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This work extends previous results on the inverse scattering problem within the framework of Marchenko theory (fixed-$l$ inversion). In particular, I approximate an $n$-channel $S$-matrix as a function of the first-channel momentum $q$ by a…

Quantum Physics · Physics 2026-02-17 N. A. Khokhlov

We present a generalization of the algebraic method for solving the Marchenko equation (fixed-$l$ inversion) for any values of the orbital angular momentum $l$. We expand the Marchenko equation kernel in a separable form using a triangular…

Quantum Physics · Physics 2021-12-30 N. A. Khokhlov

We extend our previous results of solving the inverse problem of quantum scattering theory (Marchenko theory, fixed-$l$ inversion). In particular, we apply an isosceles triangular-pulse function set for the Marchenko equation input kernel…

Nuclear Theory · Physics 2025-03-18 N. A. Khokhlov

We present a new algebraic method for solving the inverse problem of quantum scattering theory based on the Marchenko theory. We applied a triangular wave set for the Marchenko equation kernel expansion in a separable form. The separable…

Quantum Physics · Physics 2021-07-07 N. A. Khokhlov , L. I. Studenikina

We present a new algebraic method for solving the inverse problem of quantum scattering theory based on the Marchenko theory. We applied a triangular wave set for the Marchenko equation kernel expansion in a separable form. The separable…

Quantum Physics · Physics 2021-02-03 N. A. Khokhlov

The numerical algorithm of the inverse quantum scattering is developed. This algorithm is based on the Marchenko theory, and includes three steps. The first one is the algebraic Pade approximation of the unitary S-matrix, what is realized…

Nuclear Theory · Physics 2007-05-23 N. A. Khokhlov , V. A. Knyr

We present a novel method to estimate the stability of the Marchenko equation for finite data-sets. We show that we can derive a recursion relationship for the Fourier expansion coefficients of the kernel which is solved by the Marchenko…

Mathematical Physics · Physics 2007-05-23 H. J. S. Dorren

We apply the Marchenko method of quantum inverse scattering to study nucleon scattering problems. Assuming a $\beta/r^2$ type repulsive core and comparing our results to the Reid93 phenomenological potential we estimate the constant…

Nuclear Theory · Physics 2019-05-21 Mahmut Elbistan , Pengming Zhang , Janos Balog

Extending our previous results on trans-Planckian ($Gs \gg \hbar$) scattering of light particles in quantum string-gravity we present a calculation of the corresponding S-matrix from the region of large impact parameters ($b \gg…

High Energy Physics - Theory · Physics 2008-11-26 D. Amati , M. Ciafaloni , G. Veneziano

It is shown that the spectral points (bound states and resonances) generated by a central potential of a single-channel problem, can be found using rational parametrization of the S-matrix. To achieve this, one only needs values of the…

Nuclear Theory · Physics 2015-05-30 S. A. Rakityansky , S. A. Sofianos , N. Elander

We calculate the equation of state of nuclear matter based on the general analysis of the grand canonical partition function in the $S$-matrix framework. In addition to the low mass stable particles and their two-body scattering channels…

Nuclear Theory · Physics 2008-11-26 S. Mallik , J. N. De , S. K. Samaddar , Sourav Sarkar

A new method is proposed for fitting non-relativistic binary-scattering data and for extracting the parameters of possible quantum resonances in the compound system that is formed during the collision. The method combines the well-known…

Quantum Physics · Physics 2025-04-17 P. Vaandrager , M. L. Lekala , S. A. Rakityansky

The Marchenko method is developed in the inverse scattering problem for a linear system of first-order differential equations containing potentials proportional to the spectral parameter. The corresponding Marchenko system of integral…

Mathematical Physics · Physics 2022-03-08 T. Aktosun , R. Ercan

A system of linear integral equations is presented, which is the analog of the system of Marchenko integral equations, to solve the inverse scattering problem for the linear system associated with the derivative NLS equations. The…

Exactly Solvable and Integrable Systems · Physics 2023-07-19 Tuncay Aktosun , Ramazan Ercan , Mehmet Unlu

We develop a quasi-normal mode theory (QNMT) to calculate a system's scattering $S$ matrix, simultaneously satisfying both energy conservation and reciprocity even for a small truncated set of resonances. It is a practical reduced-order…

Applied Physics · Physics 2021-09-28 Mohammed Benzaouia , John D. Joannopoulos , Steven G. Johnson , Aristeidis Karalis

The problem of the Hamiltonian matrix in the oscillator and Laguerre basis construction from the S-matrix is treated in the context of the algebraic analogue of the Marchenko method.

Quantum Physics · Physics 2009-11-10 S. A. Zaytsev

A new approach is proposed to the solution of the quantum mechanical inverse scattering problem at fixed energy. The method relates the fixed energy phase shifts to those arising in an auxiliary Sturm-Liouville problem via the interpolation…

Mathematical Physics · Physics 2013-05-27 Tamas Palmai , Barnabas Apagyi

We analyze further a recently proposed S-matrix description of transplanckian scattering in the specific case of axisymmetric collisions of extended sources, where some of the original approximations are not necessary. We confirm the claim…

High Energy Physics - Theory · Physics 2014-11-18 G. Veneziano , J. Wosiek

The analytic structure of the S-matrix of singular quantum mechanics is examined within a multichannel framework, with primary focus on its dependence with respect to a parameter ($\Omega$) that determines the boundary conditions.…

Quantum Physics · Physics 2015-06-15 Horacio E. Camblong , Luis N. Epele , Huner Fanchiotti , Carlos A. Garcia Canal

We propose a new technique for constructing low-rank approximations of matrices that arise in kernel methods for machine learning. Our approach pairs a novel automatically constructed analytic expansion of the underlying kernel function…

Machine Learning · Computer Science 2022-02-09 John Paul Ryan , Anil Damle
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