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This repository contains data required to reproduce the figures of a Physics of Plasmas publication about ensemble analysis of x-ray spectra from micro-stripe laser targets.
Large-momentum effective theory is a physics-guided systematic expansion to calculate light-cone parton distributions, including collinear (PDFs) and transverse-momentum-dependent ones, at any fixed momentum fraction 𝑥 within a range of [𝑥 min , 𝑥 max ]. It theoretically solves the ill-posed inverse problem that afflicts other theoretical approaches to collinear PDFs, such as short-distance factorizations. Recently, Dutrieux et al. raised practical concerns about whether current or even future lattice data will have sufficient precision in the subasymptotic correlation region to support an error-controlled extrapolation—and if not, whether it becomes an inverse problem where the relevant uncertainties cannot be properly quantified. While we agree that not all current lattice data have the desired precision to qualify for an asymptotic extrapolation, some calculations do, and more are expected in the future. We comment on the analysis and results in Dutrieux et al. and argue that a physics-based systematic extrapolation still provides the most reliable error estimates, even when the data quality is not ideal. In contrast, reframing the long-distance asymptotic extrapolation as a data-driven-only inverse problem with ad hoc mathematical conditioning could lead to unnecessarily conservative errors.
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.
In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential for specialized applications in segmented inverse beta decay (IBD) neutrino detectors, astronomy, machine learning, and more. Although this method may easily extend to 3D scalar fields, our focus here is on 2D real-valued fields as it directly applies to directionality. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.
Abstract. This study examines the linkages between the upper-ocean (0–200 m) oxygen (O2) content and stratification in the North Pacific Ocean using four Earth system models (ESMs), an ocean hindcast simulation, and an ocean reanalysis. The trends and variability in oceanic O2 content are driven by the imbalance between physical supply and biological demand. Physical supply is primarily controlled by ocean ventilation, which is responsible for the transport of O2-rich surface waters to the subsurface. Isopycnic potential vorticity (IPV), a quasi-conservative tracer proportional to density stratification that can be evaluated from temperature and salinity measurements, is used herein as a dynamical proxy for ocean ventilation. The predictability potential of the IPV field is evaluated through its information entropy. The results highlight a strong O2–IPV connection and somewhat higher (as compared to the rest of the basin) predictability potential for IPV across the tropical Pacific, where the El Niño–Southern Oscillation occurs. This pattern of higher predictability and strong anticorrelation between O2 and stratification is robust across multiple models and datasets. In contrast, IPV at mid-latitudes has low predictability potential and its center of action differs from that of O2. In addition, the locations of extreme events or hotspots may or may not differ between the two fields, with a strong model dependency, which persists in future projections. On the one hand, these results suggest that it may be possible to monitor ocean O2 in the tropical Pacific based on a few observational sites co-located with the more abundant IPV measurements; on the other, they lead us to question the robustness of the IPV–O2 relationship in the extratropics. The proposed framework helps to characterize and interpret O2 variability in relation to physical variability and may be especially useful in the analysis of new observation-based data products derived from the BGC-Argo float array in combination with the traditional but far more abundant Argo data.
The ICARUS collaboration employed the 760-ton T600 detector in a successful three-year physics run at the underground LNGS laboratory. In 2021, ICARUS started its new operation at Fermilab, collecting a substantial amount of neutrino events from the Booster Neutrino Beam (BNB) and the neutrinos at the Main Injector (NuMI) beam off-axis. These were used to test the ICARUS event selection, reconstruction, and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, moving then to data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by the Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurements of neutrino cross sections in LAr with the NuMI beam and several Beyond Standard Model studies. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this work, preliminary results from the ICARUS data with the BNB and NuMI beams are presented, both in terms of the performance of all ICARUS subsystems and the capability to select and reconstruct neutrino events.
We report on several efforts to search for physics beyond the standard model of particle physics at broad energy scales. The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the muon to electron conversion process, which is suppressed in the Standard Model. Mu2e will be operated at a low energy, yet can probe New Physics at very high mass scales (O(1e3 - 1e4 ) TeV). At high energies, the CMS experiment at the CERN LHC continues to deliver an impressive suite of Standard Model measurements and limits on a variety of New Physics signatures. Mu2e is under construction and slated to collect its first physics data in the coming years. This thesis describes work done during the construction phase of Mu2e and focuses on two critical areas: magnetic field modeling and statistical analysis. We describe a novel method for field modeling which we validate using a simulated dataset representing the expected magnetic field in the Detector Solenoid. This method blends a standard least-squares fitting technique that utilizes physically motivated analytical model functions with a novel physics informed network that is constructed to obey Maxwell’s equations. We show the technique can model the field with an accuracy of 10−7 despite the presence of injected noise in the pseudo-measurements at the 10−5 level. We then present preliminary results of the calibration of 3D Hall probes at the sub-10−4 level. These probes will be used to directly measure the Mu2e Detector Solenoid magnetic field on a sparse grid; these measurements serve as the input to the field model fitting. Finally, we describe the first implementation of both an unbinned shape analysis and a Bayesian interpretation applied to Mu2e pseudo-data. Up to 20% tighter limits can be set by the shape analysis compared to a standard cut & count analysis. The AlCap experiment collected data at PSI in 2015 to measure several important quantities related to nuclear muon capture on an aluminum target, which is a significant background process for Mu2e. The neutron emission from muon capture can introduce background hits in the Mu2e detectors and can increase radiation damage in various elements of the apparatus. We present measurements of the neutron group fluence and mean neutron multiplicity for muon capture on aluminum nuclei. Finally, we discuss an analysis of triboson production at CMS using an Effective Field Theory framework. Standard Model triboson production, which was first observed at CMS in 2020, has a relatively small cross section and provides direct access to both anomalous triple gauge couplings and quartic gauge couplings. These couplings, interpreted in the Standard Model Effective Field Theory, are studied in the present work. We target the boosted regime where the background rate is low and yields are enhanced when dimension-6 and dimension-8 Wilson coefficients are non-zero. We do not observe an excess in the data and therefore set bounds on the Wilson coefficients. For dimension-6 coefficients the tightest observed (expected) bounds are set on cW /Λ2 where Λ is the mass scale of new physics; the bounds are [−0.13, 0.12] TeV−2 ([−0.12, 0.12] TeV−2 ) at 95% CL. The tightest bounds in dimension-8 are set on fT,0 / Λ4 ; the observed (expected) bounds at 95% CL are [−0.63, 0.69] TeV−4 ([−0.54, 0.62] TeV−4 ). Additional results are presented which include scenarios where multiple Wilson coefficients are non-zero, the application of signal model clipping to address unitarity violation in Effective Field Theories, and a novel template fit developed for easier reinterpretation of our results.
X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.
In nuclear and particle physics, reconciling sophisticated simulations with experimental data is vital for understanding complex systems like the Quark Gluon Plasma (QGP) generated in heavy ion collisions. However, computational demands pose challenges, motivating using Gaussian Process emulators for efficient parameter extraction via Bayesian calibration. We conduct a comparative analysis of Gaussian Process emulators in heavy-ion physics to identify the most adept emulator for parameter extraction with minimal uncertainty. Furthermore, our study contributes to advancing computational techniques in heavy-ion physics, enhancing our ability to interpret experimental data and understand QGP properties.
The standard “A Compact ENDF (ACE)” data libraries used by Monte Carlo based reactor physics codes calculations are provided by Los Alamos National Laboratory (LANL) with a temperature interval mostly of 300 K (e.g. 300 K, 600 K, 900 K) for the cross sections and between 100 K and 200 K for the thermal scattering libraries (TSL). However, some codes such as MCNP lack capability to perform on-the-fly temperature interpolation during simulation both for neutron and TSL cross-sections. To evaluate the impact related to Doppler broadening and spectrum shift associated with TSL changes, this paper explores the potential of adopting a temperature grid finer than the ones contained in the standard data libraries. A 50 K temperature grid was employed to quantify the error in neutronics calculations due to temperature grid resolution. This was achieved by comparing the results of this study (50 K temperature interval) against the results obtained with standard data libraries (>100 K temperature interval). While the adopted grid primarily relies on the ENDF/B-VII.1 library, for neutron cross-sections, it utilizes ENDF/B-VIII.0 library for TSL. The analyses confirmed that the accuracy of neutronics calculations is satisfactory when using a 50 K temperature grid. Notably, adopting a 50 K temperature grid, as opposed to standard libraries or coarser temperature grids, could lead to a difference of no more than a few hundred pcm in dk for both fresh fuel and burnt fuel. The most sensitive reaction type to the temperature grid was as expected identified as the capture cross-section of U-238.
In inertial confinement fusion experiments at the National Ignition Facility, asymmetries are probed by a variety of neutron diagnostics, including neutron imaging systems, real-time neutron activation diagnostics (RTNADs), and neutron spectrometers. It is often useful to generate synthetic data based on these diagnostics to validate and tune models. However, current methods of doing so using Monte Carlo particle tracing are time-consuming. In this paper, an ultra-fast method is presented for generating synthetic neutron images, RTNAD data, and spectrometry data using line integrals and 3D convolutions. While it does not contain as much physics as particle tracing codes, it is thousands of times faster and produces nearly identical data. This enables analysis techniques that depend on generating large amounts of synthetic data, which will prove very useful for the study of asymmetries going forward.
The diesel-piloted dual-fuel compression ignition combustion strategy is well-suited to accelerate the decarbonization of transportation by adopting hydrogen as a renewable energy carrier into the existing internal combustion engine with minimal engine modifications. Despite the simplicity of engine modification, many questions remain unanswered regarding the optimal pilot injection strategy for reliable ignition with minimum pilot fuel consumption. The present study uses a single-cylinder heavy-duty optical engine to explore the phenomenology and underlying mechanisms governing the pilot fuel ignition and the subsequent combustion of a premixed hydrogen-air charge. The engine is operated in a dual-fuel mode with hydrogen premixed into the engine intake charge with a direct pilot injection of n-heptane as a diesel pilot fuel surrogate. Optical diagnostics used to visualize in-cylinder combustion phenomena include high-speed IR imaging of the pilot fuel spray evolution as well as high-speed HCHO* and OH* chemiluminescence as indicators of low-temperature and high-temperature heat release, respectively. Three pilot injection strategies are compared to explore the effects of pilot fuel mass, injection pressure, and injection duration on the probability and repeatability of successful ignition. The thermodynamic and imaging data analysis supported by zero-dimensional chemical kinetics simulations revealed a complex interplay between the physical and chemical processes governing the pilot fuel ignition process in a hydrogen containing charge. Hydrogen strongly inhibits the ignition of pilot fuel mixtures and therefore requires longer injection duration to create zones with sufficiently high pilot fuel concentration for successful ignition. Results show that ignition typically tends to rely on stochastic pockets with high pilot fuel concentration, which results in poor repeatability of combustion and frequent misfiring. In conclusion, this work has improved the understanding on how the unique chemical properties of hydrogen pose a challenge for maximization of hydrogen’s energy share in hydrogen dual-fuel engines and highlights a potential mitigation pathway.
Conventional digital computing faces fundamental physical limits: large scale computing systems already con sume tens of Megawatts of power, Dennard scaling has ended, and data movement costs dominate application performance. Next generation experimental facilities generate data at rates that overwhelm conventional pro cessing and demand real-time analysis at the source. Analog computing, which exploits the continuous dynamics of physical systems to perform computation, promises a transformative path toward orders-of-magnitude gains in energy efficiency and time-to-solution for scientific workloads.
High-energy-density plasma experiments are central to progress in atomic physics, fusion energy, and national security science, but they have traditionally been constrained by slow data collection and manual, time-intensive analysis. This project targeted that bottleneck by enabling high-repetition-rate experiments to produce and interpret much larger volumes of data quickly enough to guide experiments while they run.
We present the analysis techniques developed to explore the keV-scale energy region of the Cryogenic Underground Observatory for Rare Events (CUORE) experiment, based on more than 2 metric ton yr of data collected over five years. By prioritizing a stricter selection over a larger exposure, we are able to optimize data selection for thresholds at 10 keV and 3 keV with 691 kg yr and 11 kg yr of data, respectively. We study how the performance varies among the 988-detector array with different detector characteristics and data-taking conditions. We achieve an average baseline resolution of 2.54 ±0.14 keV FWHM and 1.18 ±0.02 keV FWHM for the data selection at 10 keV and 3 keV, respectively. The analysis methods employed reduce the overall background by about an order of magnitude, reaching 2.06±0.05 counts/(keV kg days) and 16±2 counts/(keV kg days) at the thresholds of 10 keV and 3 keV. We evaluate for the first time the near-threshold reconstruction efficiencies of the CUORE experiment, and find these to be 50 ±2% and 26 ±4% at 10 keV and 3 keV, respectively. This analysis provides crucial insights into rare decay studies, new physics searches, and keV-scale background modeling with CUORE. We demonstrate that ton-scale cryogenic calorimeters can operate across a wide energy range, from keV to MeV, establishing their scalability as versatile detectors for rare event and dark matter physics. These findings also inform the optimization of future large mass cryogenic calorimeters to enhance the sensitivity to low-energy phenomena.
Westcott 𝑔 factors are used in Neutron Activation Analysis (NAA) and Prompt Gamma-ray Activation Analysis (PGAA) to evaluate the impact of non-1∕𝑣 behavior in the neutron-capture cross sections of certain nuclei on activation product yields. This non-1∕𝑣 behavior arises from the presence of neutron resonances in the neutron- capture cross sections that overlap with the source neutron spectrum at low (< 5 eV) energies. Historically, Westcott 𝑔 factors that have been cataloged for NAA and PGAA applications are the result of calculations that assume a Maxwellian neutron flux distribution with a given temperature. In this work, we use this approach with updated neutron-capture cross sections from the Evaluated Nuclear Data File, version VIII.1 (ENDF/B-VIII.1) to tabulate Westcott 𝑔 factor values for a broad range of Maxwellian distribution temperatures, comparing the results against currently-available 𝑔 factors from International Atomic Energy Agency tables and other sources. Here, it was discovered during this analysis that the use of guided thermal and cold-neutron beams at certain facilities necessitates an approach for evaluating Westcott 𝑔 factors based on arbitrary non-Maxwellian spectra. In this paper, we present an approach for calculating 𝑔 factors with user-specified neutron spectra, and we demonstrate these methods to obtain Westcott 𝑔-factors for guided- and cold-neutron beams at the Budapest Research Reactor and the Forschungsreaktor München II reactor. As part of this work, open-source software has been developed that can be used to perform these calculations for applications in PGAA and NAA experiments.
The ICARUS collaboration employed the 760-ton T600 detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous $\nu_e$ appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 $eV^2$. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operation collecting the first neutrino events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, moving then to data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurement of neutrino cross sections in LAr with the NuMI beam and several Beyond Standard Model studies. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this presentation, preliminary results from the ICARUS data with the BNB and NuMI beams are presented both in terms of performance of all ICARUS subsystems and of capability to select and reconstruct neutrino events.
We report the first observation and measurement of antiproton annihilation at rest on argon track and shower multiplicities and particle identification conducted with the LArIAT experiment. Stopping antiprotons from the Fermilab Test Beam Facility’s charged particle test beam are identified using beamline instrumentation and LArIAT’s liquid argon time projection chamber (LArTPC). The charged particle multiplicity from the annihilation vertex is manually evaluated via hand scanning, yielding a mean of 3.2 ± 0.4 tracks and a standard deviation of 1.3 tracks, consistent with a semiautomated reconstruction resulting in 2.8 ± 0.4 tracks and a standard deviation of 1.2 tracks. Both methods are consistent with Monte Carlo simulations within statistical uncertainty. The shower multiplicities and particle identification for outgoing tracks are also consistent with eant4 model predictions. These results, obtained from a low-statistics sample, provide a foundation for higher-statistics studies in larger LArTPCs, which could refine modeling of intranuclear annihilation on argon and inform scenarios such as neutron-antineutron oscillations. Published by the American Physical Society 2025