arXiv subjects

Marco Knöll

Publications and source records attributed to Marco Knöll.

8 recordsLinked to original sources

arXiv2026

High-precision ab initio nuclear theory: Learning to overcome model-space limitations

High-precision predictions of nuclear properties are a central objective of ab initio nuclear structure theory. However, state-of-the-art many-body methods rely on truncated model spaces to render the nuclear many-body problem tractable, which remains a major source of theoretical error in computations of nuclear observables. In recent years, machine learning, and artificial neural network approaches in particular, have emerged as a powerful data-driven framework for learning convergence patterns directly from ab initio calculations and enabling precision extrapolations beyond the reach of conventional schemes. This review focuses on model-space extrapolation methods developed for the no-core shell model and related many-body methods. We discuss machine learning extrapolation frameworks in comparison to conventional methods and assess their performance for energy spectra, radii, and electromagnetic observables, with particular emphasis on achievable precision and uncertainty estimates through statistical and correlation-based strategies. These developments establish machine learning as an increasingly important component of the precision toolbox in ab initio nuclear theory, enhancing the reliability and predictive power of ab initio nuclear structure calculations.

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arXiv2025

Halo Structures in p-Shell Hypernuclei with Natural Orbitals

We extend the concept of natural orbitals as an optimized single-particle basis for ab initio nuclear many-body calculations to hypernuclei and show that their superior properties, in particular accelerated convergence and independence of the underlying harmonic-oscillator frequency, can be directly transferred to the hypernuclear regime as demonstrated in no-core shell model calculations for selected p-shell hypernuclei. Moreover, the radial single-particle wavefunctions associated with the natural-orbital basis yield important structural information with respect to the different particle species allowing us to identify a hyperon halo in ΛHe5. We further explore nucleonic and hyperonic halo structures in A=6 and A=7 singly-strange hypernuclei based on one-body densities and point-particle radii obtained from no-core shell model calculations with realistic interactions from chiral effective field theory.

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arXiv2025

Machine Learning for Correlations of Electromagnetic Properties in Ab Initio Calculations

In ab initio nuclear structure theory, accurately predicting electromagnetic observables, such as moments and transition rates, is essential for a comprehensive understanding of nuclear properties. However, computational limitations and conceptual difficulties often hinder the precise calculation of these observables. In this work, we extend machine learning methods for model-space extrapolations to electric quadrupole moments. We further present a new machine learning approach that leverages the correlations between energies, radii, and electromagnetic observables. By learning these correlations from no-core shell model calculations in accessible model spaces, this new model enables the prediction of converged electromagnetic observables from predictions of converged energies and radii, which can be obtained with established machine learning extrapolation tools. An essential property of our approach is the capability for uncertainty quantification, allowing for reliable predictions with combined statistical error estimates for many-body and interaction uncertainties. Being solely built upon the physical correlations of different observables, it can be generalized across different ab initio methods. We demonstrate the power of this new extrapolation scheme through a precision study of electric quadrupole moments across a wide range of p-shell nuclei.

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arXiv2025

Benchmarking ANN extrapolations of the ground-state energies and radii of Li isotopes

We present a comparison of model-space extrapolation methods for No-Core Shell Model calculations of ground-state energies and root-mean-square radii in Li isotopes. In particular, we benchmark the latest machine learning tools against widely used exponential and infrared extrapolations for energies and crossing point estimates for radii. Our findings demonstrate that machine learning-based approaches provide reliable predictions with robust statistical uncertainties for both observables even in small model spaces. These predictions are compatible with established exponential and IR extrapolations of energies and mark a notable improvement over conventional radius estimates.

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arXiv2025

High-Precision Ab Initio Radius Calculations of Boron Isotopes

We perform a precision study of radii in Boron isotopes for multiple realistic interactions from chiral effective field theory. We obtain predictions of radii with combined many-body and interaction uncertainty quantification from ab initio no-core shell model calculations together with machine learning extrapolation methods. An extension to radius differences further allows us to investigate a potential proton halo in $^{8}$B and, moreover, provide predictions that relate directly to the isotope shift, which can be precisely measured in experiments.

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arXiv2024

Precise neural network predictions of energies and radii from the no-core shell model

For light nuclei, ab initio many-body methods such as the no-core shell model are the tools of choice for predictive, high-precision nuclear structure calculations. The applicability and the level of precision of these methods, however, is limited by the model-space truncation that has to be employed to make such computations feasible. We present a universal framework based on artificial neural networks to predict the value of observables for an infinite model-space size based on finite-size no-core shell model data. Expanding upon our previous ansatz of training the neural networks to recognize the observable-specific convergence pattern with data from few-body nuclei, we improve the results obtained for ground-state energies and show a way to handle excitation energies within this framework. Furthermore, we extend the framework to the prediction of converged root-mean-square radii, which are more difficult due to the much less constrained convergence behavior. For all observables robust and statistically significant uncertainties are extracted via the sampling over a large number of network realizations and evaluation data samples.

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arXiv2023

Hyperon-Nucleon Interaction Constrained by Light Hypernuclei

Ab initio structure calculations for p-shell hypernuclei have recently become accessible through extensions of nuclear many-body methods, such as the no-core shell model, in combination with hyperon-nucleon interactions from chiral effective field theory. However, the low-energy constants in these hyperon-nucleon interactions are poorly constraint due to the very limited amount of experimental scattering data available. We present a hyperon-nucleon interaction that is additionally constrained by experimental ground-state and spectroscopic data for selected p-shell hypernuclei and, thus, optimized for hypernuclear structure calculations. We show that the previous overestimation of the hyperon separation energies in the p-shell is remedied and discuss the significantly improved description of the $_Λ$He isotopic chain. We further discuss the uncertainty quantification for hypernuclear observables on the many-body level, obtained through a novel machine-learning tool.

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arXiv2023

Machine Learning for the Prediction of Converged Energies from Ab Initio Nuclear Structure Calculations

The prediction of nuclear observables beyond the finite model spaces that are accessible through modern ab initio methods, such as the no-core shell model, pose a challenging task in nuclear structure theory. It requires reliable tools for the extrapolation of observables to infinite many-body Hilbert spaces along with reliable uncertainty estimates. In this work we present a universal machine learning tool capable of capturing observable-specific convergence patterns independent of nucleus and interaction. We show that, once trained on few-body systems, artificial neural networks can produce accurate predictions for a broad range of light nuclei. In particular, we discuss neural-network predictions of ground-state energies from no-core shell model calculations for 6Li, 12C and 16O based on training data for 2H, 3H and 4He and compare them to classical extrapolations.

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