arXiv subjects

Morten Hjorth-Jensen

Publications and source records attributed to Morten Hjorth-Jensen.

At least 37 records · Page 2Linked to original sources

arXiv2022

Solving the nuclear pairing model with neural network quantum states

We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values.

nucl-th↗

arXiv2022

Efficient Solutions of Fermionic Systems using Artificial Neural Networks

We discuss differences and similarities between variational Monte Carlo approaches that use conventional and artificial neural network parameterizations of the ground-state wave function for systems of fermions. We focus on a relatively shallow neural-network architectures, the so called restricted Boltzmann machine, and discuss unsupervised learning algorithms that are suitable to model complicated many-body correlations. We analyze the strengths and weaknesses of conventional and neural-network wave functions by solving various circular quantum-dots systems. Results for up to 90 electrons are presented and particular emphasis is placed on how to efficiently implement these methods on homogeneous and heterogeneous high-performance computing facilities.

cond-mat.mes-hall↗

arXiv2022

Machine Learning in Nuclear Physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

nucl-th↗

arXiv2022

Predicting Solid State Material Platforms for Quantum Technologies

Semiconductor materials provide a compelling platform for quantum technologies (QT), and the properties of a vast amount of materials can be found in databases containing information from both experimental and theoretical explorations. However, searching these databases to find promising candidate materials for quantum technology applications is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor host platforms for QT using material informatics and machine learning methods, resulting in a dataset consisting of over $25.000$ materials and nearly $5000$ physics-informed features. Three approaches were devised, named the Ferrenti, extended Ferrenti and the empirical approach, to label data for the supervised machine learning (ML) methods logistic regression, decision trees, random forests and gradient boosting. We find that of the three, the empirical approach relying exclusively on findings from the literature predicted substantially fewer candidates than the other two approaches with a clear distinction between suitable and unsuitable candidates when comparing the two largest eigenvalues in the covariance matrix. In contrast to expectations from the literature and that found for the Ferrenti and extended Ferrenti approaches focusing on band gap and ionic character, the ML methods from the empirical approach highlighted features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT. All three approaches and all four ML methods agreed on a subset of $47$ eligible candidates %(to a probability of $>50 \ \%$) of $8$ elemental, $29$ binary, and $10$ tertiary compounds, and provide a basis for further material explorations towards quantum technology.

cond-mat.mtrl-sci↗

arXiv2021

Hidden spin-isospin exchange symmetry

The strong interactions among nucleons have an approximate spin-isospin exchange symmetry that arises from the properties of quantum chromodynamics in the limit of many colors, $N_c$. However this large-$N_c$ symmetry is well hidden and reveals itself only when averaging over intrinsic spin orientations. Furthermore, the symmetry is obscured unless the momentum resolution scale is close to an optimal scale that we call $Λ_{{\rm large-}N_c}$. We show that the large-$N_c$ derivation requires a momentum resolution scale of $Λ_{{\rm large-}N_c} \sim 500$ MeV. We derive a set of spin-isospin exchange sum rules and discuss implications for the spectrum of $^{30}$P and applications to nuclear forces, nuclear structure calculations, and three-nucleon interactions.

nucl-th↗

arXiv2021

Unsupervised Learning for Identifying Events in Active Target Experiments

This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection Chamber (AT-TPC). The overarching goal is to group similar events in the early stages of the data analysis, thereby improving efficiency by limiting the computationally expensive processing of unnecessary events. The application of unsupervised clustering algorithms to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on $^{46}$Ar is introduced. We explore the performance of autoencoder neural networks and a pre-trained VGG16 convolutional neural network. We study clustering performance on both data from a simulated $^{46}$Ar experiment, and real events from the AT-TPC detector. We find that a $k$-means algorithm applied to simulated data in the VGG16 latent space forms almost perfect clusters. Additionally, the VGG16+$k$-means approach finds high purity clusters of proton events for real experimental data. We also explore the application of clustering the latent space of autoencoder neural networks for event separation. While these networks show strong performance, they suffer from high variability in their results.

cs.CV↗

arXiv2020

From bound states to the continuum

This white paper reports on the discussions of the 2018 Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program "From bound states to the continuum: Connecting bound state calculations with scattering and reaction theory". One of the biggest and most important frontiers in nuclear theory today is to construct better and stronger bridges between bound state calculations and calculations in the continuum, especially scattering and reaction theory, as well as teasing out the influence of the continuum on states near threshold. This is particularly challenging as many-body structure calculations typically use a bound state basis, while reaction calculations more commonly utilize few-body continuum approaches. The many-body bound state and few-body continuum methods use different language and emphasize different properties. To build better foundations for these bridges, we present an overview of several bound state and continuum methods and, where possible, point to current and possible future connections.

nucl-th↗

arXiv2020

Predicting time to graduation at a large enrollment American university

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

physics.ed-ph↗

arXiv2018

Integrating a Computational Perspective in Physics Courses

In this contribution we discuss how to develop a physics curriculum for undergraduate students that includes computing as a central element. Our contribution starts with a definition of computing and pertinent learning outcomes and assessment studies and programs. We end with a discussion on how to implement computing in various physics courses by presenting our experiences from Michigan State University in the USA and the University of Oslo in Norway.

physics.ed-ph↗

arXiv2017

Addition and removal energies of circular quantum dots

We present and compare several many-body methods as applied to two-dimensional quantum dots with circular symmetry. We calculate the approximate ground state energy using a harmonic oscillator basis optimized by Hartree-Fock (HF) theory and further improve the ground state energy using two post-HF methods: in-medium similarity renormalization group (IM-SRG) and coupled cluster with singles and doubles (CCSD). With the application of quasidegenerate perturbation theory (QDPT) or the equations-of-motion (EOM) method to the results of the previous two methods, we obtain addition and removal energies as well. Our results are benchmarked against full configuration interaction (FCI) and diffusion Monte Carlo (DMC) where available. We examine the rate of convergence and perform extrapolations to the infinite basis limit using a power-law model.

physics.chem-ph↗

arXiv2017

Exotic neutron-rich medium-mass nuclei with realistic nuclear forces

We present the first application of the newly developed EKK theory of the effective nucleon-nucleon interaction to shell-model studies of exotic nuclei, including those where conventional approaches with fitted interactions encounter difficulties. This EKK theory enables us to derive the interaction suitable for several major shells ($sd$+$pf$ in this work). By using such an effective interaction obtained from the Entem-Machleidt QCD-based $χ\mathrm{N}^3\mathrm{LO}$ interaction and the Fujita-Miyazawa three-body force, the energies, E2 properties and spectroscopic factors of low-lying states of neutron-rich Ne, Mg and Si isotopes are nicely described, as the first shell-model description of the "island of inversion" without fit of the interaction. The long-standing question as to how particle-hole excitations occur across the $sd$-$pf$ magic gap is clarified with distinct differences from the conventional approaches. The shell evolution is shown to appear similarly to earlier studies.

nucl-th↗

arXiv2016

Computational Nuclear Physics and Post Hartree-Fock Methods

We present a computational approach to infinite nuclear matter employing Hartree-Fock theory, many-body perturbation theory and coupled cluster theory. These lectures are closely linked with those of chapters 9, 10 and 11 and serve as input for the correlation functions employed in Monte Carlo calculations in chapter 9, the in-medium similarity renormalization group theory of dense fermionic systems of chapter 10 and the Green's function approach in chapter 11. We provide extensive code examples and benchmark calculations, allowing thereby an eventual reader to start writing her/his own codes. We start with an object-oriented serial code and end with discussions on strategies for porting the code to present and planned high-performance computing facilities.

nucl-th↗

arXiv2013

Multi-shell effective interactions

Background: Effective interactions, either derived from microscopic theories or based on fitting selected properties of nuclei in specific mass regions, are widely used inputs to shell-model studies of nuclei. Until recently, most shell-model calculations have been confined to a single oscillator shell. Recent interest in nuclei away from the stability line, requires however larger shell-model spaces. Since the derivation of microscopic effective interactions has been limited to degenerate model spaces, there are both conceptual and practical limits to present shell-model calculations that utilize such interactions. Purpose: The aim of this work is to present a novel microscopic method to calculate effective interactions for the nuclear shell model. Its main difference from existing theories is that it can be applied not only to degenerate model spaces but also to non-degenerate model spaces. Methods: The formalism is presented in the form of many-body perturbation theory based on the recently developed Extended Kuo-Krenciglowa method. Our method enables us to microscopically construct effective interactions not only in one oscillator shell but also for several oscillator shells. Results: We present numerical results using effective interactions within (i) a single oscillator shell like the sd-shell and the pf-shell, and (ii) two major shells like the sdf7p3-shell and the pfg9-shell. We also present energy levels of several nuclei which have two valence nucleons on top of a given closed-shell core. Conclusions: Our results show that the present method works excellently in shell-model spaces that comprise several oscillator shells, as well as in a single oscillator shell. We show in particular that the microscopic inter-shell interactions are much more attractive than has been expected by degenerate perturbation theory. The consequences for shell-model studies are discussed.

nucl-th↗

arXiv2011

Renormalization persistency of tensor force in nuclei

In this work we analyze the tensor-force component of effective interactions appropriate for nuclear shell-model studies, with particular emphasis on the monopole term of the interactions. Standard nucleon-nucleon ($NN$) interactions such as AV8' and $χ$N$^3$LO are tailored to shell-model studies by employing $V_{low k}$ techniques to handle the short-range repulsion of the $NN$ interactions and by applying many-body perturbation theory to incorporate in-medium effects. We show, via numerical studies of effective interactions for the $sd$ and $pf$ shells, that the tensor-force contribution to the monopole term of the effective interaction is barely changed by these renormalization procedures, resulting in almost the same monopole term as the one of the bare $NN$ interactions. We propose to call this feature {\it Renormalization Persistency} of the tensor force, as it is a remarkable property of the renormalization and should have many interesting consequences in nuclear systems. For higher multipole terms, this feature is maintained to a somewhat smaller extent. We present general intuitive explanations for the renormalization persistency of the tensor force, as well as analyses of core-polarization terms in perturbation theory. The central force does not exhibit a similar renormalization persistency.

nucl-th↗

arXiv2010

Renormalized interactions with a realistic single particle basis

Neutron-rich isotopes in the sdpf space with Z < 15 require modifications to derived effective interactions to agree with experimental data away from stability. A quantitative justification is given for these modifications due to the weakly bound nature of model space orbits via a procedure using realistic radial wavefunctions and realistic NN interactions. The long tail of the radial wavefunction for loosely bound single particle orbits causes a reduction in the size of matrix elements involving those orbits, most notably for pairing matrix elements, resulting in a more condensed level spacing in shell model calculations. Example calculations are shown for 36Si and 38Si.

nucl-th↗

arXiv2010

Configuration Interactions Constrained by Energy Density Functionals

A new method for constructing a Hamiltonian for configuration interaction calculations with constraints to energies of spherical configurations obtained with energy-density-functional (EDF) methods is presented. This results in a unified model that reproduced the EDF binding-energy in the limit of single-Slater determinants, but can also be used for obtaining energy spectra and correlation energies with renormalized nucleon-nucleon interactions. The three-body and/or density-dependent terms that are necessary for good nuclear saturation properties are contained in the EDF. Applications to binding energies and spectra of nuclei in the region above 208Pb are given.

nucl-th↗

arXiv2010

Orbital dependent nucleonic pairing in the lightest known isotopes of tin

By studying the 109Xe-->105Te-->101Sn superallowed alpha-decay chain, we observe low-lying states in 101Sn, the one-neutron system outside doubly magic 100Sn. We find that the spins of the ground state (J = 7=2) and first excited state (J = 5=2) in 101Sn are reversed with respect to the traditional level ordering postulated for 103Sn and the heavier tin isotopes. Through simple arguments and state-of-the-art shell model calculations we explain this unexpected switch in terms of a transition from the single-particle regime to the collective mode in which orbital-dependent pairing correlations, dominate.

nucl-ex↗

arXiv2009

Novel features of nuclear forces and shell evolution in exotic nuclei

Novel simple properties of the monopole component of the effective nucleon-nucleon interaction are presented, leading to the so-called monopole-based universal interaction. Shell structures are shown to change as functions of $N$ and $Z$ consistently with experiments. Some key cases of this shell evolution are discussed, clarifying the effects of central and tensor forces. The validity of the present tensor force is examined in terms of the low-momentum interaction V$_{low k}$ and the Q$_{box}$ formalism.

nucl-th↗