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DOE OSTI2024

Joint analyses of lensing, clustering, and galaxy clusters with DES and LSST (Early Career Award DE-SC0020247 Final Report)

Final technical report for Early Career Award DE-SC0020247 "Joint analyses of lensing, clustering, and galaxy clusters with DES and LSST". This report summarizes contributions to Dark Energy Survey analyses of weak lensing, clustering, and galaxy clusters, and contributions to the development of LSST-DESC cosmology analysis pipelines.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

The Thermal and Kinematic Sunyaev–Zeldovich Effect in Galaxy Clusters and Filaments Using Multifrequency Temperature Maps of the Cosmic Microwave Background: A399–A401 Cluster Pair Case Study

We present a multifrequency and multi-instrument methodology to study the physical properties of galaxy clusters and cosmic filaments using cosmic microwave background observations. Our approach enables simultaneous measurement of both the thermal (tSZ) and kinematic Sunyaev–Zeldovich (kSZ) effects, incorporates relativistic corrections, and models astrophysical foregrounds such as thermal dust emission. We do this by jointly fitting a single physical model across multiple maps from multiple instruments at different frequencies, rather than fitting a model to a single Compton-y map. We demonstrate the success of this method by fitting the A399–A401 galaxy cluster pair and filament system using archival data from the Planck satellite and new, targeted deep data from the Atacama Cosmology Telescope, covering 11 different frequencies over 14 maps from 30 GHz to 545 GHz. Our tSZ results are consistent with previous work using Compton-y maps. We measure the line-of-sight peculiar velocities of the cluster–filament system using the kSZ effect and find statistical uncertainties on individual cluster peculiar velocities of ≲600 km s −1 , which are competitive with current state-of-the-art measurements. Additionally, we measure the optical depth of the filament component with a signal-to-noise of 8.5σ and reveal hints of its morphology. This modular approach is well-suited for application to future instruments across a wide range of millimeter and submillimeter wavebands.

Gill, Ajay S. [National Research Council of Canada↗
DOE OSTI2024

Exploring the Landscape of Distributed Graph Clustering on Leadership Supercomputers

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.

Community detection, graph algorithms↗
DOE OSTI2026

Reduced-Cost Four-Component Relativistic Double Ionization Potential Equation-of-Motion Coupled-Cluster Approaches with 4-Hole–2-Particle Excitations and Three-Body Clusters

The double ionization potential (DIP) equation-ofmotion (EOM) coupled-cluster (CC) method with 4-hole−2- particle (4h-2p) excitations on top of the CC with singles, doubles, and triples calculation, abbreviated as DIP-EOMCCSDT(4h-2p), along with its perturbative DIP-EOMCCSD(T)(a)(4h-2p) approximation, are extended to a relativistic four-component (4c) framework. In addition, we introduce and test a new computationally practical DIP-EOMCC approach, which we call DIPEOMCCSD( T)(ã)(4h-2p), that approximates the treatment of 4h- 2p correlations within the DIP-EOMCCSD(T)(a)(4h-2p) method and reduces the $\mathcal{N}$ 8 scaling characterizing DIP-EOMCCSDT(4h- 2p) and DIP-EOMCCSD(T)(a)(4h-2p) to $\mathcal{N}$ 7 with the system size $\mathcal{N}$. Further improvements in computational efficiency are obtained using the frozen natural spinor (FNS) approximation to reduce the numbers of unoccupied spinors entering the correlated steps of the DIP-EOMCC calculations according to a well-defined occupation-number-based threshold. The resulting 4c-FNS-DIPEOMCC approaches are used to compute DIPs for the series of inert gas atoms from argon to radon as well as the vertical DIPs in Cl 2 , Br 2 , HBr, and HI, which have been experimentally examined in the past. We demonstrate that, when using complete basis set extrapolations and FNS truncation threshold of 10 −4.5 , the 4c-FNS-DIP-EOMCCSD(T)(ã)(4h-2p) calculations are capable of predicting DIPs in agreement with experimental data, improving upon their nonrelativistic and spin-free scalar-relativistic counterparts, particularly when examining DIPs characterized by stronger spin−orbit coupling effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Association between optically identified galaxy clusters and the underlying dark matter halos

Clusters of galaxies trace massive dark matter halos in the Universe, but they can include multiple halos projected along lines of sight. Here, we study the halos contributing to clusters using the Cardinal simulation, which mimics the Dark Energy Survey data. We use the red-sequence-based cluster finding algorithm redMaPPer as a case study. For each cluster, we identify the halos hosting its member galaxies, and we define the main halo as the one contributing the most to the cluster's richness ($λ$, the estimated number of member galaxies). At $z=0.3$, for clusters with $λ> 60$, the main halo typically contributes to $92\%$ of the richness, and this fraction drops to $67\%$ for $λ\approx 20$. Defining "clean" clusters as those with $\geq50\%$ of the richness contributed by the main halo, we find that $100\%$ of the $λ> 60$ clusters are clean, while $73\%$ of the $λ\approx 20$ clusters are clean. Three halos can usually account for more than $80\%$ of the richness of a cluster. The main halos associated with redMaPPer clusters have a completeness ranging from $98\%$ at virial mass $10^{14.6}~h^{-1}M_{\odot}$ to $64\%$ at $10^{14}~h^{-1}M_{\odot}$. In addition, we compare the inferred cluster centers with true halo centers, finding that $30\%$ of the clusters are miscentered with a mean offset $40\%$ of the cluster radii, in agreement with recent X-ray studies. These systematics worsen as redshift increases, but we expect that upcoming surveys extending to longer wavelengths will improve the cluster finding at high redshifts. Our results affirm the robustness of the redMaPPer algorithm and provide a framework for benchmarking other cluster-finding strategies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The importance of accounting for the Tolman correction to surface tension for nucleation and growth modeling of Fe clusters

Gibbs free energies of clusters are required for predictive modeling of cluster growth during condensation of a cooling vapor. Here, we present a straightforward method of calculating free energies of cluster formation using the data from molecular dynamics (MD) simulations. We apply this method to iron clusters having from 2 to 100 atoms. The energies obtained are verified by comparing to an MD-simulated equilibrium cluster size distribution in a sub-saturated vapor. We show that these free energies differ significantly from those obtained with a commonly used spherical cluster approximation, which relies on a surface tension coefficient of a flat surface, as it is used in the classical nucleation theory (CNT). We show that the spherical cluster approximation in CNT can be improved by using a cluster-size-dependent Tolman correction for the surface tension. The Tolman length and effective surface tension values were derived for iron clusters, and they significantly differ from the commonly used experimentally measured values. This improved approximation does not account for geometric magic number effects responsible for spikes and troughs in densities of neighbor cluster sizes. Nonetheless, it allows to more accurately model cluster formation from a cooling vapor. It better reproduces the condensation timeline, overall shape of the cluster size distribution, average cluster size, and the distribution width. In contrast, using a constant surface tension coefficient (as done in CNT) resulted in incorrect condensation dynamics and cluster size distributions. The analytical expression for cluster nucleation rate from CNT was updated to account for the size-dependence of cluster surface tension.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Miscentring of optical galaxy clusters based on Sunyaev–Zeldovich counterparts

ABSTRACT The ‘miscentring effect’, i.e. the offset between a galaxy cluster’s optically defined centre and the centre of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalogue from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev–Zeldovich cluster catalogue from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \le z \le 1.4$ over an area of 469 deg$^2$. By modelling the distribution of centring offsets in this fiducial sample, we find a miscentred fraction (corresponding to clusters offset by more than 330 kpc) of ∼25 per cent, a value consistent with previous miscentring studies. We examine the image of each miscentred cluster in our sample and identify one of several reasons to explain the miscentring. Some clusters show significant miscentring for astrophysical reasons, i.e. ongoing cluster mergers. Others are miscentred due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentring from the sample, we find a considerably smaller miscentred fraction, $\sim 10~\,\rm per\,cent$. We show that the gravitational lensing signal within 1 Mpc of miscentred clusters is considerably smaller than that of well-centred clusters, and we suggest that the ACT SZ centres are a better estimate of the true cluster potential centroid.

Ding, Jupiter (ORCID:0000000296119799)↗
DOE OSTI2026

The Atacama Cosmology Telescope: DR6 Sunyaev-Zel'dovich Selected Galaxy Clusters Catalog

We present the results of a search for galaxy clusters in the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) microwave sky maps covering 16293 square degrees in three frequency bands, using data obtained over the lifetime of the project (2008-2022). We report redshifts and mass estimates for 10040 clusters detected via their Sunyaev-Zel'dovich (SZ) effect with signal-to-noise greater than 4 at a 2.4 arcminute filter scale. The catalog includes 1180 clusters at redshifts greater than 1, and 124 clusters at redshifts greater than 1.5. Using a relation between cluster SZ signal and mass that is consistent with recent weak-lensing measurements, we estimate that clusters detected with signal-to-noise greater than 5 form a sample which is 90% complete for clusters with masses greater than $5 \times 10^{14}$ MSun (measured within a spherical volume with mean density 500 times the critical density). El Gordo, a cluster found in an initial ACT survey of 755 square degrees, remains the most extreme cluster in mass and redshift; we find no cluster with a mass and redshift combination high enough to falsify the standard LCDM cosmology with Gaussian initial perturbations. We make public a variety of data products, including the full cluster candidate list, noise maps, and sky masks, along with our software for cluster detection and instructions for reproducing our cluster catalogs from the public ACT maps.

Aguena, M. [Trieste Observ.; LIneA, Rio de Janeiro↗
DOE OSTI2025

Weak lensing mass-richness relation of redMaPPer clusters in LSST DESC DC2 simulations

Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, where clusters are detected through their richness, cluster-weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory, starting in 2026. Cluster properties inferred from weak lensing, such as mass, suffer from several sources of bias. In this paper, we aim to test the impact of modeling choices and observational systematics in cluster lensing on the inference of the mass-richness relation. We constrained the mass-richness relation of 3600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extragalactic mock catalog of the LSST DESC DC2 simulation, covering 440 deg 2 , using number count measurements and either stacked weak lensing profiles or mean cluster masses in several intervals of richness (20 ≤ λ ≤ 200) and redshift (0.2 ≤ z ≤ 1). We provide the first constraints on the redMaPPer cluster mass-richness relation detected in cosmoDC2. We find that for an LSST-like source galaxy density, our constraints are robust to changes in the concentration-mass relation, as well as the dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We find that photometric redshift uncertainties can introduce bias at the 1σ level, which could be mitigated by an overall correction factor fitted jointly with the scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5σ. Our constraints also offer a fair comparison to a fiducial mass-richness relation, obtained from matching cosmoDC2 halo masses to redMaPPer-detected cluster richness results.

galaxy clusters↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2024

Molecular dynamics study of interstitial He clusters in nickel

This study presents a molecular dynamics analysis focusing on the behavior of interstitial helium (He) clusters in nickel (Ni), examining their formation, stability, and migration energetics. Consistent with previous research, we found that the binding energies of interstitial helium within a helium cluster are positive and increase with the cluster size, indicating a preference for helium atoms to cluster together. However, our findings also reveal that while the formation energy increases monotonically with cluster size, the increase in binding energy is non-monotonic. Importantly, small He clusters were observed to be thermally unstable at reactor operational temperatures (approximately 600 K), with the He 2 cluster exhibiting instability even at room temperature. With a binding energy of 0.49 eV for a He 4 cluster, we hypothesize that for helium bubbles to form via homogeneous nucleation (i.e., through trap mutation) at reactor operating temperatures, the helium concentration must be high enough to facilitate the formation of helium clusters of at least size 4 or larger. As expected, interstitial helium and small helium clusters are highly mobile. This mobility was observed not only at room temperature but also at temperatures as low as approximately 200 K. Furthermore, the mean squared displacement method has been utilized to determine the migration barriers and the corresponding prefactors for clusters ranging from He 1 to He 6

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Pre- and post-processing of cluster galaxies out to 5 × R 200: the extreme case of A2670

ABSTRACT We study galaxy interactions in the large-scale environment around A2670, a massive (M200 = $8.5 \pm 1.2~\times 10^{14} \, \mathrm{{M}_{\odot }}$) and interacting galaxy cluster at z = 0.0763. We first characterize the environment of the cluster out to 5× R200 and find a wealth of substructures, including the main cluster core, a large infalling group, and several other substructures. To study the impact of these substructures (pre-processing) and their accretion into the main cluster (post-processing) on the member galaxies, we visually examined optical images to look for signatures indicative of gravitational or hydrodynamical interactions. We find that ∼21 per cent of the cluster galaxies have clear signs of disturbances, with most of those (∼60 per cent) likely being disturbed by ram pressure. The number of ram-pressure stripping candidates found (101) in A2670 is the largest to date for a single system, and while they are more common in the cluster core, they can be found even at >4 × R200, confirming cluster influence out to large radii. In support of a pre-processing scenario, most of the disturbed galaxies follow the substructures found, with the richest structures having more disturbed galaxies. Post-processing also seems plausible, as many galaxy–galaxy mergers are seen near the cluster core, which is not expected in relaxed clusters. In addition, there is a comparable fraction of disturbed galaxies in and outside substructures. Overall, our results highlight the complex interplay of gas stripping and gravitational interactions in actively assembling clusters up to 5 × R200, motivating wide-area studies in larger cluster samples.

Astronomy & Astrophysics↗
DOE OSTI2026

Constraints on the normal branch of DGP gravity from SPT galaxy clusters with DES and HST weak-lensing mass calibration and from P l a n c k PR4 CMB anisotropies

We present constraints on the normal branch of the Dvali-Gabadadze-Porrati (nDGP) braneworld gravity model from the abundance of massive galaxy clusters. On scales below the nDGP crossover scale r c , the nDGP model features an effective gravitylike fifth force that alters the growth of structure, leading to an enhancement of the halo mass function (HMF) on cluster scales. The enhanced cluster abundance allows for constraints on the nDGP model using cluster samples. We employ the South Pole Telescope (SPT) cluster sample, selected through the thermal Sunyaev-Zel’dovich effect with the SPT and with mass calibration using weak-lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample contains 1,005 clusters with redshifts 0.25 < z < 1.78 , which are confirmed with the multicomponent matched filter algorithm using optical and near-infrared data. Weak-lensing data from DES and HST enable a robust mass measurement of the cluster sample. We use DES Year 3 data for 688 clusters with redshifts z < 0.95 , and HST data for 39 clusters with redshifts 0.6 < z < 1.7 . We account for the enhancement in the HMF through a semi-analytic correction factor to the standard cosmology HMF derived from the spherical collapse model in the nDGP model. We then further calibrate this model using N -body simulations. In addition, for the first time, we analyze the primary CMB temperature and polarization anisotropy measurements from Planck PR4 within the nDGP model. We obtain a competitive constraint from the joint analysis of the SPT cluster abundance with the Planck PR4 data, and report an upper bound of 1 / H 0 r c < 1.41 at 95% when assuming a cosmology with massive neutrinos.

Vogt, S. M.L. [Munich U. Observ.; LMU Munich (main↗
DOE OSTI2025

Environmental controls on the kinetics of iron-sulfur cluster nucleation and nanoparticle formation

Anoxic, sulfidic conditions have been prevalent since the early Proterozoic and favor aqueous iron-sulfur (FeS aq ) clusters as a major fraction of the soluble, reduced iron and sulfur pool. FeS aq cluster formation and nucleation is driven by the high affinity between ferrous iron (Fe(II)) and sulfide (HS − ), ultimately yielding particles that precipitate as iron sulfide minerals. FeS aq clusters were recently shown to be bioavailable sources of iron and sulfur for a variety of anaerobes, yet little is known of the factors that influence the kinetics of their formation and nucleation. Here we apply computational and spectroscopic approaches to investigate the dynamics of FeS aq nucleation, cluster growth, precipitation, and redissolution as a function of Fe(II)/HS − concentration, temperature, and pH. Experiments were conducted under excess HS − to mimic euxinic conditions common to contemporary anaerobic aquatic ecosystems and those of the Proterozoic. Density functional theory calculations reveal the key role of water oxygen-iron interactions in stabilizing small FeS aq clusters and promoting solubility. Dynamic light scattering revealed a concentration-dependent increase in the kinetics of FeS aq nucleation and cluster aggregation. Increasing temperature promoted FeS aq cluster nucleation and aggregation while also enhancing dissolution. Alkaline pH also promoted FeS aq nucleation and cluster aggregation. At 25 °C, pH 7.0, and at reactant concentrations of 30 µM, FeS aq clusters < 10 nm in diameter remained in solution for > 2 h. These results underscore the importance of temperature, pH, and reactant concentration in the kinetics of FeS aq nucleation and cluster growth that, in turn, influence their bioavailability in anaerobic ecosystems.

Aquatic ecosystems↗