Neutron-Proton Scattering Phase Shifts in S-Channel using Phase Function Method for Various Two Term Potentials

Khachi, Anil; Kumar, Lalit; Sastri, O. S. K. S.

doi:10.15415/jnp.2021.91015

Cited by 6 publications

(3 citation statements)

References 27 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After introducing the neural network model through a simple example, let us proceed to the main application of the inversion method, which is the determination of the scattering potential in low-energy elastic S-wave N N scattering from the experimental phase shifts. In particular, we are interested in the 3 S 1 channel, in which case one bound state, the deuteron, appears in the measured phase shifts [55,56]. In the The red markers represent the values at the inversion points, while the black lines are the true potentials identification process, there are no restrictions to the number of possible bound states, however, knowing apriori the number of bound states, it could be possible to restrict the training samples to only consist potentials, which can give a specific number of bound states.…”

Section: Estimating the 3 S 1 N N Scattering Potential With Neural Ne...mentioning

confidence: 99%

Estimating scattering potentials in inverse problems with Volterra series and neural networks

Balassa

2022

Eur. Phys. J. A

View full text Add to dashboard Cite

Inverse problems often occur in nuclear physics, when an unknown potential has to be determined from the measured cross sections, phase shifts or other observables. In this paper, a data-driven numerical method is proposed to estimate the scattering potentials, using data, that can be measured in scattering experiments. The inversion method is based on the Volterra series representation, and is extended by a neural network structure to describe problems, which require a more robust estimation. The Volterra series method is first used to describe the one-dimensional scattering problem, where the transmission coefficients, and the phase shifts are used as inputs to determine the unknown potentials in the Fourier domain. In the second example the scattering process described by the radial Schrödinger equation is used to estimate the scattering potentials from the energy dependence of the phase shifts, where neural networks are used to describe the scattering problem. At the end, to show the capabilities of the proposed models, real-life data is used to estimate the $${}_{}^3 S_1$$ 3 S 1 NN potential with the neural network approach from measured phase shifts, where a few percent relative match is obtained between the measured values and the model calculations.

show abstract

Section: Estimating the 3 S 1 N N Scattering Potential With Neural Ne...mentioning

confidence: 99%

Estimating scattering potentials in inverse problems with Volterra series and neural networks

Balassa

2022

Eur. Phys. J. A

View full text Add to dashboard Cite

show abstract

“…Interestingly, many simple phenomenological forms such as Square well, Malfiet-Tjon [7], Hulthen [8], have also been utilised for studying the deuteron problem. Recently, molecular potentials such as Manning-Rosen [9], Morse [10] and Deng-Fan [11] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Inverse potentials for all ℓ channels of neutron-proton scattering using reference potential approach

et al. 2023

Self Cite

View full text Add to dashboard Cite

Reference potential approach (RPA) is successful in obtaining inverse potentials for weakly bound diatomic molecules using Morse function. In this work, our goal is to construct inverse potentials for all available l-channels of np-scattering using RPA. The Riccati-type phase equations for various l-channels are solved using 5th order Runge-Kutta method to obtain scattering phase shifts (SPS) in tandem with an optimization procedure to minimize mean squared error (MSE). Interaction potentials for a total of 18 states have been constructed using only three parameter Morse interaction model. The obtained MSE is < 1% for 1S0, 3P1 and 3D1 channels and < 2% for 1P1 channel and < 0.1% for rest of the 14 channels. The obtained total scattering cross-sections at various lab energies are found to be matching well with experimental ones. Our complete study of np-scattering for all l-channels using RPA using Morse function as zeroth reference, is being undertaken for the ﬁrst time.

show abstract

“…Recently, our group has proposed an innovative way to utilise variational Monte-Carlo (VMC) technique to optimize model parameters [19] and applied it to various problems successfully [20][21][22]. In this paper, we extend it to obtain the model parameters in SEMF resulting from LDM.…”

Section: Introductionmentioning

confidence: 99%

Optimization of semi-empirical mass formula co-efficients using least square minimization and variational Monte-Carlo approaches

Gora¹,

Sastri²,

Soni³

2022

Eur. J. Phys.

Self Cite

View full text Add to dashboard Cite

In this paper, both Least Squares minimization (LSM) and Variational Monte-Carlo (VMC) techniques have been implemented to determine the co-efficients of Semi-Empirical Mass Formula (SEMF). First, the experimental binding energies (BEs) are determined for all the available nuclei from Atomic Mass Evaluation (AME2016) data. Then, LSM technique is implemented in Gnumeric worksheet to obtain the SEMF co-efficients by considering only the first three co-efficients which are deduced from Liquid Drop Model (LDM). The mean squared error (MSE) value, between obtained BEs from the optimized co-efficients and the experimental BEs, has been determined. Then, to emphasize the relevance of empirical terms, they have been introduced successively one after other and the procedure is repeated. A reduction in MSE-value has been observed after each iteration. This same procedure has also been employed using Monte-Carlo approach to obtain SEMF co-efficients by minimizing MSE-value as in variational principle. A comparative analysis has shown that the optimized parameters using VMC have resulted in smaller MSE-value than that of LSM technique.

show abstract

Neutron-Proton Scattering Phase Shifts in S-Channel using Phase Function Method for Various Two Term Potentials

Cited by 6 publications

References 27 publications

Estimating scattering potentials in inverse problems with Volterra series and neural networks

Estimating scattering potentials in inverse problems with Volterra series and neural networks

Inverse potentials for all ℓ channels of neutron-proton scattering using reference potential approach

Optimization of semi-empirical mass formula co-efficients using least square minimization and variational Monte-Carlo approaches

Contact Info

Product

Resources

About