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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Section-level genome sequencing and comparative genomics of Aspergillus sections Cavernicolus and Usti.

The genus Aspergillus is diverse, including species of industrial importance, human pathogens, plant pests, and model organisms. Aspergillus includes species from sections Usti and Cavernicolus, which until recently were joined in section Usti, but have now been proposed to be non-monophyletic and were split by section Nidulantes, Aenei and Raperi. To learn more about these sections, we have sequenced the genomes of 13 Aspergillus species from section Cavernicolus (A. cavernicola, A. californicus, and A. egyptiacus), section Usti (A. carlsbadensis, A. germanicus, A. granulosus, A. heterothallicus, A. insuetus, A. keveii, A. lucknowensis, A. pseudodeflectus and A. pseudoustus), and section Nidulantes (A. quadrilineatus, previously A. tetrazonus). We compared these genomes with 16 additional species from Aspergillus to explore their genetic diversity, based on their genome content, repeat-induced point mutations (RIPs), transposable elements, carbohydrate-active enzyme (CAZyme) profile, growth on plant polysaccharides, and secondary metabolite gene clusters (SMGCs). All analyses support the split of section Usti and provide additional insights: Analyses of genes found only in single species show that these constitute genes which appear to be involved in adaptation to new carbon sources, regulation to fit new niches, and bioactive compounds for competitive advantages, suggesting that these support species differentiation in Aspergillus species. Sections Usti and Cavernicolus have mainly unique SMGCs. Section Usti contains very large and information-rich genomes, an expansion partially driven by CAZymes, as section Usti contains the most CAZyme-rich species seen in genus Aspergillus. Section Usti is clearly an underutilized source of plant biomass degraders and shows great potential as industrial enzyme producers. Citation: Nybo JL, Vesth TC, Theobald S, Frisvad JC, Larsen TO, Kjaerboelling I, Rothschild-Mancinelli K, Lyhne EK, Barry K, Clum A, Yoshinaga Y, Ledsgaard L, Daum C, Lipzen A, Kuo A, Riley R, Mondo S, LaButti K, Haridas S, Pangalinan J, Salamov AA, Simmons BA, Magnuson JK, Chen J, Drula E, Henrissat B, Wiebenga A, Lubbers RJM, Müller A, dos Santos Gomes AC, Mäkelä MR, Stajich JE, Grigoriev IV, Mortensen UH, de Vries RP, Baker SE, Andersen MR (2025). Section-level genome sequencing and comparative genomics of Aspergillus sections Cavernicolus and Usti. Studies in Mycology 111: 101-114. doi: 10.3114/sim.2025.111.03.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multi-Differential Charged Current 𝝂_𝝁− Argon Cross Section without Pions in the Final State Measurement in MicroBooNE , and Current Status of Simultaneous Cross Section Extraction with ANNIE

MicroBooNE, an 85-tonne liquid argon time projection chamber detector is on-axis to the Booster Neutrino Beam (BNB) at Fermi National Accelerator Laboratory. MicroBooNE is elucidating neutrino interactions with argon through cross-section measurements to refine interaction models and reduce uncertainties. In this poster, we present recent published single and double multi-differential charged current (CC) cross section with zero pions in the final state (CC-0 ) as a function of muon momentum ( ) and the cosine of the muon angle ( ). We present the details of the event selection and the extraction of 1D and 2D cross sections. We further report on the status of a simultaneous cross-section extraction conducted in conjunction with the ANNIE experiment, which employs a Gd-H O target and operates on the same BNB beamline. This joint measurement enables a precision test of nuclear A-dependence between oxygen and argon, with shared flux and cross-section model correlations leading to the cancellation of systematic uncertainties.

Nguyen, Christian Van [Rutgers U., Piscataway] (OR↗
DOE OSTI2026

Inclusive and differential measurements of the $\textrm{t}\overline{\textrm{t}}\mathcal{γ}$ cross section and the $\textrm{t}\overline{\textrm{t}}\mathcal{γ}/\textrm{t}\overline{\textrm{t}}$ cross section ratio in proton-proton collisions at $\sqrt{s}=13$ TeV

Inclusive and differential cross section measurements of top quark pair $(\textrm{t}\overline{\textrm{t}})$ production in association with a photon (γ) are performed as a function of lepton, photon, top quark, and $\textrm{t}\overline{\textrm{t}}$ kinematic observables, using data from proton-proton collisions at $\sqrt{s}=13$ TeV, corresponding to an integrated luminosity of 138 fb −1 , collected at the CERN LHC with the CMS detector. Events containing two leptons (electrons or muons) and a photon in the final state are considered. The fiducial cross section of $\textrm{t}\overline{\textrm{t}}$γ is measured to be 137 ± 8 fb, in a phase space including events with a high momentum, isolated photon. The fiducial cross section of $\textrm{t}\overline{\textrm{t}}$γ is also measured to be 56 ± 5 fb when considering only events where the photon is emitted in the production part of the process. Both measurements are in agreement with the theoretical predictions, of 126 ± 19 fb and 57 ± 5 fb, respectively. Differential measurements are performed at the particle and parton levels. Additionally, inclusive and differential ratios between the cross sections of $\textrm{t}\overline{\textrm{t}}$γ and $\textrm{t}\overline{\textrm{t}}$ production are measured. The inclusive ratio is found to be 0.0133 ± 0.0005, in agreement with the standard model prediction of 0.0127 ± 0.0008. The top quark charge asymmetry in $\textrm{t}\overline{\textrm{t}}$γ production is also measured to be −0.012 ± 0.042, compatible with both the standard model prediction and with no asymmetry.

Hadron-Hadron Scattering↗
DOE OSTI2024

Multi-Differential Semi-Inclusive Charged-Current Muon Neutrino Cross Sections on Helium-4 in MINERvA and the Helium-to-CH Target Cross Section Ratio

A comprehensive international effort has been underway to elucidate the properties and behaviors of neutrinos. A major source of systematic uncertainties in studying neutrino-induced interactions comes from neutrino-nucleus cross-section models, highlighting the need for more precise statistical measurements. MINERvA, an on-axis neutrino-nucleus scattering experiment located at the Fermi National Accelerator Laboratory, was established to produce neutrino cross-section measurements with many different nuclei. The helium target is the lightest nucleus to be measured by MINERvA. Using the NuMI medium energy muon neutrino data set, we present preliminary results and summarize the extraction of the charged current (CC) muon neutrino - helium-4 semi-inclusive ($\nu_{\mu}$+$^{4}\textrm{He}$$\rightarrow$$\mu^{-}$+N$\pi$+M$\textit{p}$) multi-differential cross-section extraction as a function of transverse ($P_{T}$) and longitudinal ($P_{L}$) muon momentum with respect to the neutrino beamline. We define the final state topology as CC-N$\pi$M$\textit{p}$, with at least two reconstructed tracks: a muon and a combination of N protons and M pions, where $N + M > 0$. To probe the dependence on the size of the nucleus in neutrino-induced interaction phenomena we present a differential cross-section ratio of helium-4 to MINERvA's hydrocarbon target (\textrm{CH}) as a function of transverse muon momentum.

Nguyen, Christian↗
DOE OSTI2025

Tables of Neutron Thermal Cross Sections, Westcott Factors, Resonance Integrals, Maxwellian Averaged Cross Sections, Astrophysical Reaction Rates, and r-process Abundances Calculated from the ENDF/B-VIII.1, JEFF-3.3, JENDL-5.0, BROND-3.1, and CENDL-3.2 Evaluated Data Libraries

We present calculations of neutron thermal cross sections, Westcott factors, resonance integrals, Maxwellian-averaged cross sections, astrophysical reaction rates, and solar system r-process abundances using the latest data from the major evaluated nuclear libraries for 849 ENDF target materials. The recent release of ENDF/B-VIII.1 library, progress in 252 Cf(SF) evaluation, extensive analysis of newly-evaluated neutron reaction cross sections, neutron covariances, and improvements in data processing techniques motivated us to calculate the nuclear industry and neutron physics parameters, produce s-process Maxwellian-averaged cross sections and astrophysical reaction rates, extract r-process abundances, systematically calculate uncertainties, and provide additional insights on currently available neutron-induced reaction data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Absolute photoionization cross sections of brominated organics and determination of an effective C—Br bond cross section

For this work, absolute photoionization cross sections for five brominated species were measured using multiplexed synchrotron photoionization mass spectrometry at the Advanced Light Source (Lawrence Berkeley National Laboratory). These data enable reliable detection of bromine-containing compounds and estimation of C—Br bond photoionization cross sections. Complementary electronic-structure calculations provided adiabatic ionization energies in good agreement with experiment, while thermochemical appearance energies were measured directly. Bond dissociation energies for key fragmentation pathways of the neutral parent molecules were also analyzed. At 11 eV, cross sections were 53.8 ± 7.50 Mb (sp 2 C—Br) and 48.8 ± 7.70 Mb (sp 3 C—Br).

Brominated compounds↗
DOE OSTI2024

Measurement of the Pion Exclusive Electro-Production Cross-Section in the E12-19-006 Experiment in Hall-C at Jefferson Lab

One of the most effective methods for exploring the transition from hadronic degrees of freedom to quark-gluon degrees of freedom in Quantum Chromodynamics (QCD) is through the investigation of \exclusive" pion and kaon electro-production reactions at various Q2 and ?t values. The E12-19-006 experiment is conducted within the confi?nes of experimental Hall C at the Thomas Jefferson National Accelerator Facility, USA, for such studies. The primary aim of the experiment is to ?first enhance our comprehension of the pion electro-production cross-section and its form factor at Q2 = 0.38 and 0.42 GeV2. This is the fi?rst run period of the E12-19-006 experiment which ran in summer 2019. A more profound understanding of the pion electro-production reaction, 1H(e,e'?+)n, at low Q2 is deemed essential to employ this electro-production reaction (an indirect technique) for the high Q2 studies, thereby delving deeper into the realm of QCD. Consequently, this dissertation presents a thorough analysis of the experimental data acquired in the ?first run period of the E12-19-006 experiment. In pursuit of precision, a series of systematic studies (target boiling correction study, the elastic reaction cross-section measurements, study for determining vari- ous kinematics o?sets, etc.) are conducted to discern the accuracy of the analyzed data, a prerequisite for the use of Rosenbluth separation technique to separate the pion electro-production cross-section terms in t bins. The separated pion electro-production cross-section through the Rosenbluth separation technique is then used to extract the pion electromagnetic form factor. In this dissertation, the pion electro-production cross-section is carefully dissected into its four constituent components: longitudinal (?L), transverse (?T ), longitudinal-transverse (?LT ), and transverse-transverse (?TT ), using the full version of Rosenbluth separation technique for the Q2 = 0.38 GeV2. The technique is simultaneously fi?tted to the unseparated pion electro-production cross-sections at the three values of polarization of the virtual photon (?), i.e., ? = 0.286, 0.629 and 0.781. An iterative process is applied to re?ne the parameters of the model cross-sections until the yield ratio of experimental and Monte Carlo simulation converges. In this study, 21 iterations are conducted to re?ne the model cross-section parameters. The fi?nal pion electro-production cross-section terms are then determined for 7 t bins using the optimized parameters of the model cross-sections.

Kumar, Vijay↗
DOE OSTI2024

Photonuclear cross sections for 197 Au: An update on the gold standard

Cross sections for the 197 Au(γ, n) reaction are broadly used in nuclear physics as a standard for normalizing photonuclear reaction cross-section data at photon beam energies above approximately 8 MeV. In this paper, we report cross-section measurements for the 197 Au(γ, n) 196 Au g+m1 reaction at beam energies from 13 to 31 MeV. Our measurements provide the first cross-section data for this reaction at beam energies above 20 MeV, enabling the use of this reaction as a cross-section standard up to 30 MeV. Also, this work provides first cross-section measurements for the 197 Au(γ, n) 196 Au m2 reaction. In addition, we measured cross-section data for the 197 Au(γ, 3n) 194 Au reaction, which can be used as a cross-section standard above about 25 MeV. These measurements were performed using a new target activation method that is based on the angle-energy correlation of the laser Compton- scattered photon beams at the High Intensity Gamma-ray Source (HIγS). The technique enables measuring photonuclear reaction cross-sections at several discrete beam energies concurrently via a single irradiation on a stack of different targets. Measurements were carried out by irradiating a stack of concentric-ring targets consisting of Au, TiO 2 , Zn, Os, and Au (in order of the γ-ray beam direction). Our data for the 197 Au(γ,n) 196 Au g+m1 reaction in the energy range of 13 to 20 MeV are in good agreement with existing ones measured using monoenergetic γ-ray beams, but differ from data acquired using a bremsstrahlung γ-ray beam. Also, above 18 MeV, our data for the 197 Au(γ,n) 196 Au g+m1 and 197 Au(γ,n) 196 Au m2 reactions differ significantly from the most recent TENDL and JENDL evaluations, suggesting a need to update these data libraries. Furthermore, the TENDL evaluation and existing data are consistent with our data for the 197 Au(γ,3n) reaction, but differ significantly from the JENDL evaluation above 26 MeV.

190 ≤ A ≤ 219↗
DOE OSTI2024

Generating An Advanced Cross-section Library For HTGR Pebble Bed Depletion Calculations Using Reduced-Order Model Generation Techniques

For code development, Advanced Reactor Technologies - Gas Cooled Reactors Program (ART-GCR) rely on a collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, but the cross sections generation and the methodology definition is part of this program area goals. Based on previous studies in FY23, the size of microscopic cross section libraries increases rapidly with the number of tabulations, requiring significant amount of memory and drastically slowing down the Griffin calculations when evaluating cross sections via the multivariate linear interpolation approach. Rising to these challenges, this work investigates constructing Reduced-order Models (ROMs) for the multi-group microscopic cross sections to accelerate the cross section evaluation in Griffin. A database of multigroup cross sections is first collected considering all possible parameters that a designer could change for optimization. Down-selection of the ROM techniques afterward shows Deep Neural Network (DNN) as the best candidate when jointly consider memory efficiency, predictive accuracy, computational cost, scalability, flexibility and ease of implementation of the algorithms in comparison to the multidimensional interpolation. This work develops a specific interface that enables the cross section predictions using pre-trained DNN models into Griffin leveraging the existing ROM capabilities. DNNs have been trained for all isotopes for use in Griffin. Preliminary Griffin testing shows that DNNs exhibit exceptional predictive accuracy and the use of DNNs provides orders of magnitude improvement in memory efficiency compared to conventional interpolation techniques. With such ROM techniques, it holds great promise to further increase the fidelity of the Pebble Bed Reactor (PBR) simulation by increasing the number of tabulations/state variables during cross section evaluation, while maintaining the computational cost affordable in Griffin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.

42 - ENGINEERING↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2025

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semi-empirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Chung, Seung Whan (ORCID:0000000302501549)↗
DOE OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Genomic Analysis of Aspergillus Section Terrei Reveals a High Potential in Secondary Metabolite Production and Plant Biomass Degradation

Aspergillus terreus has attracted interest due to its application in industrial biotechnology, particularly for the production of itaconic acid and bioactive secondary metabolites. As related species also seem to possess a prosperous secondary metabolism, they are of high interest for genome mining and exploitation. Here, we present draft genome sequences for six species from Aspergillus section Terrei and one species from Aspergillus section Nidulantes. Whole-genome phylogeny confirmed that section Terrei is monophyletic. Genome analyses identified between 70 and 108 key secondary metabolism genes in each of the genomes of section Terrei, the highest rate found in the genus Aspergillus so far. The respective enzymes fall into 167 distinct families with most of them corresponding to potentially unique compounds or compound families. Moreover, 53% of the families were only found in a single species, which supports the suitability of species from section Terrei for further genome mining. Intriguingly, this analysis, combined with heterologous gene expression and metabolite identification, suggested that species from section Terrei use a strategy for UV protection different to other species from the genus Aspergillus. Section Terrei contains a complete plant polysaccharide degrading potential and an even higher cellulolytic potential than other Aspergilli, possibly facilitating additional applications for these species in biotechnology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine learning neutrino-nucleus cross sections

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section—leveraging only Standard-Model symmetries—can be learned from near-detector data. We perform a neutrino oscillation analysis with simulated far-detector events, finding that oscillation analysis results enabled by our data-driven cross-section model approach the theoretical limit achievable with perfect prior knowledge of the cross section. We further quantify the effects of flux shape and detector resolution uncertainties as well as systematics from cross-section mismodeling. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Direct cross section measurement of 102 Pd ⁢(𝛾,𝑝) and 102 Pd ⁢(𝛾,𝛼) for the astrophysical 𝑝 process

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.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Measurement of the production cross section of prompt $\Xi ^0_{\textrm{c}}$ baryons in p–Pb collisions at $\sqrt{s_{{\textrm{NN}}}}=5.02$ TeV

The transverse momentum (p T ) differential production cross section of the promptly produced charm-strange baryon $\Xi ^0_{\textrm{c}}$ (and its charge conjugate $\Xi ^0_{\textrm{c}}$) is measured at midrapidity via its hadronic decay into π + $\Xi$ – in p–Pb collisions at a centre-of-mass energy per nucleon–nucleon collision $\sqrt{s_{{\textrm{NN}}}}=5.02$ TeV with the ALICE detector at the LHC. The $\Xi ^0_{\textrm{c}}$ nuclear modification factor (R pPb ), calculated from the cross sections in pp and p–Pb collisions, is presented and compared with the R pPb of Λ$^{+}_{c}$ baryons. The ratios between the p T -differential production cross section of $\Xi ^0_{\textrm{c}}$ baryons and those of D 0 mesons and Λ$^{+}_{c}$ baryons are also reported and compared with results at forward and backward rapidity from the LHCb Collaboration. The measurements of the production cross section of prompt $\Xi ^0_{\textrm{c}}$ baryons are compared with a model based on perturbative QCD calculations of charm-quark production cross sections, which includes only cold nuclear matter effects in p–Pb collisions, and underestimates the measurement by a factor of about 50. This discrepancy is reduced when the data is compared with a model that includes string formation beyond leading-colour approximation or in which hadronisation is implemented via quark coalescence. The p T -integrated cross section of prompt $\Xi ^0_{\textrm{c}}$-baryon production at midrapidity extrapolated down to p T = 0 is also reported. These measurements offer insights and constraints for theoretical calculations of the hadronisation process. Additionally, they provide inputs for the calculation of the charm production cross section in p–Pb collisions at midrapidity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Machine Learning Neutrino-Nucleus Cross Sections

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section—leveraging only Standard-Model symmetries— can be learned from near-detector data. We perform a neutrino oscillation analysis with simulated far-detector events, finding that oscillation analysis results enabled by our data-driven cross-section model approach the theoretical limit achievable with perfect prior knowledge of the cross section. We further quantify the effects of flux shape and detector resolution uncertainties as well as systematics from cross-section mismodeling. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗