUCL SCI TECH

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“…The recently developed machine-learning (ML) or artificial intelligence (AI)-driven technologies provide a way out. The ML methods have already earned credit in the fields of big data analysis due to their advantages of efficiency and adaptivity (Krizhevsky et al 2012;Jordan & Mitchell 2015;LeCun et al 2016;Li et al 2019b), and they have already been applied in many different fields of physics, e.g., Abraham et al (2018), Mehta et al (2019), Niu et al (2019), Gu et al (2020), Brady et al (2021), Boehnlein et al (2022), He et al (2023aHe et al ( , 2023b, Oala et al (2023), and references therein.…”

Section: Introductionmentioning

confidence: 99%

Insights into Neutron Star Equation of State by Machine Learning

Guo,

Xiong,

et al. 2024

ApJ

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Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with respect to the properties of nuclear matter at saturation density and the properties of neutron stars. It is found that the neural network is able to give reasonable predictions of parameter space and provide new hints into the constraints of hadron interactions. As a specific example, we take the relativistic mean field approximation in a widely accepted Walecka-type model to illustrate the feasibility and efficiency of the platform. The results show that the neural network can indeed estimate the parameters of the model at a certain precision such that both the properties of nuclear matter around saturation density and global properties of neutron stars can be saturated. The optimization of the present modularly designed neural network and extension to other effective models is straightforward.

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“…[38], we developed a CME-meter based on convolutional neural networks (CNNs) (for reviews of deep learning techniques applied to nuclear physics, see Refs. [32][33][34][35]). After training this CME-meter with AMPT-generated data simulating CME (introducing an initial charge separation into the AMPT model [36]) for Au + Au collisions at 200 GeV, the CME-meter demonstrated exceptional robustness in distinguishing events with CME from those without.…”

Section: Introductionmentioning

confidence: 99%

Applying deep learning technique to chiral magnetic wave search*

Zhao 赵,

Huang 黄

2024

Chinese Phys. C

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The chiral magnetic wave (CMW) is a collective mode in quark-gluon plasma originated from the chiral magnetic effect (CME) and chiral separation effect. Its detection in heavy-ion collisions is challenging due to significant background contamination. In Ref.~\cite{Zhao:2021yjo}, we have constructed a neural network which can accurately identify the CME-related signal from the final-state pion spectra. In this paper, we generalize such a neural network to the case of CMW search. We show that, after a updated training, the neural network can effectively recognize the CMW-related signal. Additionally, we assess the performance of the neural network compared to other known methods for CMW search.

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High-energy nuclear physics meets machine learning

Cited by 40 publications

References 168 publications

Advances in nuclear detection and readout techniques

Advances in nuclear detection and readout techniques

Insights into Neutron Star Equation of State by Machine Learning

Applying deep learning technique to chiral magnetic wave search*

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