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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systematizing the effective theory of self-interacting dark matter

If dark matter has strong self-interactions, future astrophysical and cosmological observations, together with a clearer understanding of baryonic feedback effects, might be used to extract the velocity dependence of the dark matter scattering rate. To interpret such data, we should understand what predictions for this quantity are made by various models of the underlying particle nature of dark matter. In this paper, we systematically compute this function for fermionic dark matter with light bosonic mediators of vector, scalar, axial vector, and pseudoscalar type. We do this by matching to the nonrelativistic effective theory of self-interacting dark matter and then computing the spin-averaged viscosity cross section nonperturbatively by solving the Schrödinger equation, thus accounting for any possible Sommerfeld enhancement of the low-velocity cross section. In the pseudoscalar case, this requires a coupled-channel analysis of different angular momentum modes. We find, contrary to some earlier analyses, that nonrelativistic effects only provide a significant enhancement for the cases of light scalar and vector mediators. Scattering from light pseudoscalar and axial vector mediators is well described by tree-level quantum field theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

The Effect of Grain Boundary Facet Junctions on Segregation and Embrittlement

Junctions are discontinuities in flat grain boundaries that arise in all polycrystalline materials and are thought to play important roles in the response of a grain boundary network to thermal and mechanical loads. A key open question concerns the mechanisms by which solute segregation to junctions impacts properties of the grain boundary. Here, in this work, we investigate the influence of grain boundary facet junctions on solute embrittlement, and we present an analytical model that uses the hydrostatic stress field contributed by dislocations at multiple junctions to describe these effects. Specifically, we study junctions between {112} facets of various lengths in Au $\langle111\rangle$ Σ3 tilt grain boundaries. Copper and silver solutes are employed to determine if the effect of junctions on solute segregation and embrittlement is dependent on size relative to the host. Combined, atomistic simulation data and the analytical model show that Cu and Ag have opposite segregation responses to junctions due to the sign of the hydrostatic stress field induced by junctions. However, a positive shift in the embrittling potency is computed near junctions regardless of solute type or the stress state of the segregation site. Hence, for the conditions studied, junctions consistently shift the energetic landscape towards embrittlement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Benchmarking Monte Carlo codes for the modelling of low-energy neutron production target reactions

The increasing adoption of accelerator-based neutron sources (ABNS) for applications including neutron capture therapy (NCT) research has highlighted the need for accurate simulation tools. Precise modelling of the neutron production target is crucial to ensure that simulated predictions of neutron beam characteristics used for subsequent beam shaping assembly design are reliable. This work presents a comprehensive benchmarking of four widely-used Monte Carlo codes - Geant4, PHITS, FLUKA (CERN), and MCNP - for modelling low-energy neutron production target reactions. Using their recommended physics models and cross-section libraries, we evaluate each code’s performance in simulating four beam-target reactions: 7 Li(p,n) 7 Be, 9 Be(p,n) 9 B, 9 Be(d,n) 10 B, and C(d,n)N. Predictions of neutron yield, angular distributions, and energy spectra are compared against available thick target experimental data. Results show varying levels of agreement between the codes depending on the reaction type, energy range, and beam characteristics. Geant4, MCNP and PHITS are the overall best performing codes for the simulation of total neutron yield and yield in the forward direction across most reactions. Across energies where experimental benchmarks exist, inter-code discrepancies in total and forward-directed yield are typically 10 to 30%, with larger deviations at near-threshold incident ion energies. PHITS provides the best overall reproduction of experimental spectra, particularly for the 9 Be(p,n) 9 B reaction. Additionally, PHITS demonstrates superior computational performance for most reactions. These findings provide valuable guidance for ABNS design, highlighting the strengths and limitations of each code for the simulation of low-energy neutron production reactions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Geomechanical properties of the Meletta sandstone - the high-temperature heat storage reservoir rock of DeepStor

The DeepStor project aims at storing excess heat at temperatures up to 140 °C in the depleted Leopoldshafen oil field at a depth of about 1300 m. In order to gain knowledge on the target horizons, the different layers of the Meletta sandstone, samples cored in a block retrieved in a quarry near Nussloch where the Meletta sandstone outcrops were studied. Several petrophysical properties were investigated including mineralogy, porosity, permeability, thermal conductivity, P- and S-wave velocities and quality factor. A mechanical study focused on compressive strength under uniaxial and triaxial stress conditions, tensile strength, critical pressure and stress-dependence of physical properties. Our results show that the Meletta sandstone is heterogeneous, anisotropic, mechanically weak, stress-sensitive and prone to water weakening. Petrophysical measurements on few Meletta sandstone cores retrieved in boreholes at about 1250 m depth showed that the outcrop samples are significantly less cohesive and weaker. Therefore, one should consider the Nussloch outcrop as a good reservoir analog only with great caution. Several models were applied to account for our experimental results on anisotropy, mechanical behavior and stress-dependence of the outcrop samples. Our study provides a valuable data set which can feed numerical models to simulate the behavior of the host formation during heat exchange operations in the DeepStor project.

Geomechanics↗
DOE OSTI2023

Machine-to-machine variability of roughness and corrosion in additively manufactured 316L stainless steel

Numerous studies on the corrosion response of metal AM have been conducted. Nonetheless, the specimens being tested are commonly ground or polished to remove the outer as-built surface. If metal AM is truly going to be employed as a transformative technology that can produce complex shapes that do not require traditional machining, then the material needs to be evaluated in the as-built state. The reality is that AM alloys have shown significant inconsistencies regarding as-built surface texture, topology, and residual stress. One metric that has shown significant unpredictability in the literature is the susceptibility to localized corrosion of typically passive alloys, such as stainless steel (SS). There are a large number of studies that have attempted to understand the corrosion response of metal AM materials, but these studies are typically performed on materials that have been printed on a single machine and often mechanically polished to a smooth finish. This study compares the corrosion response of as-built AM, laser-beam powder bed fusion (LB-PBF), 316L SS parts that have been fabricated on five different machines. The majority of this work focused on understanding the susceptibility to localized corrosion of AM metals with respect to machine-dependent variables, namely surface roughness, and build angle. Surface roughness data was collected using scanning white light triangulation, laser scanning confocal microscopy, and coherence scanning interferometry. The results show that there is significant variability (p two-tail < 0.05) in the susceptibility to local corrosion initiation of LB-PBF 316L SS samples built on different machines. Surface oxides were probed with electron dispersive spectroscopy and revealed that variations in local corrosion susceptibility likely arise from differences in the stability of the passive film caused by chemical segregation, unique microstructure, and tortuous roughness features at the as-built surfaces. The variability of roughness and corrosion properties from test samples printed on different machines was corroborated by property measurements performed at five different testing sites, proving reproducibility of the data. Most importantly, this study shows that if the as-built surface layer is removed through grinding or electropolishing the machine-to-machine variation observed in the corrosion susceptibility is reduced.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Methanol oxidation up to 100 atm in a supercritical pressure jet-stirred reactor

Methanol (CH 3 OH) has attracted considerable attention as a renewable fuel or fuel additive with low greenhouse gas emissions. Methanol oxidation was studied using a recently developed supercritical pressure jet-stirred reactor (SP-JSR) at pressures of 10 and 100 atm, at temperatures from 550 to 950 K, and at equivalence ratios of 0.1, 1.0, and 9.0 in experiments and simulations. Here, the experimental results show that the onset temperature of CH 3 OH oxidation at 100 atm is around 700 K, which is more than 100 K lower than the onset at 10 atm and this trend cannot be predicted by the existing kinetics models. Furthermore, a negative temperature coefficient (NTC) behavior was clearly observed at 100 atm at fuel rich conditions for methanol for the first time. To understand the observed temperature shift in the reactivity and the NTC effect, we updated some key elementary reaction rates of relevance to high pressure CH 3 OH oxidation from the literature and added some new low-temperature reaction pathways such as CH 2 O + HO 2 = HOCH 2 O 2 (RO 2 ), RO 2 + RO 2 = HOCH 2 O (RO) + HOCH 2 O (RO) + O 2 , and CH 3 OH + RO 2 = CH 2 OH + HOCH 2 O 2 H (ROOH). Although the model with these updates improves the prediction somewhat for the experimental data at 100 atm and reproduces well high-temperature ignition delay times and laminar flame speed data in the literature, discrepancies still exist for some aspects of the 100 atm low-temperature oxidation data. In addition, it was found that the pressure-dependent HO 2 chemistry shifts to lower temperature as the pressure increases such that the NTC effect at fuel-lean conditions is suppressed. Therefore, as shown in the experiments, the NTC phenomenon was only observed at the fuel-rich condition where fuel radicals are abundant and the HO 2 chemistry at high pressure is weakened by the lack of oxygen resulting in comparatively little HO 2 formation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

13 C-metabolic flux analysis of Clostridium ljungdahlii illuminates its core metabolism under mixotrophic culture conditions

Carbon dioxide-fixing acetogenic bacteria (acetogens) utilizing the Wood-Ljungdahl Pathway (WLP) play an important role in CO 2 fixation in the biosphere and in the development of biological processes – alone or in cocultures, under both autotrophic and mixotrophic conditions – for production of chemicals and fuels. To date, limited work has been reported in experimentally validating and quantifying reaction fluxes of their core metabolic pathways. Here, the core metabolic model of the acetogen Clostridium ljungdahlii was interrogated using 13 C-metabolic flux analysis ( 13 C-MFA), which required the development of a new defined culture medium. Autotrophic, heterotrophic, and mixotrophic growth in defined medium was possible by adding 1 mM methionine to replace yeast extract. Our 13 C-MFA found an incomplete TCA cycle and inactive core pathways/reactions, notably those of the oxidative pentose phosphate pathway, Entner-Doudoroff pathway, and malate dehydrogenase. 13 C-MFA during mixotrophic growth using the parallel tracers [1– 13 C]fructose, [1,2– 13 C]fructose, [1,2,3– 13 C]fructose, and [U– 13 C]asparagine found that externally supplied CO 2 contributed the majority of carbon consumed. All internally-produced CO 2 from the catabolism of asparagine and fructose was consumed by the WLP. While glycolysis of fructose was active, it was not a major contributor to overall production of ATP, NADH, and acetyl-CoA. Gluconeogenic reactions were active despite the availability of organic carbon. Asparagine was catabolized equally via conversion to threonine and subsequent cleavage to produce acetaldehyde and glycine, and via deamination to fumarate and then the anaplerotic conversion of malate to pyruvate. Both pathways for asparagine catabolism produced acetyl-CoA, either directly via pyruvate or indirectly via the WLP. Cofactor stoichiometry based on our data predicted an essentially zero flux through the ferredoxin-dependent transhydrogenase (Nfn) reaction. Instead, nearly all of NADPH generated from the hydrogenase reaction was consumed by the WLP. Reduced ferredoxin produced by the hydrogenase reaction and glycolysis was mostly used for ATP generation via the RNF/ATPase system, with the remainder consumed by the WLP. NADH produced by RNF/ATPase was entirely consumed via the WLP.

13C metabolic flux analysis↗
DOE OSTI2024

Disentangling top-down drivers of mortality underlying diel population dynamics of Prochlorococcus in the North Pacific Subtropical Gyre

Photosynthesis fuels primary production at the base of marine food webs. Yet, in many surface ocean ecosystems, diel-driven primary production is tightly coupled to daily loss. This tight coupling raises the question: which top-down drivers predominate in maintaining persistently stable picocyanobacterial populations over longer time scales? Motivated by high-frequency surface water measurements taken in the North Pacific Subtropical Gyre (NPSG), we developed multitrophic models to investigate bottom-up and top-down mechanisms underlying the balanced control of Prochlorococcus populations. Here, we find that incorporating photosynthetic growth with viral- and predator-induced mortality is sufficient to recapitulate daily oscillations of Prochlorococcus abundances with baseline community abundances. In doing so, we infer that grazers in this environment function as the predominant top-down factor despite high standing viral particle densities. The model-data fits also reveal the ecological relevance of light-dependent viral traits and non-canonical factors to cellular loss. Finally, we leverage sensitivity analyses to demonstrate how variation in life history traits across distinct oceanic contexts, including variation in viral adsorption and grazer clearance rates, can transform the quantitative and even qualitative importance of top-down controls in shaping Prochlorococcus population dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure, electrical and thermal properties of single-crystal BaCuGdTe 3

Single crystals of the quaternary chalcogenide BaCuGdTe 3 were obtained by direct reaction of elements allowing for a complete investigation of the intrinsic electrical and thermal properties of this previously uninvestigated material. The structure was investigated by high-resolution single-crystal synchrotron X-ray diffraction, revealing an orthorhombic crystal structure with the space group Cmcm. Although recently identified as a semiconductor suitable for thermoelectric applications from theoretical analyses, our electrical resistivity and Seebeck coefficient measurements show metallic conduction, the latter revealing strong phonon-drag. Temperature dependent hole mobility reveals dominant acoustic phonon scattering. Heat capacity data reveal a Debye temperature of 183 K and a very high density of states at the Fermi level, the latter confirming the metallic nature of this composition. Thermal conductivity is relatively high with Umklapp processes dominating thermal transport above the Debye temperature. The findings in this work lay the foundation for a more detailed understanding of the physical properties of this and similar multinary chalcogenide materials, and is part of the continuing effort in investigating quaternary chalcogenide materials and their suitability for use in technological applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep level transient spectroscopy investigation of ultra-wide bandgap ($\bar201$) and ($001$) β -Ga 2 O 3

Here, this work reports on a comprehensive examination of the electrical and thermal properties of vertical Schottky diodes fabricated on ($\bar201$) and ($001$)-oriented samples of β-Ga 2 O 3 . The temperature-dependent current–voltage (I–V) and capacitance–voltage (C–V) data were gathered and analyzed down to 60 K. Deep level transient spectroscopy (DLTS) was used to study bulk and interface defects in the two materials from approx. 325 K down to 60 K. In the bulk ($\bar201$) material, an electron trap was observed at E C -0.46 eV, with a capture cross section of 1.6 x 10 -14 cm 2 and a lambda-corrected maximum trap density of 9.08 x 10 15 cm -3 . These results and others indicate that the electron trap is a strong candidate for the well-known E1 defect in β-Ga 2 O 3 based on recent investigations. Additionally, in the ($\bar201$) material, the smooth modulation typical of interface states is evident at temperatures below 275 K. The ($001$) samples manifested what is likely the E2* electron trap at E C -0.68 eV, with a capture cross section of 1.64 x 10 -15 cm 2 and a lambda-corrected maximum trap density of 8.85 x 10 15 cm -3 . The presence of the E2* defect, in particular, is a contrast to the findings of recent DLTS investigations on β-Ga 2 O 3 , which report that E2* emerged only after low-energy proton irradiation. These results help to further map out the defect signatures found in β-Ga 2 O 3 materials, which are of vital importance in the design and fabrication of future β-Ga 2 O 3 devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Review and meta-analysis of electron temperatures from high-intensity laser–solid interactions

The accelerated electron spectrum from high-intensity laser–solid interaction is often conveniently described using a Boltzmann distribution, whose temperature is known within the field as the hot-electron temperature. The importance of the electron temperature is highlighted by the sheer number of experimental and simulation studies on the subject over the past three decades. Recently, multi-kJ, multi-ps pulses have yielded electron spectra with temperatures far beyond the expected ponderomotive result. Expressions that predict the electron temperature considering laser parameters beyond intensity and wavelength have been developed, albeit using small datasets. In this review, we present what is, to the best of our knowledge, the largest dataset of electron temperatures gathered from experimental measurements and particle-in-cell simulations. This dataset allows us to compare existing analytical and empirical hot-electron temperature scaling models over a wide parameter range. We also develop new scaling models that incorporate the laser pulse duration of the laser and the plasma scale length. Three models that include pulse-duration and scale length dependence are especially successful at predicting both simulated and experimental data. The dataset will soon be made publicly available to encourage further investigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Alternative pathways utilize or circumvent putrescine for biosynthesis of putrescine-containing rhizoferrin

The siderophore rhizoferrin (N 1 ,N 4 -dicitrylputrescine) is produced in fungi and bacteria to scavenge iron. Putrescine-producing bacterium Ralstonia pickettii synthesizes rhizoferrin and encodes a single nonribosomal peptide synthetase-independent siderophore (NIS) synthetase. From biosynthetic logic, we hypothesized that this single enzyme is sufficient for rhizoferrin biosynthesis. We confirmed this by expression of R. pickettii NIS synthetase in Escherichia coli, resulting in rhizoferrin production. This was further confirmed in vitro using the recombinant NIS synthetase, synthesizing rhizoferrin from putrescine and citrate. Heterologous expression of homologous lbtA from Legionella pneumophila, required for rhizoferrin biosynthesis in that species, produced siderophore activity in E. coli. Rhizoferrin is also synthesized by Francisella tularensis and Francisella novicida, but unlike R. pickettii or L. pneumophila, Francisella species lack putrescine biosynthetic pathways because of genomic decay. Francisella encodes a NIS synthetase FslA/FigA and an ornithine decarboxylase homolog FslC/FigC, required for rhizoferrin biosynthesis. Ornithine decarboxylase produces putrescine from ornithine, but we show here in vitro that FigA synthesizes N-citrylornithine, and FigC is an N-citrylornithine decarboxylase that together synthesize rhizoferrin without using putrescine. We co-expressed F. novicida figA and figC in E. coli and produced rhizoferrin. A 2.1 Å X-ray crystal structure of the FigC N-citrylornithine decarboxylase reveals how the larger substrate is accommodated and how active site residues have changed to recognize N-citrylornithine. FigC belongs to a new subfamily of alanine racemase-fold PLP-dependent decarboxylases that are not involved in polyamine biosynthesis. These data reveal a natural product biosynthetic workaround that evolved to bypass a missing precursor and re-establish it in the final structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Quantum critical point followed by Kondo-like behavior due to Cu substitution in the itinerant antiferromagnet La 2 ⁢(Cu 𝑥 ⁢Ni 1−𝑥 ) 7

La 2 ⁢Ni 7 is an itinerant magnetic system with a small ordered moment of ∼ 0.1⁢µ 𝐵 /Ni and a series of antiferromagnetic (AFM) transitions at 𝑇 1 = 61.0K, 𝑇 2 = 56.5K, and 𝑇 3 = 42.2K. 𝑀 ⁡(𝐻), and 𝜌 ⁡(𝐻) isotherms as well as constant field 𝑀⁡ (𝑇) and 𝜌 ⁡(𝑇) measurements on single-crystalline samples manifest a complex, anisotropic 𝐻−𝑇 phase diagram with multiple phase lines. Here, in this study, we present the growth and characterization of single crystals of the La 2 ⁢(Cu 𝑥 ⁢Ni 1−𝑥 ) 7 series for 0 ≤ 𝑥 ≤ 0.181. We measured powder x-ray diffraction and composition, as well as anisotropic temperature- and field-dependent resistivity, temperature- and field-dependent magnetization, and temperature-dependent heat capacity on these single crystals. Using the measured data we infer a transition temperature-composition (𝑇−𝑥) phase diagram for this system to study the evolution of the AFM ordering upon Cu substitution. For 0 ≤ 𝑥 ≤ 0.097, the system remains magnetically ordered at base temperature with 𝑥 ≤ 0.012, showing signs of multiple AFM ordering temperatures. For the higher substitution levels 0.125 ≤ 𝑥 ≤ 0.181, there are no signatures of magnetic ordering, but anomalous features in resistance and heat capacity data are observed which are consistent with the Kondo effect in this system. The intermediate 𝑥 = 0.105 sample lies between the magnetic ordered and the Kondo regime and is in the vicinity of the AFM quantum critical point (QCP). Thus, La 2⁢ (Cu 𝑥 ⁢Ni 1−𝑥 ) 7 is an example of a small moment system that can be tuned through a QCP. Given these data combined with the fact that the La 2⁢ Ni 7 structure has kagomelike, Ni sublattices running perpendicular to the crystallographic 𝑐 axis, and a predicted 3⁢𝑑-electron flat band that contributes to the density of states near the Fermi energy, La 2 ⁢(Cu 𝑥 ⁢Ni 1−𝑥 ) 7 becomes a promising system to host and study exotic physics.

Das, Atreyee [Ames Laboratory, and Iowa State Univ↗
DOE OSTI2025

Neural-network extraction of unpolarised transverse-momentum-dependent distributions

We present the first extraction of transverse-momentum-dependent distributions of unpolarized quarks from experimental Drell-Yan data using neural networks to parametrize their nonperturbative part. We show that neural networks outperform traditional parametrizations providing a more accurate description of data. This Letter establishes the feasibility of using neural networks to explore the multidimensional partonic structure of hadrons and paves the way for more accurate determinations based on machine-learning techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗