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.Comment: 6 page

Large-scale shell-model study of the Sn isotopes

We summarize the results of an extensive study of the structure of the Sn isotopes using a large shell-model space and effective interactions evaluated from realistic two-nucleon potentials. For a fuller account, see ref. [1]

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).Comment: 28 pages, 11 figure

Efficient solutions of fermionic systems using artificial neural networks

In this study, we explore the similarities and differences between variational Monte Carlo techniques that employ conventional and artificial neural network representations of the ground-state wave function for fermionic systems. Our primary focus is on shallow neural network architectures, specifically the restricted Boltzmann machine, and we examine unsupervised learning algorithms that are appropriate for modeling complex many-body correlations. We assess the advantages and drawbacks of conventional and neural network wave functions by applying them to a range of circular quantum dot systems. Our findings, which include results for systems containing up to 90 electrons, emphasize the efficient implementation of these methods on both homogeneous and heterogeneous high-performance computing facilities

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.Comment: 4 pages, 4 figure

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.Comment: 9 pages, 3 figure