Unveiling complex magnetic interactions in RE2In (RE = Nd, Pr) compounds by neutron diffraction
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The Electron–Ion Collider (EIC) will be a novel experimental facility to explore the properties of gluons in nucleons and nuclei, shedding light on their structure and dynamics. The EIC community outlined the physics program of the EIC in a White Paper, and the demanding detector re quirements and potential technologies to deploy at an EIC detector were published in a comprehensive Yellow Report. The general-purpose detector resulting from these efforts, ePIC, is designed to perform a broad physics program. At the same time, the wider EIC community is strongly in favor of a second detector at the EIC. Having two general-purpose collider detec tors to support the EIC science program allows us to have cross-checks and control of systematic uncertainties for potential scientific discoveries. The second detector should feature complementary technologies where possible. It can also focus on specific measurements that are less well-addressed by ePIC. The second interaction region provides potentially improved forward detector acceptance at low pT and a secondary beam focus that enables to enhance the exclusive, tagging, and diffractive physics program. Hereby, I will present the potential capabilities of the second detector and discuss studies related to its diffractive physics program.
This document describes a method to handle correlated systematic uncertainties from hadron production on the beam spectrum by using the neutrino ·data from the MINOS detector. We have developed a tuning function which can be applied after the fact to the Monte Carlo to force agreement with the CC neutrino energy spectra observed in the near detector and the beam MC. The method relies on the flexible NuMI beam, which can be configured so as to selectively sample pions. of different momenta and angles off the target. There is sufficient information in the neutrino data in a detector like MINOS to deduce the portion of the underlying spectrum of hadrons off the NuMI target which contribute to the NuMI flux.
Simulations are run using the new DUNE CAFAna framework to generate pseudo-data modeling the interactions of neutrinos in the DUNE near detector at truth-level and detector-level. Truth-level analysis of neutrino kinematics reveals strong agreement with expected behavior, validating the kinematic portion of the simulation. Examination of the detector-level reconstructions of coordinates of interaction vertex appear consistent with an interaction density independent of detector position. Track lengths of particles resultant from neutrino interactions are aligned with varied particle identities, but are misaligned with prediction of uniform position density.
Pyrochemical reprocessing technologies can be used to recover valuable materials from used nuclear fuel (UNF), such as uranium. However, other critical materials, such as fission product neodymium, are challenging to recover from molten chloride eutectic mixtures (LiCl-KCl) due to the presence of multivalent states (Nd2+/Nd3+), disproportionation reactions, and re-dissolution in the molten chloride salt. Understanding, predicting, and controlling these processes is further complicated by the presence of ionizing radiation fields, for which little is known on the interaction of neodymium ions with the radiolytic transients (es– and Cl2.–) in molten LiCl-KCl salt mixtures. Here, chemical kinetics, from integrated electron pulse irradiation and transient absorption spectroscopy, are presented for the reaction of Nd3+ ions with es– and Cl2.– as a function of temperature (400–600 ?).
ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.
Temporal relationships among the three most common suites of lunar crustal rocks have been investigated by obtaining new high precision ages on Felsic/Alkali-suite Quartz monzodiorite Clast B from breccia 15405 and Magnesian-suite norite 78235/6/8/55/56 and comparing them to previously dated ferroan anorthosite sample 60025. The weighted average age of 4337.19 ± 0.49 Ma of 15405 Clast B is defined by zircon U-Pb and Pb-Pb ages as well as mineral isochron Sm-Nd and Nd-Nd ages. It is identical to the weighted average age for Apollo 17 norite 78235/6/8/55/56 of 4334.1 ± 3.5 Ma which is defined by Pb-Pb ages measured on baddeleyites in this investigation and less precise Pb-Pb and Sm-Nd ages reported in the literature. Both ages are ∼ 25 Ma younger than the weighted average of Sm-Nd and Pb-Pb ages reported in the literature on ferroan anorthosite 60025 of 4359.3 ± 2.3 Ma. The fact that ages of all three samples are defined by multiple U-Pb, Pb-Pb, Sm-Nd, and 142 Nd- 143 Nd chronometers provide confidence that they record the igneous crystallization history of the samples and do not represent disturbances or mixing lines with no temporal significance. Here, the extent to which these three ages represent broader scale magmatism is difficult to evaluate. Nevertheless, the age defined for 15405 Clast B, 78235/6/8/55/56, and 60025 are contemporaneous with the peak of ages observed in detrital zircons from the Apollo 12, 14, 15, and 17 landing sites (4340 ± 20 Ma), a Mg-suite Sm-Nd whole rock isochron defined by samples from Apollo 14, 15, 16, and 17 landing sites (4348 ± 25 Ma), and a Ferroan Anorthosite-suite Sm-Nd whole rock isochron defined by samples from the Apollo 15 and 16 landing sites (4354 ± 29 Ma). This implies that Ferroan Anorthosite-suite magmatism is temporally distinct and earlier than magmatism associated with the Mg-suite and the Felsic/Alkali-suite, as predicted by the lunar magma ocean model of lunar differentiation. The short 35 ± 10 Ma interval between primary ferroan anorthosite magmatism and secondary magmatism suggests that the lunar crust formed over a limited period of time. Although heat from decay of long-lived isotopes, large impacts, tidal heating associated with interactions between the Earth and Moon, and density driven overturn of the magma ocean have all been invoked to explain production of ancient secondary crustal magmatism, only tidal heating and cumulate overturn are consistent with the apparent short duration of secondary crustal magmatism and the great depth of crystallization implied for some Mg-suite samples. The initial ε 143 Nd values derived from the 15405 Clast B and 78238 Mg-suite norite isochrons, as well as a Mg-suite whole rock isochron are −0.23 ± 0.11, −0.27 ± 0.74, and −0.25 ± 0.09, respectively. They are identical within uncertainty indicating that Mg-suite and Felsic/Alkali-suite magmas were derived from materials that had the same time averaged Sm/Nd ratios since the formation of the solar system. This, combined with the contemporaneous nature of 15405 Clast B and 78235/6/8/55/56 Mg-suite norite, is consistent with evolution of both samples, and likely both magma suites, from a common source through closed system fractional crystallization or partial melting processes.
The Workshop for Applied Nuclear Data Activities (WANDA) is designed to increase communication among nuclear data (ND) users in multidisciplinary federal programs, ND producers, ND funders, and other ND experts. It also presents an opportunity to cross-pollinate ideas as well as introduce ND gaps identified by federal programs to ND experts and ND capabilities to the various federal ND users. WANDA 2024 included five technical sessions, three of which focused on Fusion Energy Sciences (FES)—FES Fusion Neutronics, FES Tritium Production, and FES Material Damage—and two stand-alone sessions—Isotopes and Targetry for Nuclear Data and Uncertainty Quantification. The FES sessions successfully brought new voices to the WANDA discussions, expanding the application space in which nuclear data are critical. FES programs need accurate nuclear data with realistic uncertainty quantification to properly estimate, for example, shielding, activation, tritium production, helium production, structural material integrity, and superconducting magnet operation. This includes a variety of projectile (neutrons, photons, charged particles) and target atoms. One of the action items common to all the FES sessions was a need to perform sensitivity studies to identify the prioritization of nuclear data needs. The Isotopes and Targetry session highlighted the many capabilities available to produce high-quality targets for nuclear data measurements, including 3D printing with spherical powders, combustion synthesis coupled with spin coating & electrospraying, inkjet printing, and isotopic doping. These new methods open doors for more accurate measurement, but it was also stressed that sample characterization following any method of fabrication is of the highest importance to accurately interpret nuclear data measurement results that used that sample. The Uncertainty Quantification (UQ) session was broken into two categories: nuclear data uncertainty quantification and the use of that uncertainty quantification. Thematic to the UQ session was the loss of information when going from nuclear data measurement, to evaluation, to evaluated file, and finally to neutron transport calculations. Current evaluated ND libraries typically only contain covariances, which assume that the probability distributions are Gaussian. Beyond being a simplified assumption for many evaluations, this can lead to negative values on many observables when attempting to sample the covariance. The covariance format, however, is very efficient in that a simple set of linear equations can transform uncertainty from parameters or cross sections to the application of interest. Focused collaboration is needed between nuclear data evaluators and nuclear data users to ensure that needs are being met.
NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA's primary physics goals are the precise measurements of neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ , determine the neutrino mass ordering, and constrain the value of $\delta_{CP}$, via the study of muon neutrino to electron neutrino oscillation. In the standard NOvA three-flavor analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data constrain the FD prediction through a ratio method. While this allows for systematic uncertainties sharing the same effects in both detectors to cancel, it remains an FD-only fit and does not fully leverage the constraining power of the high-statistics ND. This analysis proposes a simultaneous ND+FD fit using the PISCES method. PISCES (Parameter Inference with Systematic Covariance and Exact Statistics) is a framework designed to support complex configurations such as a joint ND+FD fit. This allows PISCES to take full advantage of the ND data to directly constrain systematic uncertainties across all samples. In PISCES, systematic uncertainties are encoded in a fractional covariance matrix, and statistical uncertainties are handled with a Poisson likelihood, making the approach well suited for low-statistics samples. For interpretability, we further use a Newton–Raphson + PCA method to recover per-systematic pulls from the covariance formulation. This poster presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, describes its implementation and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method.
A key aspect in the formation of rare earth elements (REE) deposits is the role of REE transport as aqueous REE complexes in supercritical hydrothermal solutions, where the nature of the aqueous complex is controlled by solution composition, temperature and pressure. Despite chloride being considered as one of the most abundant transporting ligands in magmatic-hydrothermal fluids, experimental investigations on the stability of aqueous REE chloride complexes are scarce above 300 °C. In this study, synthetic NdPO 4 crystals were reacted with non-saline and saline (0, 0.05 and 0.5 mNaCl), acidic (0.01 mHCl) aqueous solutions in a series of solubility experiments conducted at 500–700 °C and 1.7 kbar, where the solubilities were determined using a stable Nd isotope ( 145 Nd isotope spike) dilution technique. NdPO 4 solubility ranges between 28 ppm and 10,858 ppm, where solubility increases with both temperature and salinity. At 500 °C, log mNdPO 4 increases from –3.93 to –1.60 and there is a strong correlation between NdPO 4 solubility and NaCl concentrations (slope of 1.2 ± 0.3), indicating stabilization of the Nd chloride aqueous complexes with a stoichiometry corresponding to NdCl 2+ . At 600 °C, this correlation is weaker (slope of 0.4, log mNdPO 4 increases from –2.63 to –1.88) indicating the stabilization of both Nd chloride and hydroxyl species controlling solubility. At 700 °C, NdPO 4 solubility is largely independent of NaCl concentration indicating that solubility is controlled by Nd hydroxyl complexes, where stoichiometry suggests the neutral Nd(OH) 3 0 species is dominant. The solubility product (Ksp) of NdPO4 is derived from experimental data with the relation: log K sp = -41.81 – 0.057T – 20987/T, with T temperature in Kelvin. Comparison of the measured Nd phosphate solubility to thermodynamic predictions using the available Helgeson-Kirkham-Flowers equation of state parameters for aqueous Nd complexes indicate that predictions are up to three orders of magnitude lower compared to experimental observations. This discrepancy is most pronounced in saline solutions, suggesting that thermodynamic properties of the REE chloride species in supercritical fluids require revision. Numerical simulations of fluid-rock interaction between acidic, saline fluids and a Strange Lake felsic mineral assemblage demonstrates that NdPO 4 solubility predictions from models are four to six orders of magnitude lower than those calculated based on empirical fits from experiments, which suggests that acidic, saline fluids may play an important role in mobilizing large amounts of light REE from 450 to 700 °C.
Abstract. Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology.
NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.
The sources of the magmas that formed the Rustenburg Layered Suite of the Bushveld Complex in South Africa remain debated, despite decades of research. Vertical and lateral variation in bulk rock and mineral separate Sr-Nd isotopic compositions, which generally indicate enriched sources, demonstrate that the layered sequence was formed by the emplacement of multiple batches of magma, crucially resulting in episodes of PGE-Cr-V mineralisation. The Lu-Hf isotope compositions of zircon are, however, at odds with the bulk rock Sr-Nd isotopic heterogeneity as they show near homogeneous compositions throughout the layered sequence (εHf (2.06 Ga) =−8). This lack of variation in Hf isotope composition has been attributed to deep, continental lithospheric mantle-related and/or crustal contamination of plume-derived Bushveld magmas. In this study, we analysed the major, trace element and Sr-Nd isotope geochemistry of apatite in the Rustenburg Layered Suite. Apatite occurs as an intercumulus mineral in the lowermost regions and a cumulus mineral in the uppermost regions of the layered sequence and can therefore be used to test existing models for the isotopic disequilibrium between bulk rock Sr-Nd and zircon Hf isotopic compositions. Apatite is largely chlorapatite in the lowermost regions and fluorapatite in the uppermost regions of the layered sequence. The Merensky Reef is unusual in that it comprises both chlorapatite and fluorapatite. Apatite throughout the layered sequence is generally unzoned and shows no evidence of late-stage alteration. Trace element data show that apatite is enriched in L/HREE, with common negative Eu-Sr anomalies. These trace element signatures are consistent with a magmatic origin for the apatite grains, with prior, or concurrent, plagioclase crystallization from the same melt. Variability in in situ Sr and Nd isotope compositions of apatite is recorded throughout the layered sequence with εNd (2.06 Ga) compositions varying between −2.5 and − 10.2 and initial 87 Sr/ 86 Sr compositions varying between 0.7079 and 0.7103 (for the Marikana dikes only). The variability in Sr-Nd isotope compositions of apatite is consistent with the bulk rock (and mineral separate) variation in Sr-Nd isotope compositions, suggesting apatite preserves primary magmatic compositions in the Rustenburg Layered Suite.
Abstract Tuning the properties of a pair of entangled electron and hole in a light-induced exciton is a fundamentally intriguing inquiry for quantum science. Here, using semiconducting hybrid perovskite as an exploratory platform, we discover that Nd 2+ -doped CH 3 NH 3 PbI 3 (MAPbI 3 ) perovskite exhibits a Kondo-like exciton-spin interaction under cryogenic and photoexcitation conditions. The feedback to such interaction between excitons in perovskite and the localized spins in Nd 2+ is observed as notably prolonged carrier lifetimes measured by time-resolved photoluminescence, ~10 times to that of pristine MAPbI 3 without Nd 2+ dopant. From a mechanistic standpoint, such extended charge separation states are the consequence of the trap state enabled by the antiferromagnetic exchange interaction between the light-induced exciton and the localized 4 f spins of the Nd 2+ in the proximity. Importantly, this Kondo-like exciton-spin interaction can be modulated by either increasing Nd 2+ doping concentration that enhances the coupling between the exciton and Nd 2+ 4 f spins as evidenced by elongated carrier lifetime, or by using an external magnetic field that can nullify the spin-dependent exchange interaction therein due to the unified orientations of Nd 2+ spin angular momentum, thereby leading to exciton recombination at the dynamics comparable to pristine MAPbI 3 .
The Deep Underground Neutrino Experiment (DUNE) is an international particle physics experiment looking answer some of the largest unanswered questions in neutrino physics. DUNE uses a high power neutrino beam produced at Fermi National Accelerator Laboratory (Fermilab), and consists of a near detector (ND) also located at Fermilab and a far detector (FD) 1300 km away at the Sanford Underground Research Facility (SURF) in South Dakota. In the first phase of the experiment, the ND complex will contain a modular liquid argon TPC (ND-LAr) and a solid scintillator-based muon spectrometer (TMS), in addition to a beam monitoring detector (SAND) and systems for moving ND-LAr and TMS away from the neutrino beam axis (PRISM). A prototype of ND-LAr, the 2x2 demonstrator, alongside a solid scintillator muon tagger provided by repurposed MINERvA planes, has been built and taken data at Fermilab. For analyses with the ND, connecting particle tracks (such as muons) that exit the liquid argon active volume into the solid scintillator muon detector can improve particle identification and energy reconstruction, and alleviate pileup due to the intense beam. To match tracks between detectors during reconstruction, we have explored using Graph Neural Networks (GNNs) to connect tracks segments between the liquid argon detector region and the solid scintillator detector planes. We have trained a GNN on reconstructed simulated data from the 2×2 demonstrator and repurposed MINERvA planes. We will evaluate its performance and then train a similar network on reconstructed ND-LAr and TMS simulations.
Abstract. Aerosol–cloud interactions (ACIs) are the largest source of uncertainty in inferring the magnitude of future warming consistent with the observational record. The effective radiative forcing due to ACI (ERFaci) is dominated by liquid clouds and is composed of two terms: the change in cloud albedo due to redistributing liquid over a larger number of cloud droplets (Nd) and the change in cloud macrophysical properties due to changes in cloud microphysics. These terms are, respectively, referred to as the radiative forcing due to ACI (RFaci) and aerosol–cloud adjustments. While the magnitude of RFaci is uncertain, its sign is confidently negative and results in a cooling in the historical record. In contrast, the adjustment of cloud liquid water path (LWP) to enhanced Nd and associated radiative forcing is uncertain in sign. Increased LWP in response to increased Nd is consistent with precipitation suppression, while decreased LWP in response to increased Nd is consistent with enhanced evaporation from cloud top. Observational constraints of these processes are poor in part because of causal ambiguity in the relationship between Nd and LWP. To better understand this relationship, precipitation (P), Nd, and LWP surface observations from the Eastern North Atlantic (ENA) atmospheric observatory are combined with the output from a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model version 6 (CAM6). This allows for causal interpretation of observed covariability. Observations of precipitation and cloud from ENA constrain the range of possible LWP aerosol–cloud adjustments relative to the prior from the PPE by 15 %, resulting in a global value that is confidently positive (a historical cooling) ranging from 2.1 to 6.9 g m−2. It is found that observed covariability between Nd and LWP is dominated by coalescence scavenging and that this observed covariability is not strongly related to aerosol–cloud adjustments.
Aerosol-cloud-precipitation interactions are assessed over the non-polar ocean using more than 11 years of combined Aqua-MODIS, CALIPSO-CALIOP, and CloudSat products. The analysis first shows the benefit of incorporating vertically resolved aerosol extinction coefficient (σext) in aerosol-cloud interactions (ACI) assessments, demonstrating that: σext vertically collocated with the cloud layer () correlates best with cloud droplet number concentration (Nd), column-integrated aerosol optical depth (AOD) cannot explain the Nd variability in the extratropics, and the S-shape of the AOD-Nd relationship reported in previous studies is not replicated when using instead of AOD, with a Nd- linearity more consistent with in-situ studies over the ocean. ACI metric, estimated as the log-scale regression between CALIOP and MODIS Nd reveals that the eastern Pacific is the region with the strongest ACI, followed by the Southern Ocean. The susceptibility of clouds to changes in their liquid water path (LWP) and frequency of precipitation followed a 2-step calculation by combining the Nd- regression (ACI) with the regression between these macrophysical variables and Nd. LWP susceptibility is negative (LWP decreases with aerosol loading) and statistically significant over the eastern Pacific, eastern Atlantic, and extratropics. In contrast, vast areas of the tropical and subtropical ocean feature negligible changes in LWP with aerosol. Precipitation frequency susceptibility is negative, but the values are only significant over the coastal eastern Pacific and Atlantic. The findings suggest that previous modeling assessments relying on AOD may need to be revisited by taking advantage of the synergy between passive and active sensors.