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This final technical report summarizes the work on the award DE-FG02-07ER41460, Transverse Spin and Momentum Structure of Hadrons in QCD. During the performance period, we addressed fundamental questions in nuclear physics, focusing on how hadrons—and, by extension, light nuclei—emerge from quark and gluon interactions within Quantum Chromodynamics (QCD).
Critical experiments, sometimes referred to zero-power reactors, are crucial tools in developing and validating nuclear physics predictions and thus an indispensable capability to ensure criticality safety during all parts of the nuclear fuel cycle and in reducing uncertainties in reactor physics predictions toward optimizing nuclear energy production. The System Physics Advanced Reactor Facility (SPARC) project was recently initiated to enable large-scale criticality experiments using a horizontal split table machine well suited toward solid core materials systems (fuel, moderator, reflector). The facility selected for this mission was a former pool-type research reactor building and thus also well suited toward a second critical experiment capability able to house full-size light-water reactor (LWR) fuel bundles. A conceptual design study was undertaken to review past water tank critical experiments used for LWR physics experiments and to develop an early engineering design for a new critical assembly tank (CAT). The work described here shows that a relatively simple CAT concept can be constructed and deployed in the SPARC facility to meet the urgent demands for new critical experiments on advanced LWR fuel bundles designs. The SPARC facility layout is conducive to the receipt and upending of LWR fuel bundles using existing containers and equipment from the LWR industry. The facility’s overhead crane can then be used to handle fuel bundles and place them in a vertical storage rack or in the CAT for critical experiments, both of which fit within the building’s “open basement” alongside other equipment planned for SPARC and the horizontal split table. A slightly lower area in the basement can serve as a large drain tank so that fail-safe valves drain the CAT reactor tank for safe shutdown. Neutronic configurations were determined where a 3 × 3 array of fuel bundles can be surrounded by full-length “loose rods” to adjust reactivity so that critical is achieved when the bundles are fully submerged. Viable configurations were determined for both pressurized- and boiling-water-reactor-type fuel bundles. This design concept was used to develop an early planning basis for establishing the CAT capability alongside the otherwise planned SPARC project in order to help streamline the process. Recent presidential executive orders have highlighted the need to achieve power uprates in LWR plants and the CAT capability will be a crucial element of these initiatives. Based on the work described herein, it is recommended that an earnest and timely project begin in order to establish this urgently needed capability.
The recently completed PREX-2 campaign measured the density distribution of neutrons in the lead nucleus as a function of momentum transfer (the form factor), confirmed a relatively large extent of the neutrons beyond the protons in the nucleus (the neutron skin), and provided a precise determination of the density of protons and neutrons at the center of a heavy nucleus. In turn, the measured form factor can be related to various nuclear and neutron star properties. The NICER X-ray telescope has inferred the masses and radii of some X-ray pulsars (neutron stars), although complications arise when determining these quantities independently. Further improvements in NICER have enabled simultaneous mass–radius determinations that had not previously been possible. During the next decade, measurements in astrophysics, gravitational-wave astronomy, and nuclear physics are expected to provide a wealth of more precise data. In this review, we present an overview of the current state of neutron skin measurements and offer insights into prospects for the future.
Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for nuclear theory often generate deep quantum circuits that place demanding memory and processing requirements on conventional simulation methods. Here, we present advances in high-performance numerical simulations of deep quantum circuits to efficiently verify the accuracy of low-energy nuclear physics applications. Our approach employs novel methods for accelerating the numerical simulation including management of simulated mid-circuit measurements to verify projection based state preparation circuits. In this study, we test these methods across a variety of high-performance computing systems and our results show that circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated.
Final Scientific/Technical Report for DOE Award DE-SC0019581, “TTDAQ: A Continuous Flow, Timing and Trigger DAQ System.” The report summarizes Telluric Labs’ Phase II STTR work developing silicon-photonic building blocks for a software-defined, continuous-flow, trigger-less data acquisition system for next-generation high-energy and nuclear-physics detectors. The project focused on radiation-hard photonic integrated circuits, remote optical illumination, dense wavelength-division multiplexing, and a differential microring-resonator transceiver architecture designed to improve high-speed optical link stability and bandwidth. The report describes project objectives, technical accomplishments, AIM Photonics tape-outs, bench characterization, radiation-hardness testing, deferred integration work, and potential applications beyond physics readout.
We consider the potential for a 10 kg undoped cryogenic CsI detector operating at the Spallation Neutron Source to measure coherent elastic neutrino-nucleus scattering and its sensitivity to discover new physics beyond the standard model (BSM). Through a combination of increased event rate, lower threshold, and good timing resolution, such a detector would significantly improve on past measurements. We considered tests of several BSM scenarios such as neutrino nonstandard interactions and accelerator-produced dark matter. This detector’s performance was also studied for relevant questions in nuclear physics and neutrino astronomy, namely the weak charge distribution of Cs and I nuclei and detection of neutrinos from a core-collapse supernova. Published by the American Physical Society 2024
We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.
Background: A handful of neutron-deficient stable nuclei, known as the “p nuclei,” cannot be produced through astrophysical neutron capture processes. Instead, some of these nuclei are proposed to be produced by 𝛾-induced reactions on existing r- and s-process seeds. The specific astrophysical site or sites are not yet identified, however, with uncertainties in the cross sections of these 𝛾-induced reactions playing a role. Databases of reaction rates for astrophysical simulations often rely on theoretical statistical model calculations, such as Hauser-Feshbach, for rates where no experimental information is known. However, reasonable variations in the choice of parametrizations of various nuclear properties can create order-of-magnitude variations in the final predicted cross sections and reaction rates, which are then propagated through the models to the predicted final abundances. Purpose: To better constrain these statistical model calculations and ultimately reduce the uncertainties from the nuclear physics on our understanding of the p nuclei, a measurement of the cross sections of 𝛾-induced reactions on the p-nucleus 102 Pd was undertaken. This work represents the first measurement of its kind, using segmented silicon detectors to measure prompt charged particle emission from 𝛾-induced reactions. Methods: Quasimonoenergetic gamma beams from the High Intensity 𝛾 Source facility bombarded an enriched 102 Pd target. A segmented silicon array was arranged to detect the particles emitted from (𝛾,𝑝) and (𝛾,𝛼) reactions. Results: Reaction cross sections were deduced at multiple 𝛾-beam energies between 10 and 19 MeV, and compared to statistical model calculations using talys-1.96. The 102 Pd (𝛾,𝑝) 101 Rh reaction cross section was reasonably well reproduced by a subset of photon strength functions and level densities, though the strength to the ground state of 101 Rh was underestimated at higher incident gamma energies. The 102 Pd (𝛾,𝛼) 98 Ru was in general overpredicted by the various alpha-nucleus optical model potentials. Conclusions: While the theoretical cross sections used to model the (𝛾,𝑝) reactions for the p process may be reasonable, a more careful approach is needed in the case of (𝛾,𝛼). Further work to probe gamma-induced reaction cross sections at and near the p nuclei is warranted.
Neutron Stars as Laboratories for Nuclear and Particle Physics
Experimental nuclear spectroscopic data are evaluated for the 7 known nuclides of mass 13 (Li, Be, B, C, N, O, F). Detailed information is compiled and presented for each reaction and decay experiment. The results are combined and used to provide a set of recommended values that include level energies, spins and parities, half-lives of levels, decay branching ratios, and other nuclear properties. Furthermore, this work supersedes the earlier work by Fay Ajzenberg-Selove (1990Aj01) published in Nuclear Physics A 523 (1991) 1. Earlier reviews were published in (1986Aj01, 1981Aj01, 1976Aj04, 1970Aj04, 1959Aj76).
This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.
Measurements of the 182 W(n, 2n) 181 W cross section have been performed in the neutron energy range between 8 and 15 MeV using the activation technique. Such data are needed to help interpret results of laser shots at the National Ignition Facility using a new DT capsule design, featuring a high-Z inner shell, with tungsten as the favored material, and an outer shell made of a low-Z material. Our data are in very good agreement with the previous data of Frehaut et al., which are based on a different technique, and in fair agreement with the ENDF/B-VIII.0 and the JEFF-3.3 evaluations.
Silicon has become an unavoidable element in the circuitry central to everyday life. In turn, the interactions of silicon isotopes with neutrons for nuclear physics applications, among other motivations, have become increasingly important to understand. The dominant isotope of silicon, 28 Si, is thus of primary interest for enhanced understanding for neutron transport calculations and related investigations. Unfortunately, the existing measurement database for neutron scattering reactions on 28 Si is minimal, and nuclear data evaluations on this topic have not been updated for decades. This article details new measurements of the 28 Si (𝑛,𝑛′𝛾) reaction utilizing multiple analysis methods available within the correlated gamma neutron array for scattering (CoGNAC). Specifically, high-precision near-threshold results and high-incident-energy results were obtained using the 𝛾-only and correlated 𝑛−𝛾 techniques. First-ever measurements of the correlated 𝑛−𝛾 angular distribution for particles emitted following population of the first excited state in 28 Si were obtained as well, which provide unique insight into theoretical descriptions of the inelastic neutron scattering reaction mechanism itself and detailed guidance for nuclear reaction models. The results agree well with literature data where they exist, and substantially expand on the current database for neutron reactions on 28 Si .
Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.
Here, the double-beta (2 β )-decay is the rarest nuclear physics process, and its experimental half-lives (T 1/2 ) exceed the age of the Universe from nine to fourteen orders of magnitude. Double-beta decay was observed, and its half-life was measured in 14 parent nuclei using direct, radiochemical, and geochemical methods. The decay observables are analyzed using the Evaluated Nuclear Structure Data File (ENSDF) procedures, and the recommended T 1/2 were deduced. Using the calculated values of phase factors, the effective nuclear matrix elements were extracted and compared with available data. Thousands of theoretical and experimental works have been dedicated to these topics in the last 85 years, and we present two data sets of recommended values to encapsulate the results.
The double-beta (2β)-decay is the rarest nuclear physics process, and its experimental half-lives (T 1/2 ) exceed the age of the Universe from nine to fourteen orders of magnitude. Double-beta decay was observed, and its half-life was measured in 14 parent nuclei using direct, radiochemical, and geochemical methods. The decay observables are analyzed using the Evaluated Nuclear Structure Data File (ENSDF) procedures, and the recommended T 1/2 were deduced. Using the calculated values of phase factors, the effective nuclear matrix elements were extracted and compared with available data. Thousands of theoretical and experimental works have been dedicated to these topics in the last 85 years, and we present two data sets of recommended values to encapsulate the results.
In massive stars (initial mass of ≳9 M ⊙ ), the weak s- (slow neutron capture) process produces elements between Fe and Zr, enriching the Galaxy with these elements through core-collapse supernova explosions. The weak s-process nucleosynthesis is driven by neutrons produced in the 22 Ne(α, n) 25 Mg reaction during convective He core and C shell burning. The yields of heavy elements thus depend on the 22 Ne(α, n) 25 Mg and the competitive 22 Ne(α, γ) 26 Mg reaction rates, which are dominated by several narrow-resonance reactions. While the accuracy of these rates has been under debate for decades, recent experimental efforts, including ours, drastically reduced these uncertainties. In this work, we use a set of 280 massive star nucleosynthesis models calculated using different 22 Ne(α, n) 25 Mg and 22 Ne(α, γ) 26 Mg rates and a galactic chemical evolution (GCE) study to probe their impact on the weak s-process elemental abundances in the Galaxy. The GCE was computed with the OMEGA+ code, using the new sets of stellar yields with different 22 Ne+α rates. From GCE, we find that these rates are causing up to 0.45 dex of variations in the [Cu/Fe], [Ga/Fe], and [Ge/Fe] ratios predicted at solar metallicity. The greatest impact on the stellar nucleosynthesis and GCE results derives from uncertainties in the (α, n) strength (ωγ (α,n) ) of the E x = 11.32 MeV resonance. We show that variations observed in the GCE calculations for weak s-process elements become negligibly smaller than dispersions found in observations once the ωγ (α,n) is accurately determined within the uncertainty of 10%–20% (typically reported experimental errors for the resonance) in future nuclear physics experiments.
This work explores the construction of a fast emulator for the calculation of the final pattern of nucleosynthesis in the rapid neutron capture process (the r-process). An emulator is built using a feed-forward artificial neural network (ANN). We train the ANN with nuclear data and relative abundance patterns. We take as input the β-decay half-lives and the one-neutron separation energy of the nuclei in the rare-earth region. The output is the final isotopic abundance pattern. In this work, we focus on the nuclear data and abundance patterns in the rare-earth region to reduce the dimension of the input and output space. We show that the ANN can capture the effect of the changes in the nuclear physics inputs on the final r-process abundance pattern in the adopted astrophysical conditions. We employ the deep ensemble method to quantify the prediction uncertainty of the neural network emulator. The emulator achieves a speed-up by a factor of about 20 000 in obtaining a final abundance pattern in the rare-earth region. The emulator may be utilized in statistical analyses such as uncertainty quantification, inverse problems, and sensitivity analysis.