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DOE OSTI2025

Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94 compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗

DOE OSTI2024

Skeletal Kinetics Reduction for Astrophysical Reaction Networks

A novel methodology is developed to extract accurate skeletal reaction models for nuclear combustion. Local sensitivities of isotope mass fractions with respect to reaction rates are modeled based on the forced optimally time-dependent (f-OTD) scheme. These sensitivities are then analyzed temporally to generate skeletal models. The methodology is demonstrated by conducting skeletal reduction of constant density and temperature burning of carbon and oxygen relevant to Type Ia supernovae (SNe Ia). The 495-isotopes Torch model is chosen as the detailed reaction network. A map of maximum production of 56 Ni in SNe Ia is produced for different temperatures, densities, and proton-to-neutron ratios. The f-OTD simulations and the sensitivity analyses are then performed with initial conditions from this map. A series of skeletal models are derived and their performances are assessed by comparison against currently existing skeletal models. Previous models have been constructed intuitively by assuming the dominance of α-chain reactions. The comparison of the newly generated skeletal models against previous models is based on the predicted energy release and 44 Ti and 56 Ni abundances by each model. The consequences of ye ≠ 0.5 in the initial composition are also explored where ye is the electron fraction. The simulated results show that 56 Ni production decreases by decreasing ye as expected, and that the 43 Sc is a key isotope in proton and neutron channels toward 56 Ni production. It is shown that an f-OTD skeletal model with 150 isotopes can accurately predict the 56Ni abundance in SNe Ia for ye ≲ 0.5 initial conditions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate‐based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near‐surface air temperature (T2m). Average physics uncertainties are determined to be 1.5C for LST and T2m over land, and 1.9C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A data-driven method to estimate the antiproton background in the Mu2e experiment

The Mu2e experiment at Fermilab will search for the Charged Lepton Flavour Violating (CLFV) process of coherent, neutrinoless µ− → e − conversion in the field of an aluminum nucleus. The expected signal is a monochromatic electron with the energy of 104.97 MeV, slightly below the muon rest mass. Observation of a CLFV process would provide unambiguous evidence for Beyond the Standard Model (BSM) physics. Mu2e is sensitive to a wide range of BSM models and has the capability to distinguish between them, guiding us towards the most accurate models. The key features of the Mu2e experiment are: (1) a high intensity pulsed negative muon beam with about 1010 stopped µ −/s, and (2) a sophisticated superconducting solenoid system with a gradient magnetic field to form and guide the intense muon beam to the target. The Mu2e physics data taking is expected to begin in 2027. For Run I, the expected 5σ discovery sensitivity is Rµe = 1.2 × 10−15, with a total expected background of 0.11 ± 0.03 events. In the absence of a signal, the expected upper limit is Rµe < 6.2 × 10−16 at 90% CL. The success of this experiment hinges on the accurate estimation of the background from various SM processes that could provide signal-like electrons. One of the background processes is antiprotons annihilating in the stopping target to produce signal like electrons through π0 → γγ decays followed by γ conversions, and π− → µ−ν¯ decays followed by µ− decay. It is a relatively small background with large uncertainty (100%) due to the lack of antiproton production cross section information for the Mu2e proton beam energy of 8 GeV. We have developed a novel methodology to estimate the antiproton background in-situ. This forms the main theme of the thesis. We observed that at Mu2e energies, antiproton annihilation in the stopping target is the only source of events with multiple, simultaneous particle trajectories. From Geant4 simulations, only about 0.2% of the simulated antiproton annihilation events have a signal-like electron. Meanwhile, ∼ 5% of events have multiple reconstructible particle tracks per event. Therefore, we have devised a methodology to reconstruct the multi-track events and estimate the antiproton background by exploiting the large ratio of the production rates of the two final states.

Chithirasreemadam, Namitha [Pisa U.] (ORCID:000000↗

DOE OSTI2026

Probing Nuclear Effects with Transverse Kinematic Imbalance in Muon-neutrino Induced Charged-Current $π^0$ Production on Argon with the MicroBooNE Detector

Neutrino-nucleus cross-section measurements are needed to improve interaction modeling and to enable precision neutrino oscillation measurements in upcoming experiments such as the Deep Underground Neutrino Experiment (DUNE), Hyper-Kamiokande, and the Short-Baseline Neutrino program. Baryon-resonance neutrino interactions constitute a dominant contribution near the peak of the DUNE neutrino energy spectrum. We present the first measurement of muon neutrino charged-current resonance-like interactions on argon using transverse kinematic imbalance variables with the MicroBooNE detector. These observables are highly sensitive to the modeling of final-state interactions. This measurement probes kinematic imbalances using the reconstructed momenta of the muon, leading proton, and neutral pion. A comprehensive characterization of the $π^0$-proton final state is presented; however, none of the models considered are able to simultaneously reproduce all measured observables.

Abratenko, P. [Tufts U. (main)]↗

DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗

DOE OSTI2026

SPT-3G D1: A Measurement of Secondary Cosmic Microwave Background Anisotropy Power

We report new measurements of millimeter-wave temperature power spectra in the angular multipole range $1700 \le \ell \le 11,000$ (wavelengths $13^\prime \gtrsim λ\gtrsim 2^\prime$). We use two years of data in three observing bands centered near 95, 150, and 220 GHz from the SPT-3G receiver on the South Pole Telescope that cover a 1646 deg$^2$ region of the Southern sky. Using the measured power spectra, we present constraints on the thermal and kinematic Sunyaev-Zel'dovich (SZ) effects, radio galaxies, and cosmic infrared background (CIB). We find that inferred SZ powers are dependent on the detailed modeling of the thermal SZ-CIB correlation, and to a lesser extent on the assumed angular dependence of the SZ spectra. We report constraints for simulation-based model templates as well as fits where the angular dependencies of the SZ and CIB power spectra are allowed to vary. In the latter case at $\ell=3000$, we find thermal SZ power at 143 GHz of $D_{3000}^{\rm tSZ} = 4.91\pm0.37\, μ{\rm K}^2$ and kinematic SZ power of $D_{3000}^{\rm kSZ} =1.75\pm0.86\, μ{\rm K}^2$. We use the measured kinematic SZ power to estimate the duration of reionization, noting that the reionization inferences are sensitive to the model choices and assumed level of homogeneous kinematic SZ power from the late-time universe. We find a 95% limit on the duration from an ionization fraction of 25% to 75% of $Δ^{50} z_{\rm re} <\,3.8$ based on a semi-analytic model, or a limit on the duration from an ionization fraction of 5% to 95% of $Δ^{90} z_{\rm re} <\,6.1$ based on the AMBER simulations.

Chaubal, P. [Melbourne U.]↗

DOE OSTI2025

In vivo mapping of mutagenesis sensitivity of human enhancers

Distant-acting enhancers are central to human development1. However, our limited understanding of their functional sequence features prevents the interpretation of enhancer mutations in disease2. Here we determined the functional sensitivity to mutagenesis of human developmental enhancers in vivo. Focusing on seven enhancers that are active in the developing brain, heart, limb and face, we created over 1,700 transgenic mice for over 260 mutagenized enhancer alleles. Systematic mutation of 12-base-pair blocks collectively altered each sequence feature in each enhancer at least once. We show that 69% of all blocks are required for normal in vivo activity, with mutations more commonly resulting in loss (60%) than in gain (9%) of function. Using predictive modelling, we annotated critical nucleotides at the base-pair resolution. The vast majority of motifs predicted by these machine learning models (88%) coincided with changes in in vivo function, and the models showed considerable sensitivity, identifying 59% of all functional blocks. Taken together, our results reveal that human enhancers contain a high density of sequence features that are required for their normal in vivo function and provide a rich resource for further exploration of human enhancer logic.

Kosicki, Michael↗

DOE OSTI2025

Data-Driven Mean-Corrected Recursive Estimation-Based Optimal DER Dispatch for Distribution System Voltage Control

Recent advances in smart inverters offer opportunities to mitigate adverse grid impacts caused by high penetrations of distributed photovoltaics (PV) in distribution grids, such as voltage violations. Here, this paper proposes a novel measurement-driven optimal power flow (OPF)-based distributed energy resource management system (DERMS) voltage regulation via recursive sensitivity estimation informed coordinated control of distributed PV inverters. The proposed approach leverages available grid and controllable DER measurements, eliminating reliance on system model information while being adaptive and robust to volatile operating conditions. A mean-corrected recursive ridge regression (MCRRR) algorithm is proposed for sensitivity estimation, continuously refining the sensitivity model through a closed-form solution. It effectively manages varying grid operating conditions, such as changes in power injections and topology reconfiguration, to facilitate a time-varying update of the Load Sensitivity Factors (LSF). The proposed approach is formulated as a linear programming (LP) problem and is thus scalable to larger-scale distribution systems. Its effectiveness and efficiency are demonstrated on a realistic distribution feeder with high PV penetrations in Southern California, USA.

14 SOLAR ENERGY↗

DOE OSTI2025

Particle production as a function of charged-particle flattenicity in 𝑝⁢𝑝 collisions at $\sqrt{s}$ = 13 TeV

This paper reports the first measurement of the transverse momentum (𝑝 T ) spectra of primary charged pions, kaons, (anti)protons, and unidentified particles as a function of the charged-particle flattenicity in pp collisions at $\sqrt{s}$ =13 TeV. Flattenicity is a novel event shape observable that is measured in the pseudorapidity intervals covered by the V0 detector, 2.8 < 𝜂 < 5.1 and −3.7 < 𝜂 < −1.7. According to QCD-inspired phenomenological models, it shows sensitivity to multiparton interactions and is less affected by biases toward larger 𝑝 T due to local multiplicity fluctuations in the V0 acceptance than multiplicity. The analysis is performed in minimum-bias (MB) as well as in high-multiplicity events up to 𝑝 T = 20 GeV/⁢𝑐. The event selection requires at least one charged particle produced in the pseudorapidity interval |𝜂| < 1. The measured 𝑝 T distributions, average 𝑝 T , kaon-to-pion and proton-to-pion particle ratios, presented in this paper, are compared to model calculations using pythia 8 based on color strings and EPOS LHC. The modification of the 𝑝 T -spectral shapes in low-flattenicity events that have large event activity with respect to those measured in MB events develops a pronounced peak at intermediate 𝑝 T (2 < 𝑝 T < 8 GeV/𝑐), and approaches the vicinity of unity at higher 𝑝 T . The results are qualitatively described by pythia, and they show different behavior than those measured as a function of charged-particle multiplicity based on the V0M estimator.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Forecasts for Galaxy Formation and Dark Matter Constraints from Dwarf Galaxy Surveys

Abstract The abundance of faint dwarf galaxies is determined by the underlying population of low-mass dark matter (DM) halos and the efficiency of galaxy formation in these systems. Here, we quantify potential galaxy formation and DM constraints from future dwarf satellite galaxy surveys. We generate satellite populations using a suite of Milky Way (MW)–mass cosmological zoom-in simulations and an empirical galaxy–halo connection model, and assess sensitivity to galaxy formation and DM signals when marginalizing over galaxy–halo connection uncertainties. We find that a survey of all satellites around one MW-mass host can constrain a galaxy formation cutoff at peak virial masses of M 50 = 10 8 M ⊙ at the 1 σ level; however, a tail toward low M 50 prevents a 2 σ measurement. In this scenario, combining hosts with differing bright satellite abundances significantly reduces uncertainties on M 50 at the 1 σ level, but the 2 σ tail toward low M 50 persists. We project that observations of one (two) complete satellite populations can constrain warm DM models with m WDM ≈ 10 keV (20 keV). Subhalo mass function (SHMF) suppression can be constrained to ≈70%, 60%, and 50% that in cold dark matter (CDM) at peak virial masses of 10 8 , 10 9 , and 10 10 M ⊙ , respectively; SHMF enhancement constraints are weaker (≈20, 4, and 2 times that in CDM, respectively) due to galaxy–halo connection degeneracies. These results motivate searches for faint dwarf galaxies beyond the MW and indicate that ongoing missions like Euclid and upcoming facilities including the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope will probe new galaxy formation and DM physics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

California annual grass phenology and allometry influence ecosystem dynamics and fire regime in a vegetation demography model

Grass-dominated ecosystems cover wide areas of the land surface yet have received far less attention from the Earth System Model (ESM) community. This limits model projections of ecosystem dynamics in response to global change and coupled vegetation–climate dynamics. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model, to determine ecosystem sensitivity to alternate, observed grass allometries and biophysical traits, and evaluated model performance in capturing California C 3 annual grasslands structure and fire regimes. Grass allometry, leaf physiology, plant phenology, and plant mortality all drove the seasonal variation in matter and energy exchange and fire dynamics in California annual grasslands. Allometry influenced grassland structure and function mainly through canopy architecture-mediated space and light competition instead of through carbon partitioning strategy. Regional variation in grassland annual burned area was driven by variation in ecosystem productivity. Our study advances the modeling of grassy ecosystems in ESMs by establishing the importance of grass allometry and plant phenology and mortality in driving C 3 annual grassland seasonal dynamics and fire regime. The calibrated annual grass allometry and biophysical traits presented can be applied in future studies to project climate–vegetation–fire feedbacks in annual grass-dominant ecosystems under global change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Hadron-Argon Cross-Section Measurements at Protodune

The Deep Underground Neutrino Experiment (DUNE) is a next-generation experiment designed to measure neutrino oscillations with unprecedented precision, specifically seeking to identify CP violation in the lepton sector. DUNE utilizes the Liquid Argon Time Projection Chamber (LArTPC) as its core detecting technology, offering high-resolution imaging to reconstruct neutrino interactions on argon. A primary challenge in this reconstruction is the modeling of hadronic final-state interactions (FSI), where hadrons produced at the initial interaction vertex undergo further scattering before escaping the nucleus. In this thesis, I utilize the ProtoDUNE-SP detector and its 1~GeV/$c$ hadron beam at the CERN Neutrino Platform to present the first measurements of total inelastic $\pi^+$-argon and proton-argon cross sections in an energy regime critical to DUNE. I detail the development and validation of the ``slicing method'', which utilizes a kiloton-scale LArTPC to extract hadron-argon cross sections. These measurements provide an indispensable benchmark for informing DUNE's FSI modeling, and our results show no significant tension with current model predictions. Additionally, I study the impact of FSI modeling on DUNE’s sensitivity to oscillation parameters. This study demonstrates that variations in FSI models may be degenerate with oscillation features, highlighting the necessity of using direct experimental data to refine interaction models. Finally, I provide an outlook on related studies that will further contribute to this effort, supporting the ambitious physics goals of upcoming neutrino oscillation programs including DUNE.

Yin-Rui, Liu [U. Chicago (main)]↗

DOE OSTI2024

Numerical challenges in modeling gravothermal collapse in Self-Interacting Dark Matter halos

When dark matter has a large cross section for self scattering, halos can undergo a process known as gravothermal core collapse, where the inner core rapidly increases in density and temperature. To date, several methods have been used to implement Self-Interacting Dark Matter (SIDM) in N-body codes, but there has been no systematic study of these different methods or their accuracy in the core-collapse phase. In this paper, we compare three different numerical implementations of SIDM, including the standard methods from the GIZMO and Arepo codes, by simulating idealized dwarf halos undergoing significant dark matter self interactions (σ/m = 50 cm 2 /g). When simulating these halos, we also vary the mass resolution, time-stepping criteria, and gravitational force-softening scheme. The various SIDM methods lead to distinct differences in a halo's evolution during the core-collapse phase, as each results in spurious scattering rate differences and energy gains/losses. The use of adaptive force softening for gravity can lead to numerical heating that artificially accelerates core collapse, while an insufficiently small simulation time step can cause core evolution to stall or completely reverse. Additionally, particle numbers must be large enough to ensure that the simulated halos are not sensitive to noise in the initial conditions. Even for the highest-resolution simulations tested in this study (10 6 particles per halo), we find that variations of order 10% in collapse time are still present. Here, the results of this work underscore the sensitivity of SIDM modeling on the choice of numerical implementation and motivate a careful study of how these results generalize to halos in a cosmological context.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Sensitivity analysis of a parameterization of coagulation in an aqueous transport model

A three-dimensional aqueous transport model, ALGE, was developed at the Savannah River National Laboratory (SRNL) as a tool for emergency response. Recently, coagulation and break-up of sediment and particulate (contaminant adhered to sediment) matter has been added as a new model capability, representing both processes as functions of salinity and turbulence. A sensitivity analysis was performed by comparing time series of sediment and particulate concentrations at various locations when the model’s parameters (particle size and density) are altered. The results show significant differences between experiments in both freshwater and saltwater systems parameters vary (P-values < 0.05). From this sensitivity study, we have concluded the importance of incorporating both salinity and turbulent effects, as they have a large role in the physical process of coagulation and breakup of sediment. By accounting for these physical processes, we have made the ALGE model a more robust tool for emergency response.

Aqueous fate and transport↗

DOE OSTI2025

Assessing the impact of climate change on rainfall-triggered landslides: a case study in California

Landslides are widespread natural hazards which take a heavy toll on lives, property, and infrastructure each year. In January 2023 and 2024, widespread landslides occurred in California as a result of extreme winter precipitation. Estimating the regional evolution of landslide hazard in a changing climate is essential for adaptation planning and risk mitigation efforts. The complex effects of climate change on landslide hazard, however, are poorly understood. Here, we use climate and landslide observations to develop a novel data-driven approach for landslide susceptibility assessment under historical and future climate conditions. We apply this framework to investigate the evolution of landslide susceptibility in California and its impacts on the roadway system based on downscaled climate projections under moderate and high greenhouse gas emission scenarios. On average, the results indicate an increase in landslide susceptibility through 2100 in regions where historical susceptibility is moderate to very high. Although the spatio-temporal variations in landslide susceptibility are sensitive to climate model uncertainties in predicting extreme rainfall, the direst impacts are largely circumvented in lower emission scenarios.

Climate Change↗

DOE OSTI2025

Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials

Understanding the relationship between the evolution of microstructures of irradiated LiAlO2pellets and tritium diffusion, retention and release could improve predictions of tritium performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.

Oostrom, Marjolein T.↗

DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗