Bayesian analysis on interactions of exotic nuclear systems

Yang, L.; Lin, C. J.; Zhang, Yingxun; Wen, Peiwei; Jia, H. M.; Wang, D.X.; Ma, Nan-Ru; Feng, Yan; Zhong, F. P.; Zhong, Shanhao; Luo, Tianpeng

doi:10.1016/j.physletb.2020.135540

Cited by 13 publications

(11 citation statements)

References 42 publications

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“…In Ref. [116], the Bayesian inference has been implemented to study the properties of the reaction systems with exotic nuclei, for instance, the gross feature of the phenomenological interaction potential. As shown in Figure 4, the results of Bayesian statistics strongly de-pend on the imposed prior distributions.…”

Section: Reaction Mechanism and Phase Transitionmentioning

confidence: 99%

Machine learning in nuclear physics at low and intermediate energies

He¹,

Li²,

Ma³

et al. 2023

Preprint

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Section: Reaction Mechanism and Phase Transitionmentioning

confidence: 99%

Machine learning in nuclear physics at low and intermediate energies

He¹,

Li²,

Ma³

et al. 2023

Preprint

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“…Markov chain Monte Carlo (MCMC) methods offer efficient sampling algorithms to systematically explore complex high-dimensional parameter spaces [21,22]. With the advent of modern computational power, the use of MCMC techniques to solve problems in the Bayesian framework is becoming increasingly important in nuclear physics, especially in studies of heavy-ion collisions [23][24][25][26][27][28][29] and low-energy nuclear reactions [30][31][32][33][34][35]. Despite the many recent applications mentioned above, to the best of our knowledge, no DSAM data have ever been analyzed within a Bayesian-MCMC framework.…”

Section: Introductionmentioning

confidence: 99%

First application of Markov Chain Monte Carlo-based Bayesian data analysis to the Doppler-Shift Attenuation Method

Sun¹,

Fry²,

Davids³

et al. 2022

Preprint

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Motivated primarily by the large uncertainties in the thermonuclear rate of the 30 P(p, γ) 31 S reaction that limit our understanding of classical novae, we carried out lifetime measurements of 31 S excited states using the Doppler Shift Lifetimes (DSL) facility at the TRIUMF Isotope Separator and Accelerator (ISAC-II) facility. The 31 S excited states were populated by the 3 He( 32 S, α) 31 S reaction. The deexcitation γ rays were detected by a clover-type high-purity germanium detector in coincidence with the α particles detected by a silicon detector telescope. We have applied modern Markov chain Monte Carlo-based Bayesian methods to perform lineshape analyses of Doppler-Shift Attenuation Method γ-ray data for the first time. We have determined the lifetimes for the two lowest-lying 31 S excited states. Upper limits on the previously unknown lifetimes of four higher-lying states have been obtained. The experimental results were compared to the shell-model calculations using five universal sd-shell Hamiltonians. The γ rays originating from the astrophysically important J π = 3/2 + , 260-keV 30 P(p, γ) 31 S resonance have also been observed, but the lifetime is expected to be better constrained with more statistics at the upgraded DSL2 facility.

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“…Bayesian inference is an appealing approach for dealing with theoretical uncertainties and has been applied in different nuclear physics studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In the practice of Bayesian analyses, a sampling procedure is usually inevitable for approximating the posterior probability distribution of model parameters and for performing predictive computations.…”

Section: Introductionmentioning

confidence: 99%

Bayesian probability updates using Sampling/Importance Resampling: Applications in nuclear theory

Jiang¹,

Forssén²

2022

Preprint

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We review an established Bayesian sampling method called sampling/importance resampling and highlight situations in nuclear theory when it can be particularly useful. To this end we both analyse a toy problem and demonstrate realistic applications of importance resampling to infer the posterior distribution for parameters of ∆NNLO interaction model based on chiral effective field theory and to estimate the posterior probability distribution of target observables. The limitation of the method is also showcased in extreme situations where importance resampling breaks.

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Bayesian analysis on interactions of exotic nuclear systems

Cited by 13 publications

References 42 publications

Machine learning in nuclear physics at low and intermediate energies

Machine learning in nuclear physics at low and intermediate energies

First application of Markov Chain Monte Carlo-based Bayesian data analysis to the Doppler-Shift Attenuation Method

Bayesian probability updates using Sampling/Importance Resampling: Applications in nuclear theory

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