Related papers: Generator coordinate method for hypernuclear spect…
A recent line of work has focused on the use of low-density generator matrix (LDGM) codes for lossy source coding. In this paper, wedevelop a generic technique for deriving lower bounds on the rate-distortion functions of binary linear…
The numerical generation of random quantum states (RQS) is an important procedure for investigations in quantum information science. Here we review some methods that may be used for performing that task. We start by presenting a simple…
The measurement of nuclear generalized parton distributions (GPDs) in hard exclusive processes, such as deeply virtual Compton Scattering (DVCS), will be one of the main achievements of a new generation of experiments at high luminosity,…
We have made a new evaluation of the Lambda decay width in nuclear matter within the Propagator Method. Through the Local Density Approximation it is possible to obtain results in finite nuclei. We have also studied the dependence of the…
Single--particle spectra of $\Lambda $ and $\Sigma $ hypernuclei are calculated within a relativistic mean--field theory. The hyperon couplings used are compatible with the $\Lambda $ binding in saturated nuclear matter, neutron-star masses…
We analyze the invariant mass spectrum of Lambda-Lambda in $^{12}C(K^-,K^+ Lambda Lambda)$ reaction at P(K^+)=1.65 GeV/c by using a combined framework of IntraNuclear Cascade (INC) model and the correlation function technique. The observed…
We propose a novel method for producing very-neutron-rich hypernuclei and corresponding resonance states by employing charge-exchange reactions via pp($^{12}$C, $^{12}$N $K^+$)n$\Lambda$ with single-charge-exchange and ppp($^{9}$Be, $^{9}$C…
We have studied electron correlations in the doped two-dimensional (2D) Hubbard model by using the coupled-cluster method (CCM) to investigate whether or not the method can be applied to correct the independent particle approximations…
The recent emergence of deep learning has led to a great deal of work on designing supervised deep semantic segmentation algorithms. As in many tasks sufficient pixel-level labels are very difficult to obtain, we propose a method which…
A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to…
The nuclear energy density functional method at finite temperature is a useful tool for studies of nuclear structure at high excitation, and also for researches of nuclear matter involved in explosive stellar phenomena and neutron stars.…
The density dependent relativistic hadron field theory is used to describe single particle properties of Lambda hypernuclei. The discussion focuses on the spin-orbit systematics in the relativistic mean-field formalism by discussing general…
The density matrix renormalization group method is applied to obtain the ground state phase diagram of the single impurity Anderson model on the honeycomb lattice at half filling. The calculation of local static quantities shows that the…
Collective quadrupole and octupole states are described in a series of Sm and Gd isotopes within the framework of the interacting boson model (IBM), whose Hamiltonian parameters are deduced from mean field calculations with the Gogny energy…
${\bf Background:}$ Weakly bound and unbound nuclear states appearing around particle thresholds are prototypical open quantum systems. Theories of such states must take into account configuration mixing effects in the presence of strong…
A new method to calculate level densities for non-interacting Fermions within the constant-spacing model with a finite number of states is developed. We show that asymptotically (for large numbers of particles or holes) the densities have…
We present a machine-learning method for the nonparametric extraction of generalized parton distributions (GPDs) from Compton form factors (CFFs) constrained by experimental data. The method addresses the longstanding inverse problem posed…
We present the Incremental Generative Monte Carlo (IGMC) method, designed to measure uncertainty in deep neural networks using deep generative approaches. IGMC iteratively trains generative models, adding their output to the dataset, to…
Auxiliary Field Diffusion Monte Carlo (AFDMC) calculations have been employed to revise the interaction between $\Lambda$-hyperons and nucleons in hypernuclei. The scheme used to describe the interaction, inspired by the phenomenological…
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology. In this work we predict high resolution dark matter halos from large scale, low…