In order to reach the full civic and scientific potential of physics, this white paper calls for a culture change in physics to recognize informal physics education (also referred to as public engagement or outreach) as an essential disciplinary practice. That is, engaging in informal physics education (IPE) is part of what it means to ”do physics.” In June 2024, we hosted a summit with forty-two members of the Joint Network for Informal Physics Education and Research (JNIPER) to discuss concrete steps for fostering this cultural shift in physics. We present key findings from the Summit to motivate this culture change: IPE makes the work of physicists relevant; fosters trust and supports a society where everyone benefits from science and technology advances; serves as a gateway for entering into the physics discipline, and for staying once there; and improves physicists' skills and research. We identify three levers for promoting the culture change: structures supporting IPE; engagement of interested, influential, and/or impacted parties; and integration of research-based IPE practices. Each lever is accompanied by associated recommendations for action directed at individuals, departments and institutions, topical groups such as JNIPER, and funders and (inter)national organizations. Our clarion call is for members and supporters of the IPE community to choose one recommendation per lever to prioritize and to set forth a roadmap for implementation. Together, we can establish IPE as a central physics practice, ultimately leading to a deeper connection between physics and society, strengthening our mutual potential and impact for good.

Nuclear astrophysics is an interdisciplinary field focused on exploring the impact of nuclear physics on the evolution and explosions of stars and the cosmic creation of the elements. While researchers in astrophysics and in nuclear physics are separately using machine learning approaches to advance studies in their fields, there is currently little use of machine learning in nuclear astrophysics. We briefly describe the most common types of machine learning algorithms, and then detail their numerous possible uses to advance nuclear astrophysics, with a focus on simulation-based nucleosynthesis studies. We show that machine learning offers novel, complementary, creative approaches to address many important nucleosynthesis puzzles, with the potential to initiate a new frontier in nuclear astrophysics research.

The cross section of the C13(α,n)O16 reaction is needed for nuclear astrophysics and applications to a precision of 10% or better, yet inconsistencies among 50 years of experimental studies currently lead to an uncertainty of ≈15%. Using a state-of-the-art neutron detection array, we have performed a high resolution differential cross section study covering a broad energy range. These measurements result in a dramatic improvement in the extrapolation of the cross section to stellar energies potentially reducing the uncertainty to ≈5% and resolving long standing discrepancies in higher energy data.Received 18 March 2023Revised 5 September 2023Accepted 17 January 2024DOI:https://doi.org/10.1103/PhysRevLett.132.062702© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasH & He burningH & He induced nuclear reactionsHydrostatic stellar nucleosynthesisNeutron physicsResonance reactionsS-factors processProperties6 ≤ A ≤ 19TechniquesNuclear data analysis & compilationNuclear Physics

We present a method for measurement analyses based on probabilistic deep neural networks that provide several advantages over conventional analyses with phenomenological models. These include predicting physical quantities directly from data, the rapid generation of statistically robust uncertainties, and the ability to bypass some parameters that may induce ambiguities and complications in data analysis. As deep learning methods make predictions through "black boxes," the uncertainty quantification is typically challenging. We use a probabilistic framework that provides thorough uncertainty quantification and is straightforward to follow in practice. With the network architecture based on the Transformer, we demonstrate the current method for predicting nuclear resonance parameters from scattering data using the phenomenological R-matrix model.

The cross section of the ^{13}C(α,n)^{16}O reaction is needed for nuclear astrophysics and applications to a precision of 10% or better, yet inconsistencies among 50 years of experimental studies currently lead to an uncertainty of ≈15%. Using a state-of-the-art neutron detection array, we have performed a high resolution differential cross section study covering a broad energy range. These measurements result in a dramatic improvement in the extrapolation of the cross section to stellar energies potentially reducing the uncertainty to ≈5% and resolving long standing discrepancies in higher energy data.

Physics models typically contain adjustable parameters to reproduce measured data. While some parameters correspond directly to measured features in the data, others are unobservable. These unobservables can, in some cases, cause ambiguities in the extraction of observables from measured data, or lead to questions on the physical interpretation of fits that require these extra parameters. We propose a method based on deep learning to extract values of observables directly from the data without the need for unobservables. The key to our approach is to label the training data for the deep learning model with only the observables. After training, the deep learning model can determine the values of observables from measured data with no ambiguities arising from unobservables. We demonstrate this method on the phenomenological R-matrix that is widely utilized in nuclear physics to extract resonance parameters from cross section data. Our deep learning model based on Transformers successfully predicts nuclear properties from measurements with no need for the channel radius and background pole parameters required in traditional R-matrix analyses. Details and limitations of this method, which may be useful for studies of a wide range of phenomena, are discussed.

The cross section of the 13C(alpha, n)16O reaction is needed for nuclear astrophysics and applications to a precision of 10% or better, yet inconsistencies among 50 years of experimental studies currently lead to an uncertainty of approximate to 15%. Using a state-of-the-art neutron detection array, we have performed a high resolution differential cross section study covering a broad energy range. These measurements result in a dramatic improvement in the extrapolation of the cross section to stellar energies potentially reducing the uncertainty to approximate to 5% and resolving long standing discrepancies in higher energy data.

The rate of the final step in the astrophysical αp process, the ^{34}Ar(α,p)^{37}K reaction, suffers from large uncertainties due to a lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron stars. We present the first direct measurement constraining the ^{34}Ar(α,p)^{37}K reaction cross section, using the Jet Experiments in Nuclear Structure and Astrophysics gas jet target. The combined cross section for the ^{34}Ar,Cl(α,p)^{37}K,Ar reaction is found to agree well with Hauser-Feshbach predictions. The ^{34}Ar(α,2p)^{36}Ar cross section, which can be exclusively attributed to the ^{34}Ar beam component, also agrees to within the typical uncertainties quoted for statistical models. This indicates the applicability of the statistical model for predicting astrophysical (α,p) reaction rates in this part of the αp process, in contrast to earlier findings from indirect reaction studies indicating orders-of-magnitude discrepancies. This removes a significant uncertainty in models of hydrogen and helium burning on accreting neutron stars.

Approaches for predicting low-lying resonances,uniformly treating bound,and resonant levels have been a long-standing goal in nuclear theory.Accordingly,we explored the viability of the complex momentum representation(CMR)approach coupled with new potentials.We focus on predicting the energy of the low-lying 2p3/2 resonance in 17O,which is critical for s-process nucleosynthesis and missing in previous theoretical research.Using a Woods-Saxon potential based on the Koning-Delaroche optical model and constrained by the experimental one-neutron separation energy,we successfully predicted the resonant energy of this level for the first time.Our predictions of the bound levels and 1d3/2 resonance agree well with the measurement results.Additionally,we utilize this approach to study the near-threshold resonances that play a role when forming a two-neutron halo in 29.31F.We found that the CMR-based predictions of the bound-level energies and unbound 1f7/2 level agree well with the results obtained using the scattering phase shift method.Subsequently,we successfully found a solution for the 2p3/2 resonance with energy just above the threshold,which is decisive for halo formation.

Absolute cross sections measured using electromagnetic devices to separate and detect heavy recoiling ions need to be corrected for charge state fractions. Accurate prediction of charge state distributions using theoretical models is not always a possibility, especially in energy and mass regions where data is sparse. As such, it is often necessary to measure charge state fractions directly. In this paper we present a novel method of using a scintillation screen along with a CMOS camera to image the charge dispersed beam after a set of magnetic dipoles. A measurement of the charge state distribution for 88Sr passing through a natural carbon foil is performed. Using a Bayesian model to extract statistically meaningful uncertainties from these images, we find agreement between the new method and a more traditional method using Faraday cups. Future work is need to better understand systematic uncertainties. Our technique offers a viable method to measure charge state distributions.

Research into the cosmic synthesis of the elements, the evolution and explosion of stars, the nature of the early Universe, and other important topics in nuclear astrophysics are at the forefront of nuclear science. These studies are motivating laboratory measurements and theoretical calculations that, after significant investments, are pushing the boundaries of what is possible. The latest nuclear results, however, must be specially prepared before they can be used to advance our knowledge of the cosmos. This processing requires a set of resources unique to nuclear astrophysics, and an impressive collection of nuclear reaction and nuclear structure datasets, processing codes, thermonuclear reaction rate libraries, and simulation codes and services have been developed for the field. There are, however, some serious challenges to these efforts that will only worsen in the future, making it important to develop strategies and act now to ensure a sustainable future for this work. After detailing the specific data types needed for nuclear astrophysics and the available data resources, the major challenges in this work and their implications are discussed. A set of initiatives are proposed to meet those challenges along with suggested implementations and possible ways that they may advance our understanding of the Universe and strengthen the field of nuclear astrophysics.

The rate of the final step in the astrophysical αp process, the ^{34}Ar(α,p)^{37}K reaction, suffers from large uncertainties due to a lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron stars. We present the first direct measurement constraining the ^{34}Ar(α,p)^{37}K reaction cross section, using the Jet Experiments in Nuclear Structure and Astrophysics gas jet target. The combined cross section for the ^{34}Ar,Cl(α,p)^{37}K,Ar reaction is found to agree well with Hauser-Feshbach predictions. The ^{34}Ar(α,2p)^{36}Ar cross section, which can be exclusively attributed to the ^{34}Ar beam component, also agrees to within the typical uncertainties quoted for statistical models. This indicates the applicability of the statistical model for predicting astrophysical (α,p) reaction rates in this part of the αp process, in contrast to earlier findings from indirect reaction studies indicating orders-of-magnitude discrepancies. This removes a significant uncertainty in models of hydrogen and helium burning on accreting neutron stars.

Decay protons from ^22 Mg energy levels populated through a previously reported ^24 Mg(p, t) ^22 Mg transfer reaction (Chae et al. in Phys Rev C 79:055804, 2009) have been analyzed for proton branching ratios as a follow-up analysis. The measurement was performed at the Holifield Radioactive Ion Beam Facility of Oak Ridge National Laboratory by utilizing 41-MeV proton beams and ^24 Mg solid targets. Decay protons and reaction tritons were simultaneously detected with a silicon detector array. By investigating the ^24 Mg(p, t) ^22 Mg*(p) ^21 Na channels, the proton branching ratios of five ^22 Mg excited states were obtained. The measured branching ratios provide constraints on the proton partial widths of the populated ^22 Mg levels, which have implications for X-ray burst nucleosynthesis.

Understanding the harmful effects of galactic cosmic rays (GCRs) on space exploration requires a substantial amount of nuclear data. Specifically, the interaction of energetic GCR charged particles with spacecraft materials generates secondary radiations that, through energy deposition, can harm astronauts and electronic systems. By identifying the gaps in our knowledge of the relevant nuclear data—such as interaction cross sections—and identifying ways to fill those gaps—with measurements, compilations, evaluations, dissemination, reaction modeling, sensitivity studies, and uncertainty quantification—the safety and viability of space exploration can be improved. This work surveys the state of the art in this interdisciplinary field and identifies promising collaborative research topics that have significant potential to advance our understanding of the effects of the space radiation environment on space exploration.

Physics models typically contain adjustable parameters to reproduce measured data. While some parameters correspond directly to measured features in the data, others are unobservable. These unobservables can, in some cases, cause ambiguities in the extraction of observables from measured data, or lead to questions on the physical interpretation of fits that require these extra parameters. We propose a method based on deep learning to extract values of observables directly from the data without the need for unobservables. The key to our approach is to label the training data for the deep learning model with only the observables. After training, the deep learning model can determine the values of observables from measured data with no ambiguities arising from unobservables. We demonstrate this method on the phenomenological R-matrix that is widely utilized in nuclear physics to extract resonance parameters from cross section data. Our deep learning model based on Transformers successfully predicts nuclear properties from measurements with no need for the channel radius and background pole parameters required in traditional R-matrix analyses. Details and limitations of this method, which may be useful for studies of a wide range of phenomena, are discussed.

Background: A relativistic structure model has previously been used to predict a halo structure for Ne [S. S. Zhang, M. Smith, Z. S. Kang and J. Zhao, Phys. Lett. B 730, 30 (2014)], consistent with halo signatures from measured reaction cross sections of Ne isotopes bombarding Carbon targets. However, previous attempts to calculate those cross sections with reaction models were missing contributions from resonances and pairing correlations in their structure input. Purpose: Use a reaction model with our relativistic fully microscopic structure model input to predict these cross sections and momentum distributions and analyze for possible halo signatures. Methods: Structure input for exotic Ne isotopes were obtained via the analytical continuation of the coupling constant (ACCC) method based on the relativistic mean field (RMF) theory with Bardeen-Cooper-Schrieffer (BCS) pairing approximation, the RAB approach. Total reaction cross sections, one-neutron removal cross sections, and momentum distributions of breakup reaction products were calculated with a Glauber model using our relativistic structure input. Results: Our predictions of total reaction and one-neutron removal cross sections of Ne on a Carbon target were significantly enhanced compared with those of neighboring Neon isotopes, agreeing well with measurements at 240 MeV/nucleon and consistent with a single neutron halo. Furthermore, our calculations of the inclusive longitudinal momentum distribution of the Ne and valence neutron residues from the Ne breakup reaction indicate a dilute density distribution in coordinate space, another halo signature. Conclusions: We give a full description of the halo nature of Ne that includes a self-consistent use of pairing and continuum contributions that makes predictions consistent with reaction cross section measurements. This approach can be utilized to determine the halo structure of other exotic nuclei.

S. A. Miskovich , 2, 3, ∗ F. Montes, 3 G. P. A. Berg, 5 J. Blackmon, K. A. Chipps, M. Couder, 5 C. M. Deibel, K. Hermansen , 2, 3 A. A. Hood, R. Jain, 2, 3 T. Ruland, H. Schatz, 2, 3 M. S. Smith, P. Tsintari, and L. Wagner 2, 3 Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, MI 48824 The Joint Institute for Nuclear Astrophysics, Michigan State University, East Lansing, MI 48824 Department of Physics, University of Notre Dame, Notre Dame, IN 46556 The Joint Institute for Nuclear Astrophysics, University of Notre Dame, Notre Dame, IN 46556 Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803 Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 Cyclotron Institute, Texas A&M University, College Station, TX 77843 Department of Physics, Central Michigan University, Mt Pleasant, MI 48859 (Dated: January 2022)

The Separator for Capture Reactions (SECAR) is a next-generation recoil separator system at the Facility for Rare Isotope Beams (FRIB) designed for the direct measurement of capture reactions on unstable nuclei in inverse kinematics. To maximize the performance of this system, stringent requirements on the beam alignment to the central beam axis and on the ion-optical settings need to be achieved. These can be difficult to attain through manual tuning by human operators without potentially leaving the system in a suboptimal and irreproducible state. In this work, we present the first development of online Bayesian optimization with a Gaussian process model to tune an ion beam through a nuclear astrophysics recoil separator. We show that this method achieves small incoming angular deviations (<1 mrad) in an efficient and reproducible manner that is at least 3 times faster than standard hand-tuning. Additionally, we present a Bayesian method for experimental optimization of the ion optics, and show that it validates the nominal theoretical ion-optical settings of the device, and improves the mass separation by 32% for some beams.