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DOE OSTI2025

Data-driven high-dimensional statistical inference with generative models

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the bbγγ final state, where the di-photon resonance allows for background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Search for supersymmetry using vector boson fusion signatures and missing transverse momentum in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

This paper presents a search for supersymmetric particles in models with highly compressed mass spectra, in events consistent with being produced through vector boson fusion. The search uses 140 fb −1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider. Events containing at least two jets with a large gap in pseudorapidity, large missing transverse momentum, and no reconstructed leptons are selected. A boosted decision tree is used to separate events consistent with the production of supersymmetric particles from those due to Standard Model backgrounds. The data are found to be consistent with Standard Model predictions. The results are interpreted using simplified models of R-parity-conserving supersymmetry in which the lightest supersymmetric partner is a bino-like neutralino with a mass similar to that of the lightest chargino and second-to-lightest neutralino, both of which are wino-like. Lower limits at 95% confidence level on the masses of next-to-lightest supersymmetric partners in this simplified model are established between 117 and 120 GeV when the lightest supersymmetric partners are within 1 GeV in mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1984

Analysis of head-down tilt as an analog of weightlessness using a methematical simulation model

Antiorthostasis or head down tilt of a moderate degree was used as a ground based analog of weightless space flight to study headward fluid shifts, decreased plasma volume, orthostatic intolerance and muscular skeletal degradation. A mathematical model was used to help interpret these observations. The model proved most valuable for these studies was originally developed as a description of the major circulatory, fluid and electrolyte control systems. Two different experimental studies are employed to validate the model. The first is a 24 hour head down tilt study and the second is a 7 day head down bed rest study. The major issues addressed include the reduction in plasma volume, the dynamic changes of venous pressure and cardiac output, the extent of central hypervolemia during long term zero g exposure, the existence of an early diuresis, the mechanisms which alter the renal regulating hormones during the short term and long term periods, the significance of potassium loss on other zero g responses, and the role of transcapillary filtration in adjusting fluid shifts. The use of mathematical models as an interpretive and analysis technique for experimental research for space life science is illustrated.

Leonard, J. I.↗

NASA NTRS2017

Impact of Evolving Isoprene Mechanisms on Simulated Formaldehyde: An Inter-comparison Supported by in Situ Observations from SENEX

Isoprene oxidation schemes vary greatly among gas-phase chemical mechanisms, with potentially significant ramifications for air quality modeling and interpretation of satellite observations in biogenic-rich regions. In this study, in situ observations from the 2013 SENEX mission are combined with a constrained O-D photochemical box model to evaluate isoprene chemistry among five commonly used gas-phase chemical mechanisms: CBO5, CB6r2, MCMv3.2, MCMv3.3.1, and a recent version of GEOS-Chem. Mechanisms are evaluated and inter-compared with respect to formaldehyde (HCHO), a high-yield product of isoprene oxidation. Though underestimated by all considered mechanisms, observed HCHO mixing ratios are best reproduced by MCMv3.3.1 (normalized mean bias = -15%), followed by GEOS-Chem (-17%), MCMv3.2 (-25%), CB6r2 (-32%) and CB05 (-33%). Inter-comparison of HCHO production rates reveals that major restructuring of the isoprene oxidation scheme in the Carbon Bond mechanism increases HCHO production by only approx. 5% in CB6r2 relative to CBO5, while further refinement of the complex isoprene scheme in the Master Chemical Mechanism increases HCHO production by approx. 16% in MCMv3.3.1 relative to MCMv3.2. The GEOS-Chem mechanism provides a good approximation of the explicit isoprene chemistry in MCMv3.3.1 and generally reproduces the magnitude and source distribution of HCHO production rates. We analytically derive improvements to the isoprene scheme in CB6r2 and incorporate these changes into a new mechanism called CB6r2-UMD, which is designed to preserve computational efficiency. The CB6r2-UMD mechanism mimics production of HCHO in MCMv3.3.1 and demonstrates good agreement with observed mixing ratios from SENEX (-14%). Improved simulation of HCHO also impacts modeled ozone: at approx. 0.3 ppb NO, the ozone production rate increases approx. 3% between CB6r2 and CB6r2-UMD, and rises another approx. 4% when HCHO is constrained to match observations.

isoprene↗

NASA NTRS1970

Computer discrimination procedures applicable to aerial and ERTS multispectral data

Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

Richardson, A. J.↗

NASA NTRS1971

Computer identification of ground pattern from aerial photographs.

Two statistical models are comapared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

Richardson, A. J.↗

NASA NTRS2003

Role of the Polar Oceans in Global Climate

The project focused on ice-ocean model development and in particular on the assimilation of ice motion data and ice concentration data into both regional and global models. Many of the resulting publications below deal with improvements made in the physics treated by the model and the procedures for assimilating data. Several papers examine how the ability of the model to simulate the past behavior of the ice cover, especially to represent the ice thickness and ice deformation, is improved by data assimilation. A second aspect of the work involved interpretation of modeled behavior. Resulting papers treat the decline of arctic ice thickness over the last thirty years, and how that decline was caused by a slight warming of the near-surface atmosphere, and also how large variation in ice thickness are due to changes in wind patterns associated with a well- known oscillation of the atmospheric circulation. The research resulted in over 20 published papers on these topics.

Rothrock, D. A.↗

NASA NTRS1976

The period-pulse-width distribution of pulsars and their division into three classes

The period-pulse-width (P-W) distribution of sample of 41 pulsars has been reanalyzed under two versions of a model where radiation is beamed from magnetic polar caps which are isotropically oriented with respect to the rotation axis of the neutron stars (IOPC models). It is found that neither IOPC model can explain the P-W distribution of the entire sample. Pulsars with simple unimodal (U) or complicated multimodal (M) pulse shapes show significantly different P-W distributions. Class M pulsars have a narrower P-W distribution than do class U pulsars and, taken separately, are compatible with either IOPC model. Class U pulsars divide into two subgroups, those with relatively narrower pulses (UN) and those with relatively wider pulses (UW), each of which is compatible with either IOPC model. One interpretation of these results is that the pulsars in classes UN, M, and UW have different intrinsic beam sizes, roughly in the ratios 1:2:4, respectively. The possible origins of the three classes are discussed in terms of the force-balance magnetosphere model (Roberts et al., 1972, 1973), where the differences may be attributed to different-mass neutron stars and the structure of the polar caps. An alternative interpretation is that the angle between the rotation and emission axes is not isotropically distributed, but can take only certain preferred values.

Roberts, D. H.↗

DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗

DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1962

A Preliminary Model of the Venus Atmosphere

“Standard’ and extreme model atmospheres of the planet Venus are calculated and presented, with temperature, density, and height given as functions of pressure, and with pressure, temperature, and density given as functions of height. Until more recent data are interpreted, the models are considered preliminary.

planetary entry↗

NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗

NASA NTRS1999

Interpretation of Lidar and Satellite Data Sets Using a Global Photochemical Model

A primary goal of the NASA Tropospheric Chemistry Program (TCP) is to "contribute substantially to scientific understanding of human impacts on the global troposphere". In order to analyze global or regional trends and factors of the troposphere chemistry, for example, its oxidation capacity or composition, a continuous global/regional data coverage as well as model simulations are needed. The Global Tropospheric Experiment (GTE), a major component of the TCP, provides data vital to these questions via aircraft measurement of key trace chemical species in various remote regions of the world. Another component in NASA's effort are satellite projects for exploration of tropospheric chemistry and dynamics. A unique data product is the Tropospheric Ozone Residual (TOR) utilizing global tropospheric ozone data. Another key research tool are simulation studies of atmospheric chemistry and dynamics for the theoretical understanding of the atmosphere, the extrapolation of observed trends, and for sensitivity studies assessing a changing anthropogenic impact to air chemistry and climate. In the context with model simulations, field data derived from satellites or (airborne) field missions are needed for two purposes: 1. To initialize and validate model simulations, and 2., to interpret field data by comparison to model simulation results in order to analyze global or regional trends and deviations from standard tropospheric chemistry and transport conditions as defined by the simulations. Currently, there is neither a sufficient global data coverage available nor are existing well established global circulation models. The NASA LARC CTM model is currently not yet in a state to accomplish a sufficient tropospheric chemistry simulation, so that the current research under this cooperative agreement focuses on utilizing field data products for direct interpretation. They will be also available for model testing and a later interpretation with a finally utilized model.

Zenker, Thomas↗

DOE OSTI2026

Z α 2 correction to superallowed beta decays in effective field theory and implications for | V u d |

Superallowed ( 0 + → 0 + ) beta decays currently provide the most precise extraction of quark mixing in the Standard Model. Their interpretation as a measurement of | V u d | relies on a reliable first-principles computation of QED radiative corrections expressed as a series in Z α and α . In this work, we provide the first model-independent result for two-loop, O ( Z α 2 ) , long-distance radiative corrections where the nuclei are treated as heavy pointlike particles. We use renormalization group analysis to obtain new results at O ( Z α 3 ) for the coefficient of double logarithms in the ratio of the maximal beta energy to the inverse nuclear size, E m / R - 1 . We use the Kinoshita-Lee-Nauenberg theorem to obtain new results at O ( Z 2 α 3 ) for the coefficient of logarithms in the ratio of maximal beta energy to the electron mass, log ( 2 E m / m ) . We identify a structure-dependent, and, therefore, short-distance, contribution to the traditional Z α 2 correction that should be revisited. We provide the first comprehensive update to the long-distance corrections in almost 40 years and comment on the impact of our findings for extractions of | V u d | . We find that shifts in the long-distance corrections are 2.5 × larger than past estimates of their uncertainty, 1.5 × larger than the statistical uncertainty from the combined fit of superallowed decays, and about 1 / 2 the size of estimated systematic error, which stems dominantly from nuclear structure effects.

Cao, Zehua [Kentucky U.] (ORCID:0009000354256423)↗

DOE OSTI2026

A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis

To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.

42 ENGINEERING↗

NASA NTRSDate not supplied

Performance of Heterogeneous Sulfate Aerosol Formation Mechanisms Against Aircraft and Ground-Based Observations in Seoul, South Korea

Haze events are a common occurrence in East Asia, where local meteorology combines with anthropogenic emissions under certain conditions to create rapid increases in secondary aerosol. Often these events are driven by rapid production of secondary sulfate-nitrate-ammonium (SIA) aerosol. Models have typically included simple parameterizations of the production of sulfate during these events which vary widely in their magnitude. There are a range of different mechanistic descriptions of the chemical processes occurring during these events in large part due to poor constraints on aerosol pH and transition metal content, both of which are important to determining the key pathway(s) driving aerosol production. Here, we use the GEOS-Chem chemical transport model to interpret detailed ground and aircraft observations from May and June 2016 during the Korea-United States Air Quality (KORUS-AQ) field study to evaluate the mechanistic pathways governing sulfate production during a haze event. We apply our model of sulfate production mechanisms to ground-based speciated aerosol measurements in winter, summer, and fall in Seoul.

Katherine R Travis↗

NASA NTRS1991

AIRSAR observations of the Gulf Stream with interpretation from sea truth and modeling

On 20 Jul., JPL/DC-8 synthetic aperture radar (SAR) participated in the 17-21 Jul. 1990 NRL Gulf Stream (GS) experiment in preparation for SIR-C missions in 1993, 1994, and 1996 for calibration purposes and to check modes and techniques for operation at our experimental site off the east coast of the US. During this experiment, coordinated and near simultaneous measurements were performed from ship (R/V Cape Henlopen) and other aircraft (NADC/P-3 and NRL/P-3) to address scientific questions relating to the origin of 'slick-like' features observed by Scully-Power, the refraction and modulation of waves by variable currents, the effect of current and thermal fronts on radar imagery signatures and the modification of Kelvin ship wakes by fronts. The JPL/DC-8 and NADC/P-3 SAR's are fully polarimetric systems. Their composite frequency range varies between P- and X-band. We describe in detail the Airborne SAR (AIRSAR) participation in the Jul. 1990 GS experiment and present preliminary results of the ongoing analysis and interpretation of the radar imagery in the context of ground truth, other remote measurements, and modeling efforts.

Valenzuela, G. R.↗

NASA NTRSDate not supplied

On the Importance of Laboratory Astrophysics and Astrochemistry and Interdisciplinary Research: Two Success Stories

Here we present two examples that demonstrate how cross-disciplinary research projects, where experimentalists, modelers and observers work together to answer science questions, allow expertise to be shared and misconceptions or missing key elements to be tackled by looking at the problem from different perspectives. They also show how, by working in unison, the group can accomplish more than the sum of its parts by combining results into a higher-level understanding of the chemical processes taking place. The first project is a collaborative study between experimentalists, modelers and observers to1) produce laboratory analogs of cosmic grains and planetary aerosols (Titan, Pluto...) from different gas mixtures in cold astrophysically relevant conditions (<200 K); and 2) characterize them with scanning electron microscopy and visible-to-far-infrared spectroscopy in order to assess the impact of the precursors on their growth structure and optical properties. We can then produce and study analogs that are representative of different formation stages or environments, and provide their complex refractive indices to the scientific community. We will show how, by using these experimental optical constants of more representative analogs in radiative transfer and reflectance spectra models, better interpretations of (exo)planetary atmosphere- and surface observations are possible. The second project is an interdisciplinary study of the formation of benzene clouds in the atmosphere of Saturn’s largest moon, Titan. We will show how combining Earth and Planetary Science laboratory expertise, modeling and observations has led to providing to the scientific community, for the first time, experimental vapor pressures for benzene at cold temperatures(<200K) relevant to Titan’s atmospheric conditions. These have been used in microphysical models instead of the higher temperature extrapolations used previously, allowing a better match to observations.

Ella Sciamma-o'Brien↗