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23 records · Page 2

Nuclear-level effective theory of 𝜇→𝑒 conversion: Formalism and applications

Over the next decade new 𝜇→𝑒 conversion searches at Fermilab (Mu2e) and J-PARC (COMET, DeeMe) are expected to advance limits on charged lepton flavor violation (CLFV) by more than four orders of magnitude. Here, by considering the consequence of 𝑃 and 𝐶⁢𝑃 on elastic 𝜇→𝑒 conversion and the structure of possible charge and current densities, we show that rates are governed by six nuclear responses and a single scale, 𝑞/𝑚 𝑁 , where 𝑞 ≈ 𝑚 𝜇 is the momentum transferred from the leptons to the nucleus. To relate this result to microscopic formulations of CLFV, we construct in nonrelativistic effective theory (NRET) the CLFV nucleon-level interaction, pointing out the relevance of the dimensionless scales 𝑦=($^{𝑞⁢𝑏}_2$) 2 ⁢ > |$\overrightarrow{𝑣}_N$⁢|>|$\overrightarrow{𝑣}_μ$⁢|>|$\overrightarrow{𝑣}_T$| , where 𝑏 is the nuclear size, $\overrightarrow{𝑣}_N$ and $\overrightarrow{𝑣}_μ$ are the nucleon and muon intrinsic velocities, and $\overrightarrow{𝑣}_T$ is the target recoil velocity. We discuss previous work, noting the lack of a systematic treatment of the various small parameters. Because the parameter 𝑦 is not small, a proper calculation of 𝜇→𝑒 conversion requires a full multipole expansion of the nuclear response functions, an apparently daunting task with Coulomb-distorted electron partial waves. We demonstrate that the multipole expansion can be carried out to high precision by introducing a simplifying local momentum 𝑞 eff for the electron. Previous work has been limited to simple charge or spin interactions, thereby treating the nucleus effectively as a point particle. We show that such formulations are not compatible with the general form of the 𝜇→𝑒 conversion rate, failing to generate three of the six allowed nuclear response functions. The inclusion of the nucleon velocity $\overrightarrow{𝑣}_N$ yields an NRET with 16 operators and a rate of the general form. Consequently, in the current discovery era for CLFV, it provides the most sensible starting point for experimental analysis, defining what can and cannot be determined about CLFV from the highly exclusive process of 𝜇→𝑒 conversion. Finally, we expand the NRET operator basis to account for the effects of $\overrightarrow{𝑣}_μ$, associated with the muon's lower component, generating corrections to the CLFV coefficients of the point-nucleus response functions. Using advanced shell-model methods, we compute 𝜇→𝑒 conversion rates for a series of experimental targets, deriving bounds on the coefficients of the CLFV operators. These calculations are the first to include a general basis of CLFV operators, full evaluation of the associated nuclear response functions, and an accurate treatment of electron and muon Coulomb effects. We discuss target selection as an experimental “knob” that can be turned to probe the microscopic origins of CLFV. We describe two types of coherence that enhance certain CLFV operators and selection rules that blind elastic 𝜇→𝑒 conversion to others. We discuss the matching of the NRET onto higher level effective field theories, such as those constructed at the light quark level, noting opportunities to build on existing work in direct detection of dark matter. We discuss the relation of 𝜇→𝑒 conversion to 𝜇→𝑒+𝛾 and 𝜇→3⁢𝑒, showing how MEG II and Mu3e results will complement those of Mu2e and COMET. Finally we describe a accompanying script—in Mathematica and Python versions—that can be used to compute 𝜇→𝑒 conversion rates in various nuclear targets for the full set of NRET operators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Turbo-Turtle v0.12.1

A collection of solid body modeling tools for 2D sketched, 2D axisymmetric, and 3D revolved models. It also contains general purpose meshing and image generation utilities appropriate for any model, not just those created with this package. Implemented for Abaqus and Cubit as backend modeling and meshing software. Orginal implementation targeted Abaqus so most options and descriptions use Abaqus modeling concepts and language. Turbo-Turtle makes a best effort to maintain common behaviors and features across each third-party software’s modeling concepts. As much as possible, the work for each subcommand is performed in Python 3 to minimize solution approach duplication in third-party tools. The third-party scripting interface is only accessed when creating the final tool specific objects and output. The tools contained in this project can be expanded to drive other meshing utilities in the future, as needed by the user community. This project derives its name from the origins as a sphere partitioning utility following the turtle shell (or soccer ball) pattern.s.

Brindley, Kyle↗

TORCHE - Toolbox for Reactor Cross-Flow Heat Exchangers

As interest within the scientific community for the development of small modular reactors (SMRs) grows, utilizing compact and cross-flow heat exchangers present many design advantages. Cross-flow configurations of tube bundles allow water, liquid metal, or a molten salt to be contained within the tube side or shell side in an efficient spatial arrangement. The goal of this project is to provide the user with an easy-to-use set of tools for cross-flow heat exchanger design. The tools are intended to require little to no geometric set-up like meshing in a CFD code in order to enable rapid preliminary design and optimization. With this set of simple Python scripts, the user can determine the necessary quantities and correctional factors in order to assess the heat exchanger characteristics. The two characteristics used in designing novel and efficient heat exchangers incorporated into this work currently are pressure drop and heat transfer coefficients. In support of these efforts, the literature has been scoured for sources of experimental data and proposed methods for calculating pressure drop and heat transfer in cross-flow tube banks. Several empirical correlations and methodologies for calculating pressure drop and heat transfer incorporated into the Toolbox for Reactor Cross-Flow Heat Exchangers (TORCHE). TORCHE is an expansion on the cross-flow pressure drop scripts reported by Haefner et al.. Presented in this summary is a synopsis of the empirical correlations and methodologies of pressure drops and heat transfer coefficient. A validation effort of these models is also presented to provide confidence in the implementation. For this validation of TORCHE, experimental data has been gathered and extracted from graphs in the literature. Error bars were not reported in the experimental data used in this summary and therefore is not included in the analysis.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Absolute Neutron Rate Measurement and Non-Thermal/Thermonuclear Fusion Differentiation

The goal of fusion energy is to produce significantly more energy from fusion reactions than is input into the device. One of the products of fusion reactions is neutrons, which, due to their lack of charge, provide a unique view into the parameters of the device. Lawrence Livermore National Laboratory (LLNL) in collaboration with the University of California, Berkeley (UCB) have designed, assembled, and fielded a robust and portable neutron detection system known as PANDA (Portable and Adaptable Neutron Diagnostics for ARPA-E). This detector suite consists of three LaBr activation detectors that are calibrated to give a total neutron yield on shot, and twenty-four scintillators coupled to photo-multiplier tubes (SPMT). The SPMTs can be configured to attain spatial, temporal and/or energy information from fusion neutrons. The system was designed to be portable and is compartmentalized so that individual components can be used at different fusion facilities. During the duration of this work, part of the system was installed at the FuZE facility, a part of Zap Energy. Another component was installed that the CESZAR facility at the University of California, San Diego (UCSD) to support experiments by Magneto-Inertial Fusion Technologies, Inc. (MIFTI). The diagnostics were successful at both locations and the LLNL/UCB team supported the data analysis by creating and running analysis scripts and Monte-Carlo calculations. At Zap Energy the diagnostics demonstrated that the fusion from the FuZE device is thermonuclear in nature, a result that resulted in an invited talk at the American Physical Society Division of Plasma Physics and an invited paper. Additionally, temporal and spatial data was taken using the SPMTs to understand the duration and length of fusion production. At UCSD the neutron yield from the diagnostics was used to show improvements to fusion yields on their gas puff Z-pinch when using a gas shell surrounding the fuel. The success in this diagnostic has led to continued work at both Zap Energy and MIFTI, as well as follow on funding and interest at other fusion energy companies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Computation of the expectation value of the spin operator S^ 2 for the spin-flip Bethe–Salpeter equation

Spin-flip (SF) methods applied to excited-state approaches like the Bethe–Salpeter equation allow access to the excitation energies of open-shell systems, such as molecules and defects in solids. The eigenstates of these solutions, however, are generally not eigenstates of the spin operator S^ 2 . Even for simple cases where the excitation vector is expected to be, for example, a triplet state, the value of S^ 2 may be found to differ from 2.00; this difference is called 'spin contamination'. The expectation values S^ 2 must be computed for each excitation vector, to assist with the characterization of the particular excitation and to determine the amount of spin contamination of the state. Here, our aim is to provide for the first time in the SF methods literature a comprehensive resource on the derivation of the formulas for S^ 2 as well as its computational implementation. After a brief discussion of the theory of the SF Bethe–Salpeter equation (BSE) and some examples further illustrating the need for calculating S^ 2 , we present the derivation for the general equation for computing S^ 2 with the eigenvectors from an SF-BSE calculation, how it is implemented in a Python script, and timing information on how this calculation scales with the size of the SF-BSE Hamiltonian.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗