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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Precision Polishing of Spheres Via In-Situ Process Monitoring and Machine-Learning-Based Optimization

In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High density carbon capsules made for ICF experiments on the National Ignition Facility (NIF) are precision polished to achieve the surface smoothness in the order of a few nm as well as to minimize isolated defects in the form of pits. Given the critical role of this process, we are developing smart manufacturing techniques with goal of elevating the efficiency of this process. Our approach is to use MEMS-based sensors to capture the fine vibrational signals generated during the polishing process and combine it with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, we describe the multiple fronts that we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used for ensuring the progress of the run as well as a means for faster optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

MatLib-1.2.2: Nuclear Material Properties Library

The U.S. Nuclear Regulatory Commission (NRC) uses the computer code Fuel Analysis under Steady-state and Transients (FAST) to model steady-state and transient fuel behavior to support regulatory decisions. FAST relies on a material properties library (MatLib) that contains the thermal and mechanical properties of the nuclear materials and coolants of interest to support the U.S. commercial nuclear industry. MatLib contains properties for a variety of nuclear fuels, cladding and other structural materials, gases, and coolants. In this document, material property correlations for the materials contained within MatLib are presented and discussed. When available, comparisons are made between the material property correlations and available data. Additionally, uncertainties are quantified on the material properties, which is then used by the NRC to support uncertainty quantification for best-estimate plus uncertainty safety evaluation reviews. This document describes MatLib-1.2.2, updated from MatLib-1.2.1 to include additional properties for metallic fuel. It is one of a series of documents on FAST; the other documents detail the models used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Filtered Rayleigh-Ritz is all you need

Recent work has shown that the (block) Lanczos algorithm can be used to extract approximate energy spectra and matrix elements from (matrices of) correlation functions in quantum field theory, and identified exact coincidences between Lanczos analysis methods and others. In this work, we note another coincidence: the Lanczos algorithm is equivalent to the well-known Rayleigh-Ritz method applied to Krylov subspaces. Rayleigh-Ritz provides optimal eigenvalue approximations within subspaces; we find that spurious-state filtering allows these optimality guarantees to be retained in the presence of statistical noise. We explore the relation between Lanczos and Prony's method, their block generalizations, generalized pencil of functions (GPOF), and methods based on the generalized eigenvalue problem (GEVP), and find they all fall into a larger "Prony-Ritz equivalence class", identified as all methods which solve a finite-dimensional spectrum exactly given sufficient correlation function (matrix) data. This equivalence allows simpler and more numerically stable implementations of (block) Lanczos analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Chemistry, Formation and Passivation of Solid-Electrolyte Interphase (SEI) from FSI- Breakdown at Li-metal Potential

LiFSI-based liquid electrolytes are promising for realizing high Coulombic efficiency (CE) and long cycle life of next-generation high-energy Li metal battery. Nevertheless, the fundamentals of both the chemistry and formation of solid-electrolyte interphase (SEI) in these electrolytes have remained elusive. Herein, we extensively combine electrochemistry and X-ray photoelectron spectroscopy (XPS) to illustrate the reaction pathways of electrolyte decomposition, especially FSI- breakdown, as well as the dissolution trend of various inorganic SEI components. The SEI-forming reactions at Cu/electrolyte interface manifest a potential (E) dependence, driven by direct electron-transfer (ET) and Li underpotential-deposition (Li UPD) at high and low E regions, respectively. By closely correlating the XPS data obtained with and without solvent washing, we demonstrate that not all the products derived from SEI reactions are incorporated into forming a passivating surface layer, with a notable portion dissolving into liquid electrolyte instead. Importantly, high-CE electrolyte dictated by the chemical identity of solvent, is found to exhibit minimized interfacial reactivity of SEI formation enabling more effective interphasial passivation, via preferential integration of passivating ingredients such as LiF. Overall, this work formulates a systematic understating of bridging the SEI chemistry, formation and passivation while identifying key characteristics of high-performance electrolyte for guiding future design.

Bao, Zhenan [SLAC National Accelerator Laboratory ↗
DOE OSTI2024

Searching for parity violation in SDSS DR16 Lyman-α forest data

The four-point correlation function is the lowest order correlation function for scalar fields that can be used to probe statistical parity invariance in an isotropic universe. There are intriguing claims of detection of parity violation in the 4-point function of BOSS galaxy clustering data. We apply the same estimator to the public SDSS Data Release 16 Lyman-α forest data. Lyman-α forest data probes a different redshift range and is sensitive to a different density regime using a completely different technique. A detection would therefore be a strong indication of new physics. We identify an accurate covariance matrix as a crucial impediment to performing this measurement accurately, consistent with existing literature on galaxy 4-point function. Here, we discuss several approaches to estimating the covariance matrix, several of which produce spurious detection. Using a robust, but very suboptimal, covariance matrix derived from subsample bootstrapping, we find no evidence for parity violation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

Chemical Physics (physics.chem-ph)↗
DOE OSTI2025

Atacama Cosmology Telescope DR6 and DESI: Structure growth measurements from the cross-correlation of DESI legacy imaging galaxies and CMB lensing from ACT DR6 and 𝑃⁢𝑙⁢𝑎⁢𝑛⁢𝑐⁢𝑘 PR4

We measure the growth of cosmic density fluctuations on large scales and across the redshift range 0.3 < 𝑧 < 0.8 through galaxy clustering and the cross-correlation of the ACT data release 6 cosmic microwave background (CMB) lensing map and galaxies from the Dark Energy Spectroscopic Instrument Legacy Survey, using three galaxy samples spanning the redshifts of 0.3 ≲ 𝑧 ≲ 0.45, 0.45 ≲ 𝑧 ≲ 0.6, 0.6 ≲ 𝑧 ≲ 0.8. We adopt a scale cut where nonlinear effects are negligible, so that the cosmological constraints are derived from the linear regime. We determine the amplitude of matter fluctuations over all three redshift bins using Atacama Cosmology Telescope (ACT) data alone to be 𝑆 8 ≡ 𝜎 8 ⁢(Ω 𝑚 /0.3) 0.5 =0.772 ± 0.040 in a joint analysis combining the three redshift bins and ACT lensing alone. Using a combination of ACT and Planck data we obtain 𝑆 8 = 0.765 ± 0.032. The lowest redshift bin used is the least constraining and exhibits a ∼2⁢𝜎 tension with the other redshift bins; thus we also report constraints excluding the first redshift bin, giving 𝑆 8 = 0.785 ± 0.033 for the combination of ACT and Planck. This result is in excellent agreement at the 0.3⁢𝜎 level with measurements from galaxy lensing, but is 1.8⁢𝜎 lower than predictions based on Planck primary CMB data. Understanding whether this hint of discrepancy in the growth of structure at low redshifts arises from a fluctuation, from systematics in data, or from new physics is a high priority for forthcoming CMB lensing and galaxy cross-correlation analyses.

cosmic microwave background↗
DOE OSTI2025

Atacama Cosmology Telescope DR6 and DESI: Structure growth measurements from the cross-correlation of DESI legacy imaging galaxies and CMB lensing from ACT DR6 and P l a n c k PR4

We measure the growth of cosmic density fluctuations on large scales and across the redshift range 0.3 < z < 0.8 through galaxy clustering and the cross-correlation of the ACT data release 6 cosmic microwave background (CMB) lensing map and galaxies from the Dark Energy Spectroscopic Instrument Legacy Survey, using three galaxy samples spanning the redshifts of 0.3 ≲ z ≲ 0.45 , 0.45 ≲ z ≲ 0.6 , 0.6 ≲ z ≲ 0.8 . We adopt a scale cut where nonlinear effects are negligible, so that the cosmological constraints are derived from the linear regime. We determine the amplitude of matter fluctuations over all three redshift bins using Atacama Cosmology Telescope (ACT) data alone to be S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.772 ± 0.040 in a joint analysis combining the three redshift bins and ACT lensing alone. Using a combination of ACT and Planck data we obtain S 8 = 0.765 ± 0.032 . The lowest redshift bin used is the least constraining and exhibits a ∼ 2 σ tension with the other redshift bins; thus we also report constraints excluding the first redshift bin, giving S 8 = 0.785 ± 0.033 for the combination of ACT and Planck. This result is in excellent agreement at the 0.3 σ level with measurements from galaxy lensing, but is 1.8 σ lower than predictions based on Planck primary CMB data. Understanding whether this hint of discrepancy in the growth of structure at low redshifts arises from a fluctuation, from systematics in data, or from new physics is a high priority for forthcoming CMB lensing and galaxy cross-correlation analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A road map to cosmological parameter analysis with third-order shear statistics: III. Efficient estimation of third-order shear correlation functions and an application to the KiDS-1000 data

Context. Third-order lensing statistics contain a wealth of cosmological information that is not captured by second-order statistics. However, the computational effort it takes to estimate such statistics in forthcoming stage IV surveys is prohibitively expensive. Aims. We derive and validate an efficient estimation procedure for the three-point correlation function (3PCF) of polar fields such as weak lensing shear. We then use our approach to measure the shear 3PCF and the third-order aperture mass statistics on the KiDS-1000 survey. Methods We constructed an efficient estimator for third-order shear statistics that builds on the multipole decomposition of the 3PCF. We then validated our estimator on mock ellipticity catalogs obtained from N -body simulations. Finally, we applied our estimator to the KiDS-1000 data and presented a measurement of the third-order aperture statistics in a tomographic setup. Results. Our estimator provides a speedup of a factor of ∼100–1000 compared to the state-of-the-art estimation procedures. It is also able to provide accurate measurements for squeezed and folded triangle configurations without additional computational effort. We report a significant detection of tomographic third-order aperture mass statistics in the KiDS-1000 data (S/N = 6.69). Conclusions. Our estimator will make it computationally feasible to measure third-order shear statistics in forthcoming stage IV surveys. Furthermore, it can be used to construct empirical covariance matrices for such statistics.

Astronomy & Astrophysics↗
DOE OSTI2024

Rare Earth Elements (REE) and Critical Minerals (CM) in Middle Pennsylvanian-Age Coals and Associated Sediments in the Central Appalachian Basin, Eastern U.S.A.

The Central Appalachian Basin (CAB) of Kentucky, Tennessee, West Virginia and Virginia has a long history of coal mining and oil and gas extraction that has empowered the regional and national economies, the development of infrastructure, and a highly trained energy resources work force. As our societal demands for advanced technologies have rapidly increased in recent years, coal-related materials are viewed as an important new unconventional domestic source of critical minerals (CM) that are required for telecommunications, aerospace and transportation industries, electronics, the transition to low-carbon emissions energy production, and many consumer products. Coal-related materials encompass coal, associated sediments, coal mining waste materials, produced waters, and ash residues from coal-fired power plants. An important objective of the Evolve Central Appalachia Project (Evolve CAPP), sponsored by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL) is to assess the quantity and distribution of CM resources in the CAB region. The rare earth elements (REE) are considered highest priority, although other important CM such as niobium, gallium, and zirconium are known to occur in the Middle Pennsylvanian-age coals and sediments. Working with coal industry partners who provided access to drill cores, coal-related sediments and waste materials, over 600 samples have been collected for laboratory analysis, and over 730 materials have been scanned using portable x-ray fluorescence (pXRF) equipment. The application of pXRF provides the means for real-time semiquantitative analysis of CM content at very close spacings (typically 2-3 inch intervals) along drill core and in-situ channel samples that span the roof, coal seam, and floor rock. The comparison of pXRF geochemical data with laboratory results, geologic data, and downhole spectral gamma logs can provide high resolution input to lithologic and depositional models for future CM resource evaluations. The preliminary findings show that pXRF is capable of accurately measuring low concentrations of many of the CM with a high level of confidence (Ba, Cr, Ga, K, Nb, Rb, Sr, Th, Y), whereas for others (La, Ce, Co, Mn, Nd, Ni, Sc, Ti, V) the detection limits are very high or spectral interferences increase the uncertainty. Notably, the mean abundances of Y (34 ppm), La (86 ppm), Ga (40 ppm), and V (146 ppm) in coal underclays in the CAB region are up to 7X enriched compared with the overlying coal. These values also exceed the reported concentrations in published reference materials for upper continental crustal rocks (Rudnick and Gao, 2003), North American Shale Composite (Gromet et al., 1984), and North American coal (Finkelman, 1993). The pXRF data are in part verified by laboratory results that indicate the mean Y abundance (36 ppm) is highly correlated (R2 = 0.807) with total REE (ΣREE). The correlation is even higher (R2 = 0.957) with heavy REE (ΣHREE). Applying these correlations to the pXRF data for the coal underclays, the mean estimated values for ΣREE+Y and ΣHREE+Y are 270 ppm and 58 ppm, respectively. Although these average values are not considered high, the range of Y measured by pXRF in the coal underclays extended as high as 114 ppm, which would suggest ΣREE+Y equal to 847 ppm. The mean abundances of Zr (192 ppm) and Th (21 ppm) in coal underclays are also enriched compared with the overlying coal and these results likely reflect the presence of resistant detrital heavy minerals such as monazite, xenotime, and zircon in the underclay matrix. Several of the profiled coal seams and associated wall rocks contained thin volcanic ash layers up to 4-5 inches in thickness. The extent to which these ash fall layers provided a source for CM under the paleoenvironmental conditions that resulted in coal deposits in the CAB remains to be fully studied. Continuing investigations in the Evolve CAPP study area will include laboratory determinations of mineralogic and clay compositions, and evaluations of CM geochemical mobility in the coal and coal underclays.

Lassetter, Billy↗
DOE OSTI2025

Recrystallization driven softening and heating rate dependencies of FeCrAl nuclear fuel cladding during accident transients

A refined understanding of FeCrAl cladding behavior during rapid transients is critical for its potential deployment in light-water reactors. Current assessments focus on transient burst testing metrics such as balloon geometry, burst temperature, and hoop stress, often used as proxies for simpler conventional tensile properties. However, directly correlating isothermal tensile and creep data with accident transient scenarios remains a challenge, although it is essential for high fidelity model development. Recent modeling based on tensile tests up to 800 °C, conducted with both immediate loading and a 10-minute soak, showed that immediate loading better predicts experimental burst temperatures, indicating a thermal softening effect. Building upon this observation, the current study connects transient performance, microstructural evolution, and high-temperature tensile properties by leveraging results from C26M claddings burst tests performed at heating rates of 1–50 °C/s and hoop stresses from 25 to 100 MPa. At 25 MPa, rupture temperatures varied by only 6 °C, but at 100 MPa, the difference reached 116 °C, with faster heating yielding higher burst temperatures. Microstructural analysis identified recrystallization as the primary cause of heating rate-dependent softening, eliminating prior cold-working. In-situ thermomechanical data linked ballooning onset to localized instabilities, similar to ultimate tensile strength behavior in conventional tensile tests. High heating rates correlated with immediate loading tensile data, while lower rates matched soaked data. Furthermore, by linking burst performance to microstructural evolution and tensile properties, this work provides a foundation for more accurate modeling of FeCrAl claddings and potentially other Fe-based materials under accident conditions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
DOE OSTI2026

Universality of Shallow Global Quenches in Critical Spin Chains

Measuring universal data in the strongly correlated regime of quantum critical points remains a fundamental objective for quantum simulators. In foundational work, Calabrese and Cardy demonstrated how these data govern the dynamics of certain global quenches to 1+1-dimensional conformal field theories. While the quasiparticle picture they introduce has been widely successful in both theory and experiment, their seminal prediction that the critical exponents are simply encoded in the relaxation rates of local observables is challenging to investigate experimentally. In this Letter, we examine the critical quench dynamics of local observables from two types of readily accessible initial conditions: ground states and finite-temperature ensembles. Here, we identify universal scaling collapses and scaling functions, utilizing a combination of conformal perturbation theory and tensor network numerics. For the finite-temperature quenches, we determine a regime in which the conformal field theory results are recovered, thereby allowing universal quantum critical data to be extracted from realistic quenches.

Quantum many-body systems↗
DOE OSTI2025

Automated qualification data tool for high temperature metallic materials

This report describes a framework for storing, processing, and displaying qualification data for high temperature mechanical properties. The framework automates the process of generating design data from mechanical test results, for example for a data qualification report for the ASME Boiler \& Pressure Vessel Code. The framework has three parts: a data storage model with common formats for several types of typical mechanical property tests, a backend based on the \pycreep Python library for correlating and extrapolating the data to generate design material properties and allowable stresses, and a demonstration user interface for displaying, sorting, and filtering the data and exploring different options for modeling the design mechanical properties. The report discusses the options available for data processing, with illustrations from real test data on Alloy 617, Alloy 709, Alloy 740H, and Laser-Powder Bed Fusion 316H. The framework is complete for ASME type data analysis and will be used to store test data generated by the Department of Energy, Office of Nuclear Energy, Advanced Materials and Manufacturing Technologies sponsored qualification programs. Future work could extend the tool to other types of material properties and/or expand the demo user interface to make it accessible across the AMMT program.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Optical and Infrared Are Connected

Galaxies are often modeled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model that exploits subtle correlations between physical processes to accurately predict infrared (IR) Wide-field Infrared Survey Explorer (WISE) photometry from a neural summary of optical Sloan Digital Sky Survey spectra. The model achieves accuracies of $χ^{2}_{N} ≈ 1$ for all photometric bands in WISE, as well as good colors. We are able to tightly constrain typically IR-derived properties, e.g., the bolometric luminosities of active galactic nuclei (AGN) and dust parameters such as q PAH . We also test whether current spectral energy distribution (SED) fitting methods reproduce such panchromatic relations, but find their predictions biased and overconfident, likely due to model misspecification, with correlated biases in star-formation rates (SFRs) and AGN luminosities being most evident. To help improve SED models, we determine which features of the optical spectrum are responsible for our improved predictions, and identify several lines (Ca II , Sr II , Fe I , [O II ], and Hα), which point to the complex chronology of star formation and chemical enrichment being incorrectly modeled.

Jespersen, Christian Kragh [Princeton Univ., NJ (U↗
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2025

Modelling the impact of quasar redshift errors on the full-shape analysis of correlations in the Lyman-α forest.

In preparation for the first cosmological measurements from the full shape of the Lyman-α (Lyα) forest from DESI, we must carefully model all relevant systematics that might bias our analysis. It was shown in Youles et al. (2022) that random quasar redshift errors produce a smoothing effect on the mean quasar continuum in the Lyα forest region. This, in turn, gives rise to spurious features in the Lyα autocorrelation and its cross-correlation with quasars. Using synthetic data sets based on the DESI survey, we confirm that the impact on BAO measurements is small, but that a bias is introduced to parameters which depend on the full shape of our correlations. We combine a model of this contamination in the cross-correlation (Youles et al. 2022) with a new model we introduce here for the auto-correlation. These are parametrised by 3 parameters, which, when included in a joint fit to both correlation functions, successfully eliminate any impact of redshift errors on our full-shape constraints. We also present a strategy for removing this contamination from real data, by removing ∼0.3% of correlating pairs.

cosmology↗
DOE OSTI2025

Polarized and unpolarized gluon PDFs: Generative machine learning applications for lattice QCD matrix elements at short distance and large momentum

Lattice quantum chromodynamics (QCD) calculations share a defining challenge by requiring a small finite range of spatial separation z between quark/gluon bilinears for controllable power corrections in the perturbative QCD factorization, and a large hadron boost p z for a successful determination of collinear parton distribution functions (PDFs). However, these two requirements make the determination of PDFs from lattice data very challenging. We present the application of generative machine learning algorithms to estimate the polarized and unpolarized gluon correlation functions utilizing short-distance data and extending the correlation up to z p z ≲ 14 , surpassing the current capabilities of lattice QCD calculations. We train physics-informed machine learning algorithms to learn from the short-distance correlation at z ≲ 0.36 fm and take the limit, p z → ∞ , thereby minimizing possible contamination from the higher-twist effects for a successful reconstruction of the polarized gluon PDF. We also expose the bias and problems with underestimating uncertainties associated with the use of model-dependent and overly constrained functional forms, such as x α ( 1 − x ) β and its variants to extract PDFs from the lattice data. We propose the use of generative machine learning algorithms to mitigate these issues and present our determination of the polarized and unpolarized gluon PDFs in the nucleon. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗