Bulletin of the American Physical Society
Fall 2022 Meeting of the APS Division of Nuclear Physics
Volume 67, Number 17
Thursday–Sunday, October 27–30, 2022; Time Zone: Central Daylight Time, USA; New Orleans, Louisiana
Session PI: Nuclear Theory V |
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Chair: Calvin Johnson, San Diego State University Room: Hyatt Regency Hotel Imperial 12 |
Sunday, October 30, 2022 10:30AM - 10:42AM |
PI.00001: Lattice Scalar Field Theory At Complex Coupling Hyunwoo Oh, Yukari Yamauchi, Scott Lawrence Lattice scalar field theories encounter a sign problem when the coupling constant is complex. This is a close cousin of the real-time sign problems that afflict the lattice Schwinger-Keldysh formalism, and a more distant relative of the fermion sign problem that plagues calculations of QCD at finite density. We demonstrate the methods of complex normalizing flows and contour deformations on scalar fields in $0+1$ and $1+1$ dimensions, respectively. In both cases, intractable sign problems are readily bypassed. These methods extend to negative couplings, where the partition function can be defined only by analytic continuation. Finally, we examine the location of partition function zeros, and discuss their relation to the performance of these algorithms. |
Sunday, October 30, 2022 10:42AM - 10:54AM |
PI.00002: A Machine-learning method for spin classification of neutron resonances Gustavo P Nobre, David A Brown, Sophia J Hollick, Sergey Scoville, Pedro Rodríguez, Mary Fucci, Khadim Mbacke, Rose-Marie Crawford Neutron resonances are sharp fluctuations seen in neutron transmission and capture experiments at low-energy neutron-induced reactions. Properties of neutron resonances are some of the few experimental constraints to nuclear level densities and gamma strength functions (crucial for modeling many nuclear applications). Resonances are characterized by their angular momenta quantum numbers, which are normally assigned through fits often done in an ad hoc and not fully-reproducible manner. Comprehensive compilations of evaluated resonances often contain incorrectly-assigned spins. To address these, we developed a Machine-Learning method to train an algorithm to identify resonances with incorrect spin assignments. Model training is done on synthetic data constructed to simulate statistical properties of resonances seen in real nuclei. The trained classifier can be applied to resonances sequences from compiled, evaluated, or experimental data. In this work we use 52Cr as a test case to assess the performance of the reclassification, showing how multiple realizations of synthetic data can serve as a validation tool for the machine-learning classifier. We then apply the trained algorithm to make reclassification predictions on a 52Cr evaluated file. |
Sunday, October 30, 2022 10:54AM - 11:06AM |
PI.00003: Neural Network Ansätze for Infinite Matter Jane M Kim, Bryce Fore, Alessandro Lovato, Morten Hjorth-Jensen Artificial neural networks have shown tremendous promise as a flexible ansatz for quantum many-body problems. In this work, we approximately solve the Schrödinger equation by performing variational Monte Carlo calculations with a deep, permutation-invariant neural network as a Jastrow correlator. We discuss the reinforcement learning scheme and the stochastic reconfiguration algorithm which helps stabilize the optimization of the wave function parameters. Ground state energies for the three-dimensional electron gas and infinite neutron matter will be compared to standard variational and diffusion Monte Carlo results. |
Sunday, October 30, 2022 11:06AM - 11:18AM |
PI.00004: Renormalization Group Evolution of Optical Potentials Mostofa A Hisham, Anthony J Tropiano, Richard J Furnstahl To take full advantage of experimental facilities such as FRIB for applications to nuclear astrophysics, nuclear structure, and explorations of neutrinos and fundamental symmetries, we need a better understanding of the interplay of reaction and structure theory. Renormalization group (RG) techniques can treat reaction and structure components in a consistent manner, resulting in more accurate calculation of observables. As such, it is imperative to understand the behaviors of optical potentials under RG evolution, as these interactions simplify the many-body dynamics of a direct reaction to that of a few-body one. Here we apply similarity RG (or SRG) on a one-dimensional nuclear Hamiltonian to properly analyze the behaviors of the corresponding optical potential under RG transformations. We investigate how the optical potential decouples momenta during the RG evolution, as well as the evolution of the perturbativeness of the optical potential at each RG resolution scale. In addition, as the SRG introduces its own nonlocality, we will compare this to the nonlocality of the optical potential as well. We will further extend this analysis in future work to more realistic nuclear reactions, like the d(p,d)p process. |
Sunday, October 30, 2022 11:18AM - 11:30AM |
PI.00005: Application of Machine Learning to Many-Body Studies of Infinite Nuclear Matter Julie L Butler, Morten Hjorth-Jensen Neutron stars are extremely cold and dim, making observational astronomy difficult. Therefore, the only way to study them is through many-body studies of their constituent particles using state-of-the-art many-body methods with modern nuclear forces. However, many-body computations of infinite nuclear matter involve a large number of particles and complex potentials. This has a high computational cost, making large studies difficult. Machine learning is emerging as a useful tool in physics that will allow us to tackle problems which are difficult to solve with traditional computational methods. This talk will explore ways in which machine learning can speed up many-body calculations of infinite symettric nuclear matter and infinite pure neutron matter while still maintaining physically relevant accuracy. Ridge regression and kernel ridge regression will be applied using a variety of algorithms to find converged correlation energies and to extrapolate to the thermodynamic limit Accuracy compared to full calculations and time savings will be presented to justify the use of machine learning as a valid computational method for these calculations |
Sunday, October 30, 2022 11:30AM - 11:42AM |
PI.00006: Parameter calibration for beta-decay calculations Tong Li, Mookyong Son, Vojtech Kejzlar, Witold Nazarewicz, Tapabrata Maiti, Shrijita Bhattacharya, Samuel A Giuliani Reliable theoretical predictions for β-decay rates of neutron-rich nuclei are essential for r-process studies. Recent development of the proton-neutron finite amplitude method (pnFAM) makes global β-decay studies feasible within the nuclear density functional theory. In pnFAM calculations with the Skyrme functional, time-odd terms, isoscalar pairing, and the axial-vector coupling strongly impact the results, but they are not constrained by the properties of even-even nuclei. Model calibration is thus necessary and it is the first step toward the uncertainty quantification of β-decay predictions. We perform both χ2 optimization and Bayesian calibration with selected experimental data, and obtain optimal parameters and their uncertainties. Comparisons between various calibration schemes provide insights into the use of statistical methods in physics. |
Sunday, October 30, 2022 11:42AM - 11:54AM |
PI.00007: Implications of the CREX+PREX-2 Result on the Equation of State of Dense Matter Brendan T Reed, Charles J Horowitz, Farrukh J Fattoyev, Jorge Piekarewicz The experimental results for the CREX experiment suggest a small weak charge radius in 48Ca. This implies that its neutron skin is small. This has a large tension with the results of the PREX-2 experiment, which measure a large weak charge radius in 208Pb thereby also suggesting it has a 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, October 30, 2022 11:54AM - 12:06PM |
PI.00008: Photon Radiation from Anisotropic Nuclear Matter produced in Heavy-Ion Collisions at Fermi Energy Thomas J Onyango, Ralf F Rapp Heavy-ion collisions (HICs) are broadly used to examine the properties of nuclear matter under extreme conditions of density and temperature. Electromagnetic radiation in the form of photons or dileptons has long served as a penetrating probe of strongly interacting matter because of its ability to escape from the nuclear medium mostly unperturbed by final-state interactions. In this work, we focus on HICs at Fermi energies, where, in particular, the degree of thermalization remains an open question. We first briefly review our analysis of time dependence of thermodynamic properties of Ca-Ca(35AMeV) collisions using a coarse-graining approach to Constrained Molecular Dynamics simulations; significant non-thermal features caused by the initial motion of the incoming nuclei have been quantified in terms of anisotropic distribution functions. We incorporate these distributions into the calculation of photon rates from nucleon-nucleon Bremsstrahlung and discuss how the non-thermal features manifest themselves. The rates are then convoluted over the space-time evolution of Ca-Ca(35AMeV) collisions, and the resulting photon spectra are compared to experimental spectra in an attempt to shed light on their composition of collective and local emission sources. |
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