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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

A Distant, X-Ray Luminous Cluster of Galaxies at Redshift 0.83

We have observed the most distant (= 0.829) cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), with the ASCA and ROSAT satellites. We find an X-ray temperature of 12.3(sup 3.1, sub 2.2) keV for this cluster, and the ROSAT map reveals significant substructure. The high temperature of MS1054-0321 is consistent with both its approximate velocity dispersion, based on the redshifts of 12 cluster members we have obtained at the Keck and the Canada-France-Hawaii telescopes, and with its weak lensing signature. The X-ray temperature of this cluster implies a virial mass approximately 7.4 x 10(exp 14) /h solar mass, if the mean matter density in the universe equals the critical value (OMEGA(sub 0) = 1), or larger if OMEGA(sub 0) < 1. Finding such a hot, massive cluster in the EMSS is extremely improbable if clusters grew from Gaussian perturbations in an OMEGA(sub 0) = 1 universe. Combining the assumptions that OMEGA(sub 0) = 1 and that the initial perturbations were Gaussian with the observed X-ray temperature function at low redshift, we show that this probability of this cluster occurring in the volume sampled by the EMSS is less than a few times 10(exp -5). Nor is MS1054-0321 the only hot cluster at high redshift; the only two other z > 0.5 EMSS clusters already observed with ASCA also have temperatures exceeding 8 keV. Assuming again that the initial perturbations were Gaussian and OMEGA(sub 0) = 1, we find that each one is improbable at the < 10(exp -2) level. These observations, along with the fact that these luminosities and temperatures of the high-z clusters all agree with the low-z L(sub x) - T(sub x) relation, argue strongly that OMEGA(sub 0) < 1. Otherwise, the initial perturbations must be non-Gaussian, if these clusters' temperatures do indeed reflect their gravitational potentials.

Donahue, Megan↗
NASA NTRS1998

The CfA-Rosat Survey of Distant Clusters of Galaxies

We (Vikhlinin, McNamara, Forman, Jones, Hornstrup, Quintana) have completed a new survey of distant clusters of galaxies, which we use to to study cluster evolution over cosmological timescales. The clusters were identified as extended X-ray sources in 650 ROSAT PSPC images of high Galactic latitude fields. Our catalog of approximately 230 extended X-ray sources covers 160 square degrees on the sky. Ours is the largest of the several ROSAT serendipitous cluster surveys in progress (e.g. SHARC, Rosati, WARPS etc.). Using V,R,I imagery obtained at several observatories, we find that greater than 90% of the X-ray sources are associated with distant clusters of galaxies. We have obtained spectroscopic redshifts for nearly 80 clusters in our catalog, and we have measured photometric redshifts for the remaining clusters. Our sample contains more than 20 clusters at z > 0.5. I will discuss the logN-logS relationship for our clusters. Because our large survey area, we are able to confirm the evolution of the most luminous distant clusters first seen in the Einstein Extended Medium Sensitivity Survey. In addition, I will discuss the relationships between optical richness, core radius, and X-ray luminosity for distant, X-ray-selected clusters.

McNamara, Brian↗
NASA NTRS1999

Characterizing the Small Scale Structure in Clusters of Galaxies

We have applied a wavelet transform analysis to quantify the small-scale (less than a few arcminutes) X-ray structure of clusters of galaxies to several clusters. Three of these clusters show interesting structure. In addition to the small scale structure in surface brightness, we have searched for temperature structure using Advanced Satellite for Cosmology and Astrophysics (ASCA) observations. For A754, we find a filamentary structure, similar to that previously detected in A85. For A119, we find small scale structure associated with both cool and hot regions with a hot filament extending to the northeast. For A1750, one of the first clusters to show substructure from Einstein, we have a rederived the substructure from the ROSAT observations. The ASCA observations show a hot region between the two small scale features - sub-clusters. The hot region shows that the two subclusters are merging and producing a shock wave at the leading edge of the interaction. This analysis had been applied to a sample of 17 clusters to determine the radial distribution of substructure in a sample. The number of detected substructures was rather small and it was decided to select a different cluster sample at larger redshift. This provided a larger radial range over which substructure could be detected. This new cluster sample also includes about 20 clusters and is being analyzed. Finally, we are considering how to compare the substructure parameters of nearby clusters with a more distant sample to see if one can extract cosmological information. Since substructure frequency should depend on the cosmological density parameter, it may be possible to derive new constraints by comparing nearby and distant clusters.

Forman, William R.↗
NASA NTRS1998

A Very Hot, High Redshift Cluster of Galaxies: More Trouble for Omega(0) = 1

We have observed the most distant (= 0.829) cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), with the ASCA and ROSAT satellites. We find an X-ray temperature of 12.3 (sup +3.1) (sub -2.2)keV for this cluster, and the ROSAT map reveals significant substructure. The high temperature of MS1054-0321 is consistent with both its approximate velocity dispersion, based on the redshifts of 12 cluster members we have obtained at the Keck and the Canada-France-Hawaii telescopes, and with its weak lensing signature. The X-ray temperature of this cluster implies a virial mass approx. 7.4 x 10 (sup 14) h (sup -1) M (circle dot), if the mean matter density in the universe equals the critical value (OMEGA (sub 0) = 1), or larger if OMEGA (sub 0) is less than 1. Finding such a hot, massive cluster in the EMSS is extremely improbable if clusters grew from Gaussian perturbations in an OMEGA (sub 0) = 1 universe. Combining the assumptions that OMEGA (sub 0) = 1 and that the initial perturbations were Gaussian with the observed X-ray temperature function at low redshift, we show that this probability of this cluster occurring in the volume sampled by the EMSS is less than a few times 10 (sup -5). Nor is MS1054-0321 the only hot cluster at high redshift; the only two other z greater than 0.5 EMSS clusters already observed with ASCA also have temperatures exceeding 8 keV. Assuming again that the initial perturbations were Gaussian and OMEGA (sub 0) = 1, we find that each one is improbable at the less than 10 (sup -2) level. These observations, along with the fact that these luminosities and temperatures of the high-z clusters all agree with the low-z L (sub X) - T (sub X) relation, argue strongly that OMEGA (sub 0) less than 1. Otherwise, the initial perturbations must be non-Gaussian, if these clusters' temperatures do indeed reflect their gravitational potentials.

Donahue, Megan↗
NASA NTRS2004

Using Fuzzy Clustering for Real-time Space Flight Safety

To ensure space flight safety, it is necessary to monitor myriad sensor readings on the ground and in flight. Since a space shuttle has many sensors, monitoring data and drawing conclusions from information contained within the data in real time is challenging. The nature of the information can be critical to the success of the mission and safety of the crew and therefore, must be processed with minimal data-processing time. Data analysis algorithms could be used to synthesize sensor readings and compare data associated with normal operation with the data obtained that contain fault patterns to draw conclusions. Detecting abnormal operation during early stages in the transition from safe to unsafe operation requires a large amount of historical data that can be categorized into different classes (non-risk, risk). Even though the 40 years of shuttle flight program has accumulated volumes of historical data, these data don t comprehensively represent all possible fault patterns since fault patterns are usually unknown before the fault occurs. This paper presents a method that uses a similarity measure between fuzzy clusters to detect possible faults in real time. A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This method is applied to downlink data from Columbia flights. The results show that this technique can detect an unexpected fault that has not been present in the training data set.

Lee, Charles↗
NASA NTRS2001

Greenhouse tomato limited cluster production systems: crop management practices affect yield

Limited-cluster production systems may be a useful strategy to increase crop production and profitability for the greenhouse tomato (Lycopersicon esculentum Mill). In this study, using an ebb-and-flood hydroponics system, we modified plant architecture and spacing and determined the effects on fruit yield and harvest index at two light levels. Single-cluster plants pruned to allow two leaves above the cluster had 25% higher fruit yields than did plants pruned directly above the cluster; this was due to an increase in fruit weight, not fruit number. Both fruit yield and harvest index were greater for all single-cluster plants at the higher light level because of increases in both fruit weight and fruit number. Fruit yield for two-cluster plants was 30% to 40% higher than for single-cluster plants, and there was little difference in the dates or length of the harvest period. Fruit yield for three-cluster plants was not significantly different from that of two-cluster plants; moreover, the harvest period was delayed by 5 days. Plant density (5.5, 7.4, 9.2 plants/m2) affected fruit yield/plant, but not fruit yield/unit area. Given the higher costs for materials and labor associated with higher plant densities, a two-cluster crop at 5.5 plants/m2 with two leaves above the cluster was the best of the production system strategies tested.

Non-NASA Center↗
NASA NTRS2001

Clustered DNA damages induced by high and low LET radiation, including heavy ions

Clustered DNA damages--here defined as two or more lesions (strand breaks, oxidized purines, oxidized pyrimidines or abasic sites) within a few helical turns--have been postulated as difficult to repair accurately, and thus highly significant biological lesions. Further, attempted repair of clusters may produce double strand breaks (DSBs). However, until recently, there was no way to measure ionizing radiation-induced clustered damages, except DSB. We recently described an approach for measuring classes of clustered damages (oxidized purine clusters, oxidized pyrimidine clusters, abasic clusters, along with DSB). We showed that ionizing radiation (gamma rays and Fe ions, 1 GeV/amu) does induce such clusters in genomic DNA in solution and in human cells. These studies also showed that each damage cluster results from one radiation hit (and its track), thus indicating that they can be induced by very low doses of radiation, i.e. two independent hits are not required for cluster induction. Further, among all complex damages, double strand breaks comprise--at most-- ~20%, with the other clustered damages being at least 80%.

NASA Discipline Radiation Health↗
NASA NTRS1998

Method and system for data clustering for very large databases

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

Zhang, Tian↗
NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗
NASA NTRS2010

The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected Via the Sunyaev-Zel'dovich Effect

We present constraints on cosmological parameters based on a sample of Sunyaev-Zel'dovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148 GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives (sigma)8 = 0.851 +/- 0.115 and w = -1.14 +/- 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find (sigma)8 + 0.821 +/- 0.044 and w = -1.05 +/- 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernova which give (sigma)8 = 0.802 +/- 0.038 and w = -0.98 +/- 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.

Sehgal, Neelima↗
NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗
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↗
DOE OSTI2025

The [4Fe-4S] Cluster of HydF Is Essential for [FeFe]-Hydrogenase Maturation

The organometallic H-cluster of the [FeFe]-hydrogenase is assembled in vivo through a complex process requiring the action of three dedicated maturation enzymes, HydG, HydE, and HydF, as well as the aminomethyl-lipoyl-H-protein (H met ) of the glycine cleavage system (GCS). Here we probe the role of HydF and its [4Fe-4S] cluster in [FeFe]-hydrogenase maturation by using a defined semisynthetic approach in which [Fe I 2 (μ-SH) 2 (CO) 4 (CN) 2 ] 2– ([2Fe] E ) is used to bypass HydE and HydG, and GCS components are used in place of cell lysate. We show that inclusion of the iron–sulfur carrier protein NfuA and the high-CO-affinity myoglobin variant Mb H64L provides dramatically improved hydrogenase activities up to 828 μmol/min/mg, equivalent to the best reported activities for Chlamydomonas reinhardtii [FeFe]-hydrogenase isolated from the native organism. Apo-HydF lacking a [4Fe-4S] cluster provides very little hydrogenase activity; however, full maturation is restored with the addition of NfuA, which we demonstrate reconstitutes the [4Fe-4S] cluster of HydF. In addition, a HydF variant lacking a [4Fe-4S] cluster by changing two cysteine ligands to alanine is completely unable to support either semisynthetic maturation using [2Fe] E , or full maturation using HydG and HydE, even in the presence of NfuA, demonstrating that the HydF [4Fe-4S] cluster is absolutely essential for [FeFe]-hydrogenase maturation. The possibility that the HydF [4Fe-4S] cluster plays a role in direct binding of [2Fe] E is negated by our results with the HydF D311C variant, which demonstrate that the labile Asp311 cluster ligand is not essential for [2Fe] E binding and HydA maturation. We therefore conclude that [2Fe]E binds HydF adjacent to, but not directly coordinated to, the [4Fe-4S] cluster. The HydF [4Fe-4S] cluster is proposed to be essential due to its impact on the [2Fe] E binding orientation and the ability of the HydF/[2Fe] E complex to form productive interactions with H met or the H met /T-protein complex during DTMA ligand biosynthesis.

cluster chemistry↗
DOE OSTI2025

Comparing XRISM Cluster Velocity Dispersions with Predictions from Cosmological Simulations: Are Feedback Models Too Ejective?

The dynamics of the intracluster medium (ICM), the hot plasma that fills galaxy clusters, are shaped by gravity-driven cluster mergers and feedback from supermassive black holes (SMBHs) in the cluster cores. XRISM measurements of ICM velocities in several clusters offer insights into these processes. We compare XRISM measurements for nine galaxy clusters (Virgo, Perseus, Centaurus, Hydra-A, PKS 0745–19, A2029, Coma, A2319, and Ophiuchus) with predictions from three state-of-the-art cosmological simulation suites, TNG-Cluster, the Three Hundred Project GADGET-X, and GIZMO-SIMBA, that employ different models of feedback. In cool cores, XRISM reveals systematically lower velocity dispersions than the simulations predict, with all 10 measurements below the median simulated values by a factor of 1.5–1.7 on average and all falling within the bottom 10% of the predicted distributions. The observed kinetic-to-total pressure ratio is also lower, with a median value of 2.2%, compared to the predicted 5.0%–6.5% for the three simulations. Outside the cool cores and in non-cool-core (NCC) clusters, simulations show better agreement with XRISM measurements, except for the outskirts of the relaxed, cool-core cluster A2029, which exhibits an exceptionally low kinetic pressure support (<1%), with none of the simulated systems in either of the three suites reaching such low levels. The NCC Coma and A2319 exhibit dispersions at the lower end but within the simulated spread. Our comparison suggests that the three numerical models may overestimate the kinetic effects of SMBH feedback in cluster cores. Additional XRISM observations of NCC clusters will clarify if there is a systematic tension in the gravity-dominated regime as well.

galaxy clusters↗
DOE OSTI2025

Fundamental Interactions of Bimetallic Cu x Pd y ( x + y = 4) Clusters Supported on the α-WC(0001) Surface and Their Performance for CO 2 Adsorption and Dissociation

The tungsten carbide α-WC(0001) surface, an active system for the activation of H 2 and important hydrogenation processes involving unsaturated hydrocarbons, can serve as a support of bimetallic clusters to produce materials with unique catalytic properties, opening routes for a wide range of technical applications. In particular, Cu x Pd y clusters are of particular interest because they combine metals with different properties. A stochastic method was applied to obtain the geometry of Cu x Pd y (x + y = 4) bare clusters, evaluating thousands of possibilities to obtain stable structures, yielding one isomer for Cu 4 , Cu 2 Pd 2 , Cu 1 Pd 3 , and Pd 4 and two isomers for Cu 3 Pd 1 . These clusters were supported on C and W terminations of the tungsten carbide (0001) surface, exploring all of the binding possibilities. The adsorption energies on the C and W terminations are in the ranges from −2.51 to −3.02 eV and from −2.26 to −3.30 eV, respectively. The strongest and weakest binding was seen for monometallic Cu 4 and Pd 4 clusters on both C and W terminations, while the Cu-Pd bimetallics have intermediate adsorption energies but lack a clear trend in terms of composition. The location of Cu x Pd y clusters over the (0001) surface induces a decrease in the work function relative to the pristine surface, while the cluster-surface Bader charge transfer and variations in the partial density of states point to changes in the electronic structure of the carbide atoms upon binding of the metallic clusters. The d-band center of the Cu x Pd y deposited on WC(0001) indicates an intermediate reactivity among Cu(111) and Pd(111) surfaces, modulating the reactivity with small numbers of Cu and Pd atoms, i.e., atom economy in catalyst design. The likelihood of existence of the most stable Cu x Pd y (x + y = 4) clusters in the temperature range of 298-400 K is 100%. The composite Cu x Pd y /α-WC(0001) (x + y = 4), is a nontrivial system since 22 isomers are needed to completely describe its structural properties. Among the isomers, seven structures are necessary to represent Cu 3 Pd 1 /α-WC(0001), five for Pd 4 /α-WC(0001), two for Cu 4 /α-WC(0001), and four for Cu 2 Pd 2 /α-WC(0001) and Cu 1 Pd 3 /α-WC(0001). The large number of cluster isomers supported on the tungsten carbide surface opens the door for several applications in the heterogeneous catalysis of the Cu x Pd y /α-WC(0001) composite, with the possibility of modulating the geometric, electronic, and chemical properties according to a desired application. Test studies for the adsorption of CO 2 indicate that the Cu x Pd y /α-WC(0001) composites are highly active for the adsorption and decomposition of the molecule, with bimetallic and admetal-carbide interactions playing a key role in the binding performance. In conclusion, this high activity indicates that these systems should be useful as catalysts for the conversion of CO 2 to oxygenates or light alkanes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Evolution of size-selected Pt cluster catalysts on prototypical oxide supports

The current quest for new pathways into sustainable, efficient and durable energy conversion technologies makes the used for a fundamental understanding of the atomic-scale phenomena underlying catalytic processes ever more pressing. In this context, characterizing catalyst particles in situ provides valuable information about the evolution of their composition, structure, oxidation state and charge state during an ongoing process. To disentangle the influence of individual parameters – temperature, pressure, gas composition, cluster size, as well as support acidity, redox state and defect density – it is crucial to control them precisely and separately in experiments. At the example of size-selected Pt n clusters – i.e. sub-nm particles defined to the exact number of atoms – on flat oxide supports, we follow their rich evolution phenomena via (synchrotron-based) X-ray photoelectron spectroscopy (XPS) and scanning tunnelling microscopy (STM) during temperature ramps and in various gas environments. Here, we present our experience with these highly defined, yet complicated-to-create samples in ultra-high vacuum (UHV) and at mbar pressures. We discuss their stability during transport to synchrotrons and under reaction conditions on three prototypical oxide supports and show the various phenomena that can be disentangled. Ptn clusters on the non-reducible silicon dioxide, SiO 2 , remain size-selected and show remarkable stability. Doping strongly influences the size-dependent binding energy shifts and changes the Pt response to oxidative and reaction conditions, which we attribute to different cluster geometries: p-type doping leads to wetting, enhanced sinter resistance and a diminished response to oxidative environments, compared to clusters on n-type doped samples. We compare the system with our previous findings of a similar change in dimensionality for Pt 20 clusters on the reducible ceria, CeO 2 (111), support, induced by modulation of its O vacancy density. The Pt n /CeO 2 system is particularly interesting for strategies to stabilize and redisperse Pt clusters dynamically. Finally, we study the evolution of Pt n clusters on another reducible oxide support, magnetite, Fe 3 O 4 (001), in 0.1 mbar alternating redox conditions at RT and elevated temperature. In analogy to findings previously reported for Pt/TiO 2 (110), the clusters either become encapsulated by a thin oxide film via strong metal–support interaction (SMSI) or deeply buried in the magnetite. Overall, our approach of following the evolution of size-selected clusters on oxide supports leads to fundamental atomic-scale insights on nano-scale catalyst materials, on our path to sustainable, dynamic and self-repairing catalysts.

Falling, Lorenz J. [Technical Univ. of Munich (Ger↗
DOE OSTI2024

The SRG/eROSITA All-Sky Survey: Dark Energy Survey year 3 weak gravitational lensing by eRASS1 selected galaxy clusters

Context. Number counts of galaxy clusters across redshift are a powerful cosmological probe if a precise and accurate reconstruction of the underlying mass distribution is performed – a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing (WL) by clusters has become the method of choice for this measurement. Aims. We measured and validated the WL signature in the shape of galaxies observed in the first three years of the Dark Energy Survey (DES Y3) caused by galaxy clusters and groups selected in the first all-sky survey performed by SRG (Spectrum Roentgen Gamma)/eROSITA (eRASS1). These data were then used to determine the scaling between the X-ray photon count rate of the clusters and their halo mass and redshift. Methods. We empirically determined the degree of cluster member contamination in our background source sample. The individual cluster shear profiles were then analyzed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we performed mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allowed us to establish that hydrodynamical modeling uncertainties at low lens redshifts (z < 0.6) are the dominant systematic limitation. At high lens redshift, the uncertainties of the sources’ photometric redshift calibration dominate. Results. With regard to the X-ray count rate to halo mass relation, we determined its amplitude, its mass trend, the redshift evolution of the mass trend, the deviation from self-similar redshift evolution, and the intrinsic scatter around this relation. Conclusions. The mass calibration analysis performed here sets the stage for a joint analysis with the number counts of eRASS1 clusters to constrain a host of cosmological parameters. We demonstrate that WL mass calibration of galaxy clusters can be performed successfully with source galaxies whose calibration was performed primarily for cosmic shear experiments, opening the way for the cluster cosmological exploitation of future optical and NIR surveys like Euclid and LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The SRG/eROSITA All-Sky Survey: Optical identification and properties of galaxy clusters and groups in the western galactic hemisphere

The first SRG/eROSITA All-Sky Survey (eRASS1) provides the largest intracluster medium-selected galaxy cluster and group catalog covering the western Galactic hemisphere. Compared to samples selected purely on X-ray extent, the sample purity can be enhanced by identifying cluster candidates using optical and near-infrared data from the DESI Legacy Imaging Surveys. Using the red-sequence-based cluster findereROMaPPer, we measured individual photometric properties (redshiftz λ , richnessλ, optical center, and BCG position) for 12000 eRASS1 clusters over a sky area of 13 116 deg 2 , augmented by 247 cases identified by matching the candidates with known clusters from the literature. The median redshift of the identified eRASS1 sample isz= 0.31, with 10% of the clusters atz> 0.72. The photometric redshifts have an accuracy ofδz/(1 +z) ≲ 0.005 for 0.05 specand velocity dispersionσ) were measured a posteriori for a subsample of 3210 and 1499 eRASS1 clusters, respectively, using an extensive compilation of spectroscopic redshifts of galaxies from the literature. We infer that the primary eRASS1 sample has a purity of 86% and optical completeness >95% forz> 0.05. For these and further quality assessments of the eRASS1 identified catalog, we applied our identification method to a collection of galaxy cluster catalogs in the literature, as well as blindly on the full Legacy Surveys covering 24069 deg 2 . Using a combination of these cluster samples, we investigated the velocity dispersion-richness relation, finding that it scales with richness as log(λ norm ) = 2.401 × log(σ) − 5.074 with an intrinsic scatter ofδ in = 0.10 ± 0.01 dex. The primary product of our work is the identified eRASS1 cluster catalog with high purity and a well-defined X-ray selection process, opening the path for precise cosmological analyses presented in companion papers.

Astronomy & Astrophysics↗