Bulletin of the American Physical Society
APS April Meeting 2022
Volume 67, Number 6
Saturday–Tuesday, April 9–12, 2022; New York
Session H12: Nuclear Theory IIRecordings Available

Hide Abstracts 
Sponsoring Units: DNP GDS GFB Chair: Maria Piarulli, Washington University, St. Louis Room: Shubert 
Sunday, April 10, 2022 10:45AM  10:57AM 
H12.00001: Emulating Observables from Chiral EFT Potentials Alberto J Garcia, Richard J Furnstahl, Xilin Zhang Bayesian nucleonnucleon (NN) uncertainty quantification (UQ) has proven useful in helping understand to what extent our models work, but the process is known to be computationally expensive when using direct calculation methods. Recently, the development of emulators has accelerated in lowenergy nuclear physics due to their accuracy and computational efficiency in predicting bound state and scattering observables for different potentials, making them a promising candidate for UQ. Of the currently available emulators, eigenvector continuation (EC) has demonstrated its ability in making accurate predictions for potentials in both coordinate and momentum space. The emulation process is performed by creating a basis of eigensolutions for several sets of known parameters to accurately interpolate and extrapolate solutions for the same Hamiltonian with different parameters. In addition, a twobody scattering emulator that employs a basis of scattering matrices has recently emerged as a strong alternative to EC. Here we compare the predictive capabilities of both emulators for a variety of observables and chiral effective field theory potentials. 
Sunday, April 10, 2022 10:57AM  11:09AM 
H12.00002: Artificial neural network quantum states for atomic nuclei Alessandro Lovato, Corey Adams, Noemi Rocco, Alex Gnech, Giuseppe Carleo, Nicholas Brawand Due to the exponential complexity of the manybody problem, solving the nuclear Schrödinger equation beyond light nuclei necessarily involves approximations. In this talk, we present a novel variational Monte Carlo method based on Artificial Neural Network representations of the groundstate wave functions that reach stateoftheart precisions on light systems and favorably scale with the number of nucleons. We successfully benchmark nuclear binding energies, pointnucleon densities, and radii with the highlyaccurate Green's function Monte Carlo and hyperspherical harmonics methods. The extensions of our approach to larger nuclei, including ^{16}O, and periodic systems, will also be discussed. 
Sunday, April 10, 2022 11:09AM  11:21AM 
H12.00003: Fast emulators for solving quantum fewbody problems Xilin Zhang A common bottleneck issue in solving quantum fewbody problems in hadronic, nuclear, and atomic physics is expensive computing costs. It restricts or even precludes many desirable calculations, such as theory calibrations. An emulator can be used to solve this issue. It is first trained on the exact solutions of the fewbody problem at a small set of points in the theory parameter space. It is then used to make fast predictions at other points in the same space. 
Sunday, April 10, 2022 11:21AM  11:33AM 
H12.00004: Physics Informed Deep Learning Model for Deeply Virtual Compton Scattering Manal Almaeen, Brandon Kriesten, Jake Grigsby, Yaohang Li, Simonetta Liuti, HueyWen Lin, Joshua Hoskins, Sorawich Maichum3 We present a physics informed deep learning technique for Deeply Virtual Compton 
Sunday, April 10, 2022 11:33AM  11:45AM 
H12.00005: Constraining NeutronStar Matter with Microscopic and Macroscopic Collisions Ingo Tews Neutron stars contain the largest reservoirs of degenerate fermions, reaching the highest densities we can observe in the cosmos, and probe matter under conditions that cannot be recreated in terrestrial experiments. Interpreting highenergy, astrophysical phenomena involving neutron stars, such as supernova explosions or neutronstar collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores of neutron stars remains limited. Fortunately, dense matter is not only probed in astrophysical observations, but also in terrestrial heavyion collision experiments. 
Sunday, April 10, 2022 11:45AM  11:57AM 
H12.00006: Contribution of Hadron Families to the Equation of State of QCD Angel R Nava, Claudia Ratti, Alejandro Florez Currently Lattice QCD simulations provide the best method of deriving the pressure of QCD as a function of the temperature. In the lowtemperature regime, the thermodynamics can be understood in terms of a gas of noninteracting hadrons and resonances, but the contribution of the single hadronic species cannot be easily isolated. In this work we propose linear combinations of susceptibilities of conserved charges, that isolate the contribution of hadrons to the pressure of QCD according to their baryon number B, electric charge Q and strangeness S content. We build these partial pressures such that they vanish in the StefanBoltzmann limit. This generates a nonmonotonic behavior which can be used to identify the melting temperature of each hadron family. We test the validity of these linear combinations in the Hadron Resonance Gas (HRG) model and compare them to available lattice QCD results. 
Sunday, April 10, 2022 11:57AM  12:09PM 
H12.00007: Implications of CREX on the Dense Matter Equation of State Brendan T Reed, Charles J Horowitz The experimental results for the CREX experiment suggest a small weak charge radius in $^{48}$Ca. This implies that its neutron skin $R_nR_p$ is $\textit{small}$. This has a large tension with the results of the PREX2 experiment, which measure a large weak charge radius in $^{208}$Pb thereby also suggesting it has a $\textit{large}$ neutron skin. In this talk, I will discuss the assumptions and analysis of CREX and what conclusions we can draw from it which impact the dense matter equation of state. The connection to neutron stars will also be discussed. 
Sunday, April 10, 2022 12:09PM  12:21PM 
H12.00008: Normalizing flows for microscopic calculations of the nuclear equation of state Pengsheng Wen, Jeremy W Holt, Jack Brady The nuclear equation of state (EOS) at finite temperature is fundamental to describe the properties of mediumenergy heavyion collisions as well as the hydrodynamic evolution of corecollapse supernovae and neutron star mergers. Microscopic calculations of the hot and dense matter equation of state using stateoftheart nuclear twobody and threebody forces in manybody perturbation theory is numerically challenging due to the repeated evaluation of highdimensional integrals across varying density, temperature, and composition. In this talk we demonstrate that normalizing flows provide a suitable Monte Carlo integration framework for such microscopic EOS calculations. Normalizing flows are a class of machine learning models used to construct a complex distribution from a simple base distribution and thus can be used to generate highly expressive representations of the integrands that appear in highorder manybody perturbation theory calculations. Moreover, a normalizing flow model trained on one target integrand can be easily transferred to the calculation of similar integrals as the density, temperature, or even nuclear potential is varied. 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2024 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700