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DOE OSTI2025

Neutral but Impactful: Gallium Cluster-Induced Nanopores from Beam-Blanked Gallium Ion Sources

Neutral atoms originating from liquid metal ion sources are an often-overlooked source of contamination and damage in focused ion beam microscopy. Beyond ions and single atoms, these sources also generate atom clusters. While most studies have investigated charged clusters, here we demonstrate that neutral clusters are also formed. These neutral clusters bypass the electrostatic beam blanking system, allowing them to impinge on samples even when the ion beam is blanked. We investigate this phenomenon using thin (≤20 nm) freestanding membranes of hexagonal boron nitride, silicon, and silicon nitride as targets. Randomly dispersed nanopores that form upon neutral cluster exposure are revealed. The average nanopore diameter is ∼2 nm with a narrow size distribution, suggesting that the atom clusters have a preferred size. Various electron microscopy techniques are used to characterize the nanopores, including high-resolution transmission electron microscopy, multislice ptychography, and electron energy-loss spectroscopy. Finally, we show how electron irradiation in the transmission electron microscope can be used to both remove any amorphous material that may clog the pores and to controllably grow the pores to specific sizes. Tunable nanopores such as these are interesting for nanofluidic applications requiring size-selective membranes.

Byrne, Dana O↗

DOE OSTI2020

Imaging systematics and clustering of DESI main targets

ABSTRACT We evaluate the impact of imaging systematics on the clustering of luminous red galaxies (LRG), emission-line galaxies (ELG), and quasars (QSO) targeted for the upcoming Dark Energy Spectroscopic Instrument (DESI) survey. Using Data Release 7 of the DECam Legacy Survey, we study the effects of astrophysical foregrounds, stellar contamination, differences between north galactic cap and south galactic cap measurements, and variations in imaging depth, stellar density, galactic extinction, seeing, airmass, sky brightness, and exposure time before presenting survey masks and weights to mitigate these effects. With our sanitized samples in hand, we conduct a preliminary analysis of the clustering amplitude and evolution of the DESI main targets. From measurements of the angular correlation functions, we determine power law fits $r_0 = 7.78 \pm 0.26\, h^{-1}$Mpc, γ = 1.98 ± 0.02 for LRGs and $r_0 = 5.45 \pm 0.1\, h^{-1}$Mpc, γ = 1.54 ± 0.01 for ELGs. Additionally, from the angular power spectra, we measure the linear biases and model the scale-dependent biases in the weakly non-linear regime. Both sets of clustering measurements show good agreement with survey requirements for LRGs and ELGs, attesting that these samples will enable DESI to achieve precise cosmological constraints. We also present clustering as a function of magnitude, use cross-correlations with external spectroscopy to infer dN/dz and measure clustering as a function of luminosity, and probe higher order clustering statistics through counts-in-cells moments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

SPIDERS: overview of the X-ray galaxy cluster follow-up and the final spectroscopic data release

ABSTRACT SPIDERS (The SPectroscopic IDentification of eROSITA Sources) is a large spectroscopic programme for X-ray selected galaxy clusters as part of the Sloan Digital Sky Survey-IV (SDSS-IV). We describe the final data set in the context of SDSS Data Release 16 (DR16): the survey overall characteristics, final targeting strategies, achieved completeness, and spectral quality, with special emphasis on its use as a galaxy cluster sample for cosmology applications. SPIDERS now consists of about 27 000 new optical spectra of galaxies selected within 4000 photometric red sequences, each associated with an X-ray source. The excellent spectrograph efficiency and a robust analysis pipeline yield a spectroscopic redshift measurement success rate exceeding 98 per cent, with a median velocity accuracy of 20 km s−1 (at z = 0.2). Using the catalogue of 2740 X-ray galaxy clusters confirmed with DR16 spectroscopy, we reveal the 3D map of the galaxy cluster distribution in the observable Universe up to z ∼ 0.6. We highlight the homogeneity of the member galaxy spectra among distinct regions of the galaxy cluster phase space. Aided by accurate spectroscopic redshifts and by a model of the sample selection effects, we compute the galaxy cluster X-ray luminosity function and we present its lack of evolution up to z = 0.6. Finally we discuss the prospects of forthcoming large multiplexed spectroscopic programmes dedicated to follow up the next generation of all-sky X-ray source catalogues.

Clerc, N.↗

DOE OSTI2020

Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe

ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $M_r^* + 3$, we limit our work to 30 clusters with $z$phot-cl ≤ 0.045. Masses (M200) are larger than $\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$ (most above $3\times 10^{14} \, \mathrm{M}_{\odot }$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $\sim 92{\rm{ per\ cent}}$ and purity, P $\sim 87{\rm{ per\ cent}}$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.

Lopes, Paulo A. A.↗

DOE OSTI2021

Measuring cosmological distances using cluster edges as a standard ruler

ABSTRACT The line-of-sight velocity dispersion profile of galaxy clusters exhibits a ‘kink’ corresponding to the spatial extent of orbiting galaxies. Because the spatial extent of a cluster is correlated with the amplitude of the velocity dispersion profile, we can utilize this feature as a gravity-calibrated standard ruler. Specifically, the amplitude of the velocity dispersion data allows us to infer the physical cluster size. Consequently, observations of the angular scale of the ‘kink’ in the profile can be translated into a distance measurement to the cluster. Assuming the relation between cluster radius and cluster velocity dispersion can be calibrated from simulations, we forecast that with existing data from the Sloan Digital Sky Survey we will be able to measure the Hubble constant with 3.0 per cent precision. Implementing our method with data from the Dark Energy Spectroscopic Instrument (DESI) will result in a 1.3 per cent measurement of the Hubble constant. Adding cosmological supernova data improves the uncertainty of the DESI measurement to 0.7 per cent. While these error estimates are statistical only, they provide strong motivation for pursuing the necessary simulation program required to characterize and calibrate the systematic uncertainties impacting our proposed measurement. Whether or not our proposed measurement can in fact result in competitive H0 constraints will depend on what the eventual systematics floor for this method is.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Preliminary clustering properties of the DESI BGS bright targets using DR9 Legacy Imaging Surveys

ABSTRACT We characterize the selection cuts and clustering properties of a magnitude-limited sample of bright galaxies that is part of the Bright Galaxy Survey (BGS) of the Dark Energy Spectroscopic Instrument (DESI) using the ninth data release of the Legacy Imaging Surveys (DR9). We describe changes in the DR9 selection compared to the DR8 one and we also compare the DR9 selection in three distinct regions: BASS/MzLS in the north Galactic Cap (NGC), DECaLS in the NGC, and DECaLS in the south Galactic Cap (SGC). We investigate the systematics associated with the selection and assess its completeness by matching the BGS targets with the Galaxy and Mass Assembly (GAMA) survey. We measure the angular clustering for the overall bright sample (rmag ≤ 19.5) and as function of apparent magnitude and colour. This enables to determine the clustering strength r0 and slope γ by fitting a power-law model that can be used to generate accurate mock catalogues for this tracer. We use a counts-in-cells technique to explore higher order statistics and cross-correlations with external spectroscopic data sets in order to check the evolution of the clustering with redshift and the redshift distribution of the BGS targets using clustering redshifts. While this work validates the properties of the BGS bright targets, the final target selection pipeline and clustering properties of the entire DESI BGS will be fully characterized and validated with the spectroscopic data of Survey Validation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

WALLABY pilot survey: H i gas disc truncation and star formation of galaxies falling into the Hydra I cluster

ABSTRACT We present results from our analysis of the Hydra I cluster observed in neutral atomic hydrogen (H i) as part of the Widefield ASKAP L-band Legacy All-sky Blind Survey (WALLABY). These WALLABY observations cover a 60-square-degree field of view with uniform sensitivity and a spatial resolution of 30 arcsec. We use these wide-field observations to investigate the effect of galaxy environment on H i gas removal and star formation quenching by comparing the properties of cluster, infall, and field galaxies extending up to ∼5R200 from the cluster centre. We find a sharp decrease in the H i-detected fraction of infalling galaxies at a projected distance of ∼1.5R200 from the cluster centre from $\sim 85{{\ \rm per\ cent}}$ to $\sim 35{{\ \rm per\ cent}}$. We see evidence for the environment removing gas from the outskirts of H i-detected cluster and infall galaxies through the decrease in the H i to r-band optical disc diameter ratio. These galaxies lie on the star-forming main sequence, indicating that gas removal is not yet affecting the inner star-forming discs and is limited to the galaxy outskirts. Although we do not detect galaxies undergoing galaxy-wide quenching, we do observe a reduction in recent star formation in the outer disc of cluster galaxies, which is likely due to the smaller gas reservoirs present beyond the optical radius in these galaxies. Stacking of H i non-detections with H i masses below $M_{\rm {HI}}\lesssim 10^{8.4}\, \rm {M}_{\odot }$ will be required to probe the H i of galaxies undergoing quenching at distances ≳60 Mpc with WALLABY.

Reynolds, T. N. (ORCID:0000000266065953)↗

DOE OSTI2022

X-ray morphology of cluster-mass haloes in self-interacting dark matter

ABSTRACT We perform cosmological zoom-in simulations of 19 relaxed cluster-mass haloes with the inclusion of adiabatic gas in the cold dark matter (CDM) and self-interacting dark matter (SIDM) models. These clusters are selected as dynamically relaxed clusters from a parent simulation with $M_{\rm 200} \simeq (1\!-\!3)\times 10^{15}{\, \rm M_\odot }$. Both the dark matter and the intracluster gas distributions in SIDM appear more spherical than their CDM counterparts. Mock X-ray images are generated based on the simulations and are compared to the real X-ray images of 84 relaxed clusters selected from the Chandra and ROSAT archives. We perform ellipse fitting for the isophotes of mock and real X-ray images and obtain the ellipticities at cluster-centric radii of $r\simeq 0.1\!-\!0.2R_{\rm 200}$. The X-ray isophotes in SIDM models with increasing cross-sections are rounder than their CDM counterparts, which manifests as a systematic shift in the distribution function of ellipticities. Unexpectedly, the X-ray morphology of the observed non-cool-core clusters agrees better with SIDM models with cross-section $(\sigma /m)= 0.5\!-\!1\, {\rm cm}^2\, {\rm g}^{-1}$ than CDM and SIDM with $(\sigma /m)=0.1\, {\rm cm}^2\, {\rm g}^{-1}$. Our statistical analysis indicates that the latter two models are disfavoured at the $68{{\ \rm per\ cent}}$ confidence level (as conservative estimates). This conclusion is not altered by shifting the radial range of measurements or applying a temperature selection criterion. However, the primary uncertainty originates from the lack of baryonic physics in the adiabatic model, such as cooling, star formation and feedback effects, which still have the potential to reconcile CDM simulations with observations.

Shen, Xuejian (ORCID:000000026196823X)↗

DOE OSTI2024

Redshift evolution and covariances for joint lensing and clustering studies with DESI Y1

ABSTRACT Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N-body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in $0.4 < z < 0.8$, subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width $\Delta z = 0.1$, evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of $0.1h^{-1}$ Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Measuring M31 globular cluster ages and metallicities using both photometry and spectroscopy

ABSTRACT The ages and metallicities of globular clusters play an important role not just in testing models for their formation and evolution but also in understanding the assembly history of their host galaxies. Here, we use a combination of imaging and spectroscopy to measure the ages and metallicities of globular clusters in M31, the closest massive galaxy to our own. We use the strength of the near-infrared calcium triplet spectral feature to provide a relatively age-insensitive prior on the metallicity when fitting stellar population models to the observed photometry. While the age–extinction degeneracy is an issue for globular clusters projected on to the disc of M31, we find generally old ages for globular clusters in the halo of M31 and in its satellite galaxy NGC 205 in line with previous studies. We measure ages for a number of outer halo globular clusters for the first time, finding that globular clusters associated with halo substructure extend to younger ages and higher metallicities than those associated with the smooth halo. This is in line with the expectation that the smooth halo was accreted earlier than the substructured halo.

Usher, Christopher (ORCID:0000000273837106)↗

DOE OSTI2024

Impact of property covariance on cluster weak lensing scaling relations

ABSTRACT We present an investigation into a hitherto unexplored systematic that affects the accuracy of galaxy cluster mass estimates with weak gravitational lensing. Specifically, we study the covariance between the weak lensing signal, ΔΣ, and the ‘true’ cluster galaxy number count, Ngal, as measured within a spherical volume that is void of projection effects. By quantifying the impact of this covariance on mass calibration, this work reveals a significant source of systematic uncertainty. Using the MDPL2 simulation with galaxies traced by the SAGE semi-analytic model, we measure the intrinsic property covariance between these observables within the three-dimensional vicinity of the cluster, spanning a range of dynamical mass and redshift values relevant for optical cluster surveys. Our results reveal a negative covariance at small radial scales (R ≲ R200c) and a null covariance at large scales (R ≳ R200c) across most mass and redshift bins. We also find that this covariance results in a $2{\!-\!}3~{{\ \rm per\ cent}}$ bias in the halo mass estimates in most bins. Furthermore, by modelling Ngal and ΔΣ as multi-(log)-linear equations of secondary halo properties, we provide a quantitative explanation for the physical origin of the negative covariance at small scales. Specifically, we demonstrate that the Ngal–ΔΣ covariance can be explained by the secondary properties of haloes that probe their formation history. We attribute the difference between our results and the positive bias seen in other works with (mock)-cluster finders to projection effects. These findings highlight the importance of accounting for the covariance between observables in cluster mass estimation, which is crucial for obtaining accurate constraints on cosmological parameters.

Astronomy & Astrophysics↗

DOE OSTI2020

Mixed ortho- H 2 and para- H 2 clusters studied by vibrational coherent anti-Stokes Raman spectroscopy

The search for macroscopic quantum effects, including superfluidity, in molecular hydrogen is mostly focused on its parahydrogen (p-H 2 ) nuclear spin modification because of weaker intermolecular interaction compared to orthohydrogen (o-H 2 ), both modifications being bosonic. In this work, mixed clusters of o-H 2 and p-H 2 containing similar to 10(4) molecules are prepared by supersonic expansion with helium and studied by vibrational coherent anti-Stokes Raman scattering (CARS) spectroscopy. At similar experimental conditions the neat p-H 2 clusters avoid freezing and remain fluid at 1-2 K, which is predicted to be the realm of their superfluid behavior [Phys. Rev. Lett. 101, 205301 (2008)]. Dependence of the vibrational frequencies and intensities of the main CARS peaks due to o-H 2 and p-H 2 versus the ratio of the o-H 2 and p-H 2 concentrations in the expanding gas suggests that o-H 2 and p-H 2 molecules are uniformly mixed in the interior of the clusters. A weak spectral feature at 4157 cm -1 that appears independent of the concentration ratio is assigned to the outer shell of the clusters enriched with p-H 2 molecules. Although the phase of the mixed clusters could not be unambiguously identified, the shift of the vibrational frequencies with respect to the bulk solid is consistent with the liquid state of the clusters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Continuous momentum dependence in the dynamical cluster approximation

The dynamical cluster approximation (DCA) is a quantum cluster extension to the single-site dynamical mean-field theory that incorporates spatially nonlocal dynamic correlations systematically and nonperturbatively. The DCA + algorithm addresses the cluster shape dependence of the DCA and improves the convergence with cluster size by introducing a lattice self-energy with continuous momentum dependence. However, we show that the DCA + algorithm is plagued by a fundamental problem when its self-consistency equations are formulated using the bare Green's function of the cluster. This problem is most severe in the strongly correlated regime at low doping, where the DCA + self-energy becomes overly metallic and local, and persists to cluster sizes where the standard DCA has long converged. In view of the failure of the DCA + algorithm, we propose to complement DCA simulations with a post-interpolation procedure for single-particle and two-particle correlation functions to preserve continuous momentum dependence and the associated benefits in the DCA. We demonstrate the effectiveness of this practical approach with results for the half-filled and hole-doped two-dimensional Hubbard model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Masses of Sunyaev-Zel’dovich galaxy clusters detected by the Atacama Cosmology Telescope: Stacked lensing measurements with Subaru HSC year 3 data

We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5 σ -level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R [ h - 1 Mpc ] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445 , 0.445 < z ≤ 0.695 , and 0.695 < z ≤ 1.180 , with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample ( 2.1 - 2.4 × 10 14 h - 1 M ⊙ ), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2 , 1.6 ± 0.2 , and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1 σ -level difference in our parameter inferences.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗

DOE OSTI2023

Clustering High-dimensional Toxicogenomics Data with Rare Signals

Toxicogenomics studies the gene and protein activities to drug treatments or toxic exposures. As the drugs and genes are numerous, toxicogenomics data are naturally high dimensional, with dimension sizes up to millions. In addition, the distribution of toxicogenomics data is oftentimes skewed, and they contain rare but important signals representing a cell or organism’s response to toxicity. The combination of high dimension and extremely skewed distribution of toxicogenomics data makes clustering analysis extremely challenging.We present our study of clustering toxicogenomics data using classical approaches such as principal component analysis as well as deep learning approaches such as auto-encoders. Our experiments show that these approaches fail to preserve rare signals and produce high-quality clusters. We then explore augmenting matrix factorization with deep learning techniques such as attention mechanism to produce latent representations for clustering. Our technique is able to better preserve rare signals after dimensionality reduction than prior approaches. Furthermore, we combine our augmented matrix factorization with a mechanism similar to autoencoder to balance separable clusters and low regeneration errors. Our experiments demonstrate better clustering with our proposed approach.

Cong, Guojing↗

DOE OSTI2025

Investigation of the Cyanothece nitrogenase cluster in Synechocystis : a blueprint for engineering nitrogen-fixing photoautotrophs

ABSTRACT The nitrogenase gene cluster of unicellular diazotrophic cyanobacteria, such asCyanothece, is frequently selected by nature for nitrogen-fixing partnerships with eukaryotic phototrophs. The essential cluster components that confer an advantage in such partnerships remain underexplored. To use this cluster for the development of synthetic, phototrophic nitrogen-fixing systems, a thorough and systematic analysis of its constituent genes is necessary. An initial effort to assess the possibility of engineering this cluster into non-diazotrophic phototrophs led to the generation of aSynechocystis6803 strain with significant nitrogenase activity. In the current study, a refactoring approach was taken to determine the dispensability of the non-structural genes in the cluster and define a minimal gene set for constructing a functional nitrogenase for phototrophs. Using a bottom-up strategy, thenifgenes fromCyanothece51142 were re-organized to form new operons. The genes were then seamlessly removed to determine their essentiality in the nitrogen fixation process. We demonstrate that besides the structural genesnifHDK,nifBSUENPVZTXW, as well ashesAB, are important for optimal nitrogenase function in a phototroph. We also show that optimal expression of these genes is crucial for efficient nitrogenase activity. Our findings provide a solid foundation for generating synthetic systems that will facilitate solar-powered conversion of atmospheric nitrogen into nitrogen-rich compounds, a stride toward a greener world. IMPORTANCE Integrating nitrogen fixation genes into various photosynthetic organisms is an exciting strategy for converting atmospheric nitrogen into nitrogen-rich products in a green and energy-efficient way. In order to facilitate this process, it is essential that we understand the fundamentals of the functioning of a prokaryotic nitrogen-fixing machinery in a non-diazotrophic, photoautotrophic cell. This study examines a nitrogenase gene cluster that has been naturally selected on multiple occasions for a nitrogen-fixing partnership by eukaryotic photoautotrophs and provides a basic blueprint for designing a photosynthetic organism with nitrogen-fixing ability.

Microbiology↗

DOE OSTI2022

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

Abstract Background The Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs. Results Profiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload. Discussion Optimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

59 BASIC BIOLOGICAL SCIENCES↗