Bulletin of the American Physical Society
APS April Meeting 2023
Volume 68, Number 6
Minneapolis, Minnesota (Apr 15-18)
Virtual (Apr 24-26); Time Zone: Central Time
Session N15: Nuclear Theory |
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Sponsoring Units: DNP Chair: Dean Lee, Michigan State University Room: Marquette VI - 2nd Floor |
Monday, April 17, 2023 1:30PM - 1:42PM |
N15.00001: Abstract Withdrawn
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Monday, April 17, 2023 1:42PM - 1:54PM |
N15.00002: Ab initio computations of deformed odd-mass nuclei Zhonghao Sun, Thomas Papenbrock, Gaute Hagen Coupled cluster theory has been successful in describing spherical nuclei because of its polynomial scaling in computation and its capability to capture the correlation energy. To calculate the open-shell nuclei, we start from a deformed mean field, and the broken rotational symmetry is restored by angular-momentum projection. In an odd mass nucleus, the unpaired nucleon informs us about single-particle states. We validate our approach in 9Be and 21Ne and calculate rotational bands of odd-mass nuclei in the first island of inversion. |
Monday, April 17, 2023 1:54PM - 2:06PM |
N15.00003: Generative modeling for the nucleon-nucleon interaction Pengsheng Wen, Jeremy W Holt, Maggie Li Developing precision models for nuclear interactions remains one of the primary challenges in nuclear physics and is key to a better understanding of theoretical uncertainties for many nuclear many-body calculations. In recent years, Chiral Effective Theory has become the most widely used framework for constructing nuclear potentials due to its ability to systematically generate contributions order-by-order in a well-defined expansion parameter. However, the uncertainties related to the truncation of order in the chiral expansion and the choice of regulator cutoff have not been fully estimated. To address this issue, an effective method to generate a range of chiral potentials with different truncations and cutoffs is required. We propose the application of the Glow model, a generative machine learning model which can learn the properties of a set of data and generate new samples for the nuclear interaction model. Our results show that a well-trained Glow model can accurately reproduce the original interactions used for training and in addition generate realistic nuclear potentials at different momentum-space cutoffs that reproduce very well nucleon-nucleon scattering phase shifts. |
Monday, April 17, 2023 2:06PM - 2:18PM |
N15.00004: Efimov Effect in Relativistic Three Body Integral Equations Md Habib E Islam, Sebastian M Dawid, Raul A Briceno Relativistic three-body integral equations have important applications in connecting finite volume (Lattice QCD) observables to infinite volume physical observables. In this work, we numerically solve the relativistic three-body integral equations for a system of three identical scalar bosons. The two-body sub-channel of the underlying three-body system is defined by the leading order effective range expansion, i.e. scattering length, and can form two-body bound states. We determine the three-body bound states by solving the integral equations for energies below three particle threshold. In the large scattering length limit, we find a series of three-body bound states. In this limit, the ratio of the binding energies of two consecutive bound states follows Efimov's prediction. Although these binding energies are model dependent, we show that their ratios are not, hence a universal property of the defined three-body system. We also calculate the three-body bound state wavefunctions and compare them to non-relativistic quantum mechanical predictions. |
Monday, April 17, 2023 2:18PM - 2:30PM |
N15.00005: Neutrino absorption mean free path in hot and dense nuclear matter within the random phase approximation Eunkyoung Shin, Ermal Rrapaj, Jeremy W Holt, Sanjay K Reddy The neutrino absorption mean free path in warm and dilute nuclear matter is derived from response functions calculated within the random phase approximation starting from realistic chiral effective field theory nuclear potentials. For comparison we discuss also the density and spin response functions including mean field and vertex corrections at only first order in perturbation theory. Under certain conditions, the first-order perturbative response functions lead to unphysical neutrino absorption cross sections, while in contrast the RPA diagrammatic resummation always produces physical cross sections. In general, the RPA vertex corrections act opposite to nuclear mean fields and lead to an overall increase in the neutrino mean free path compared to the noninteracting response. |
Monday, April 17, 2023 2:30PM - 2:42PM |
N15.00006: Medium-induced coherent gluon radiation at collider and cosmic ray energies Greg Jackson, François Arleo, Stéphane Peigné In high energy proton-nucleus collisions, hadron production is significantly affected by induced fully coherent gluon radiation. This energy loss mechanism stems from an interference between initial and final state color configurations for the underlying partonic subprocess. Quenching of heavy and light mesons due solely to this effect is important at LHC energies and quantitatively (at least) on par with expected suppression from nuclear modifications to parton distribution functions. As a further application, we study collisions of cosmic rays with light nuclei in the Earth's atmosphere and find a sizeable reduction in the corresponding neutrino flux induced by semileptonic decays. |
Monday, April 17, 2023 2:42PM - 2:54PM |
N15.00007: Trimmed Sampling Algorithm for the Noisy Generalized Eigenvalue Problem Caleb R Hicks, Dean J Lee The generalized eigenvalue problem is a useful method for finding energy eigenstates of large quantum systems. It uses projection onto a set of basis states which are typically not orthogonal. One needs to invert a matrix whose entries are inner products of the basis states, and the process is unfortunately susceptible to even small errors. The problem is especially bad when matrix elements are evaluated using stochastic methods and have significant error bars. In this work, we introduce the trimmed sampling algorithm in order to solve this problem. Using the framework of Bayesian inference, we sample prior probability distributions determined by uncertainty estimates of the various matrix elements and likelihood functions composed of physics-informed constraints. The result is a probability distribution for the eigenvectors and observables which automatically comes with a reliable estimate of the error and performs far better than standard regularization methods. The method should have immediate use for a wide range of applications involving classical and quantum computing calculations of large quantum systems. |
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