Nuclear Responses with Neural-Network Quantum States.

Area of Science:

• Quantum Many-Body Physics
• Computational Physics
• Nuclear Physics

Background:

• Studying dynamical properties of quantum systems is computationally challenging.
• Accurate theoretical predictions require robust methods and uncertainty quantification.
• Photoabsorption cross sections of light nuclei are important for nuclear structure studies.

Purpose of the Study:

• To introduce a novel variational Monte Carlo framework for quantum many-body systems.
• To compute dynamical properties, specifically photoabsorption cross sections, of self-bound systems.
• To validate the framework using light nuclei and compare with existing benchmarks.

Main Methods:

• Combining neural-network quantum states with the Lorentz integral transform technique.
• Utilizing a variational Monte Carlo approach in continuous Hilbert spaces.
• Employing a leading-order pionless effective field theory (EFT) expansion for the nuclear Hamiltonian.

Main Results:

• Accurate theoretical predictions for the photoabsorption cross section of light nuclei.
• Robust uncertainty quantification for theoretical results.
• Demonstration that a simple nuclear Hamiltonian provides reliable photoabsorption predictions.

Conclusions:

• The developed framework is broadly applicable to various quantum systems.
• The method provides accurate and reliable predictions for nuclear photoabsorption.
• The study validates the use of a specific nuclear Hamiltonian for dynamical properties.