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DOE OSTI2022

Cluster Expansion and Resurgence in the Polyakov Model

In the Polyakov model, a nonperturbative mass gap is formed at leading-order semiclassics by instanton effects. By using the notions of critical points at infinity, cluster expansion, and Lefschetz thimbles, we show that a third-order effect in semiclassics gives an imaginary ambiguous contribution to the mass gap, which is supposed to be real and unambiguous. This is troublesome for the original analysis, and it is difficult to resolve this issue directly in quantum field theory (QFT). However, we find a new compactification of the Polyakov model to quantum mechanics, by using a background ’t Hooft flux. The compactification has the merit of remembering the monopole instantons of the full QFT within Born-Oppenheimer approximation, while the periodic compactification does not. In the quantum mechanical limit, we prove the resurgent cancellation of the ambiguity in three-instanton sector against ambiguity in the Borel resummation of the perturbation theory around one instanton. Assuming that this result holds in QFT, we provide a large-order asymptotics of perturbation theory around perturbative vacuum and instanton.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

STRUCTURAL PARAMETERS FOR GLOBULAR CLUSTERS IN M31

In this paper, we present surface brightness profiles for 79 globular clusters in M31, using images observed with the Hubble Space Telescope, some of which are from new observations. The structural and dynamical parameters are derived from fitting the profiles to several different models for the first time. The results show that in the majority of cases, King models fit the M31 clusters just as well as Wilson models and better than Sersic models. However, there are 11 clusters best fitted by Sersic models with the Sersic index n > 2, meaning that they have cuspy central density profiles. These clusters may be the well-known core-collapsed candidates. There is a bimodality in the size distribution of M31 clusters at large radii, which is different from their Galactic counterparts. In general, the properties of clusters in M31 and the Milky Way fall in the same regions of parameter spaces. The tight correlations of cluster properties indicate a ''fundamental plane'' for clusters, which reflects some universal physical conditions and processes operating at the epoch of cluster formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A direct measurement of the mean occupation function of quasars: Breaking degeneracies between halo occupation distribution models

Recent work on quasar clustering suggests a degeneracy in the halo occupation distribution constrained from two-point correlation functions. To break this degeneracy, we make the first empirical measurement of the mean occupation function (MOF) of quasars at z ∼ 0.2 by matching quasar positions with groups and clusters identified in the MaxBCG sample. We fit two models to the MOF, a power law and a four-parameter model. The number distribution of quasars in host halos is close to Poisson, and the slopes of the MOF obtained from our best-fit models (for the power-law case) favor an MOF that monotonically increases with halo mass. The best-fit slopes are 0.53 ± 0.04 and 1.03 ± 1.12 for the power-law model and the four-parameter model, respectively. We measure the radial distribution of quasars within dark matter halos and find it to be adequately described by a power law with a slope –2.3 ± 0.4. We measure the conditional luminosity function (CLF) of quasars and show that there is no evidence that quasar luminosity depends on host halo mass, similar to the inferences drawn from clustering measurements. We also measure the conditional black hole mass function (CMF) of our quasars. Although the results are consistent with no dependence on halo mass, we observe a slight indication of downsizing of the black hole mass function. The lack of halo mass dependence in the CLF and CMF shows that quasars residing in galaxy clusters have characteristic luminosity and black hole mass scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

Context. Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. However, modeling the selection function of such samples is complicated by noncluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). Aims. We empirically constrained the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy Survey data with the redMaPPer algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). Methods. For matched clusters, we augmented the redMaPPer catalog with the SPT detection significance. For unmatched objects we used the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multiwavelength data, we explored various physically motivated models to describe the relationship between observed richness and halo mass. Results. Our analysis reveals a clear preference for models with an additional skewed scatter component associated with projection effects over a purely log-normal scatter model. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge that requires a combination of simulation and data-driven approaches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

We introduce the deep post Hartree–Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree–Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

NEAMS-ATF M3 Milestone Report: Literature Review of Modeling of Radiation-Induced Swelling in Fe-Cr-Al Steels

Fe-Cr-Al steels are proposed as accident-tolerant-fuel (ATF) cladding materials in light water reactors due to their excellent oxidation resistance at high temperatures. Currently, the understanding of their performance in reactor environment is still limited. In this review, firstly we reviewed the experimental studies of Fe-Cr-Al based alloys with particular focus on the radiation effects in these alloys. Although limited data are available in literature, several previous and recent experimental studies have shown that Fe-Cr-Al based alloys have very good void swelling resistance at low and moderate irradiation doses but the growth of dislocation loops is very active. Overall, the behavior of radiation damage evolution is similar to that in Fe-Cr ferritic/martensitic alloys. Secondly, we reviewed the rate theory-based modeling methods for modeling the coevolution of voids and dislocation loops in materials under irradiation such as Frenkel pair three-dimensional diffusion model (FP3DM) and cluster dynamics. Finally, we summarized and discussed our review and proposed our future plans for modeling radiation damage in Fe-Cr-Al based alloys.

36 MATERIALS SCIENCE↗
NASA NTRS1984

X-ray spectra of clusters of galaxies

The X-ray emission from luminous clusters of galaxies is dominated by thermal bremsstrahlung from an intergalactic medium. The central density of the gas is strongly correlated with the X-ray surface brightness. The X-ray surface brightness S(sigma) of many clusters is well modeled by a law of the form S(sigma) alpha S(o) 1 + R(2)/A(2) to the minus 3 beta + 1/2 power with beta approximately equal to 0.66. However, this model does not fit the X-ray spectral or optical galaxy counts well. In clusters with cooling flows in their center there is a strong correlation between the cooling rates of X-ray emitting material and optical H alpha emission. It is not clear, at present, what percentage of the virial mass of the cluster is in hot gas but if beta = 0.66, it is possible for the values to be of the order of 1/2. Spatially resoled X-ray spectroscopy is necessary to determine this value with any accuracy.

Mushotzky, R. F.↗
DOE OSTI2017

Shocks and Tides Quantified in the “Sausage” Cluster, CIZA J2242.8+5301 Using N-body/Hydrodynamical Simulations

The colliding cluster, CIZA J2242.8+5301, displays a spectacular, almost 2 Mpc long shock front with a radio based Mach number M≃5, that is puzzlingly large compared to the X-ray estimate of M≃2.5. The extent to which the X-ray temperature jump is diluted by cooler unshocked gas projected through the cluster currently lacks quantification. Here we apply our self-consistent N-body/hydrodynamical code (based on FLASH) to model this binary cluster encounter. We can account for the location of the shock front and also the elongated X-ray emission by tidal stretching of the gas and dark matter between the two cluster centers. The required total mass is 8.9×10{sup 14} M{sub ⊙} with a 1.3:1 mass ratio favoring the southern cluster component. The relative velocity we derive is ≃2500 km s{sup −1} initially between the two main cluster components, with an impact parameter of 120 kpc. This solution implies that the shock temperature jump derived from the low angular resolution X-ray satellite Suzaku is underestimated by a factor of two, due to cool gas in projection, bringing the observed X-ray and radio estimates into agreement. Finally, we use our model to generate Compton-y maps to estimate the thermal Sunyaev–Zel’dovich (SZ) effect. At 30 GHz, this amounts to ΔS{sub n}=−0.072 mJy/arcmin{sup 2} and ΔS{sub s}=−0.075 mJy/arcmin{sup 2} at the locations of the northern and southern shock fronts respectively. Our model estimate agrees with previous empirical estimates that have inferred the measured radio spectra of the radio relics can be significantly affected by the SZ effect, with implications for charged particle acceleration models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

The magnetic effects on the energetic landscape of Fe-Cu alloy: A model Hamiltonian approach

Using ab initio calculations and the magnetic cluster expansion formula, a model with the magnetic effects on the energetic landscape of Fe–Cu was developed. The (partially) disordered local moment picture and Monte Carlo simulation method were combined to study the magnetically disordering effects on the thermodynamic properties in Fe–Cu alloys. The simulation results show nonlinear enhancements of Fe magnetism with increasing Cu concentration under varying magnetic ordering/disordering states, and the nonlinearity of the enhancement is more significant under the ferromagnetic state. It is also found that magnetism has subtle effects on the clustering of Cu because both the substitutional and binding energies could be affected but in different ways depending on the magnetic states. The results suggest that the alloying-induced enhancement of the Fe magnetism is a fundamental feature of Fe–Cu, which introduces difference on the concentration dependence of mixing, depending on the magnetic ordering/disordering states. In conjunction with the concentration dependence of short-range ordering, the enhancement of Fe magnetism also strongly affects the Curie temperatures in Fe–Cu.

Ab Initio Calculations, Fe-Cu, ultrasonic backscat↗
DOE OSTI2026

Data and scripts associated with “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments”

This data package is associated with the publication “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments” published in Scientific Reports (Garayburu-Caruso et al., 2026). The package contains processed data products and scripts used to quantify how drying and re-inundation of riverbed sediments influence dissolved organic matter (DOM) thermodynamic properties and their relationship with sediment oxygen (O₂) consumption across 33 stream sites in the contiguous United States. The data package contains DOM thermodynamic metrics (e.g., Gibbs free energy of carbon oxidation and thermodynamic efficiency), and O₂ consumption along with watershed-scale climate and land-cover metrics used as explanatory variables in the analyses. Underlying unprocessed and processed ultrahigh-resolution mass spectrometry data, oxygen consumption rates from laboratory moisture-manipulation experiments, within-sample environmental properties, sediment moisture content and contextual field measurements are archived separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2428003 (Laan et al., 2024) and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689 (Forbes et al.,2023). A preliminary version of this data package was published in February 2026 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include the finalized data and additional metadata (readme, data dictionary, and file level metadata). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. At the top level, the data package is organized into five main folders: (1) Data, (2)Figures, (3) Map, (4) GAM_Reulsts, and (5) src. The Data folder contains analysis-ready tabular files with oxygen consumption rates, DOM thermodynamic properties by site and treatment, site-level environmental variables, watershed-scale metrics, and other derived variables referenced in the manuscript. The Figures folder contains static image files associated with the main text and supplemental figures, while the Map folder includes spatial data and map-layer files used to create the sampling-location map. The GAM results folder contains the results for each of the general additive model (GAM).The src folder contains R scripts used to perform data processing, statistical analyses (including clustering, generalized additive models, and threshold analysis), and figure generation. This data package is associated with a GitHub repository found at https://github.com/WHONDRS-Hub/ECA_DOM_Thermodynamics.

Dissolved organic matter↗
DOE OSTI2019

PCS: A Productive Computational Science Platform

As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next- generation scientific applications. We propose a new scheme — Edge Neural Network — for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2019

PCS: A Productive Computational Science Platform

As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next-generation scientific applications. We propose a new scheme — Edge Neural Network — for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.

Ojika, David↗
DOE OSTI2019

PCS: A Productive Computational Science Platform

As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next- generation scientific applications. We propose a new scheme - Edge Neural Network - for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Frequentist cosmological constraints from full-shape clustering measurements in DESI DR1

We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set {σ 8 , H 0 , Ω m , w 0 , w a } using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the w 0 w a CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following 1σ frequentist confidence intervals for ΛCDM (w 0 w a CDM): σ 8 = 0.863 +0.048 -0.040 , H 0 = 68.96 +0.81 -0.80 km s -1 Mpc -1 , Ω m = 0.3034 ± 0.0110 (σ 8 = 0.782 +0.060 -0.036 , H 0 = 63.7 +4.2 -2.0 km s -1 Mpc -1 , Ω m = 0.378 +0.024 -0.047 , w 0 = -0.16 +0.10 -0.50 , w a = -3.0 +1.7 ), corresponding to 0.8σ, 0.3σ, 0.7σ (2.1σ, 4.1σ, 6.5σ, 6.3σ, 6.6σ) shifts between the maximum likelihood estimate and the Bayesian posterior mean for ΛCDM (w 0 w a CDM) respectively.

Bayesian reasoning↗
DOE OSTI2015

NEW CONSTRAINTS ON THE FAINT END OF THE UV LUMINOSITY FUNCTION AT z ∼ 7-8 USING THE GRAVITATIONAL LENSING OF THE HUBBLE FRONTIER FIELDS CLUSTER A2744

Exploiting the power of gravitational lensing, the Hubble Frontier Fields (HFF) program aims at observing six massive galaxy clusters to explore the distant universe far beyond the limits of blank field surveys. Using the complete Hubble Space Telescope observations of the first HFF cluster A2744, we report the detection of 50 galaxy candidates at z ∼ 7 and eight candidates at z ∼ 8 in a total survey area of 0.96 arcmin{sup 2} in the source plane. Three of these galaxies are multiply imaged by the lensing cluster. Using an updated model of the mass distribution in the cluster we were able to calculate the magnification factor and the effective survey volume for each galaxy in order to compute the ultraviolet galaxy luminosity function (LF) at both redshifts 7 and 8. Our new measurements reliably extend the z ∼ 7 UV LF down to an absolute magnitude of M {sub UV} ∼ –15.5. We find a characteristic magnitude of M{sub UV}{sup ⋆}=−20.90{sub −0.73}{sup +0.90} mag and a faint-end slope α=−2.01{sub −0.28}{sup +0.20}, close to previous determinations in blank fields. We show here for the first time that this slope remains steep down to very faint luminosities of 0.01 L {sup *}. Although prone to large uncertainties, our results at z ∼ 8 also seem to confirm a steep faint-end slope below 0.1 L {sup *}. The HFF program is therefore providing an extremely efficient way to study the faintest galaxy populations at z > 7 that would otherwise be inaccessible with current instrumentation. The full sample of six galaxy clusters will provide even better constraints on the buildup of galaxies at early epochs and their contribution to cosmic reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Galaxies in X-ray selected clusters and groups in Dark Energy Survey data – II. Hierarchical Bayesian modelling of the red-sequence galaxy luminosity function

Using ~100 X-ray selected clusters in the Dark Energy Survey Science Verification data, in this work we constrain the luminosity function (LF) of cluster red-sequence galaxies as a function of redshift. This is the first homogeneous optical/X-ray sample large enough to constrain the evolution of the LF simultaneously in redshift (0.1 < z < 1.05) and cluster mass ($13.5 \le \rm {log_{10}}(M_{200crit}) \sim \lt 15.0$). We pay particular attention to completeness issues and the detection limit of the galaxy sample. We then apply a hierarchical Bayesian model to fit the cluster galaxy LFs via a Schechter function, including its characteristic break (m*) to a faint end power-law slope (α). Our approach allows us to avoid known issues in similar analyses based on stacking or binning the clusters. We find weak and statistically insignificant (~1.9σ) evolution in the faint end slope α versus redshift. We also find no dependence in α or m* with the X-ray inferred cluster masses. Yet, the amplitude of the LF as a function of cluster mass is constrained to ${\sim } 20{{\ \rm per\ cent}}$ precision. As a by-product of our algorithm, we utilize the correlation between the LF and cluster mass to provide an improved estimate of the individual cluster masses as well as the scatter in true mass given the X-ray inferred masses. This technique can be applied to a larger sample of X-ray or optically selected clusters from the Dark Energy Survey, greatly improving the sensitivity of the analysis.

79 ASTRONOMY AND ASTROPHYSICS↗