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The improved proper elements of 4100 numbered asteroids have been searched for clusterings in a, e, i space using a computer technique based on the D-criterion. A list of 14 dynamical families each with more than 15 members is presented. Quantitative measurements of the density and dimensions in phase space of each family are presented.
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.
Here, the stable equilibrium geometries, relative stabilities, and electronic and magnetic characteristics of Re n (n = 2–16) clusters were investigated by density functional theory method. The calculated fragmentation energies and second-order differences of energies exhibited interestingly that the stabilities of Re n (n = 2–16) clusters show a dramatic odd-even alternative behavior of the cluster size n: with the even-numbered Ren clusters being obviously more stable than their neighboring odd-numbered Re n clusters (beside n = 11). Simultaneously, the calculated HOMO-LUMO gaps of Re n (n = 6–16) display an oscillatory feature at large-sized Ren clusters. From the calculated magnetic moments and growth behaviors of Rhenium clusters, the magnetic Re 6 unit can be seen as the building block for the novel magnetic cluster-assembled nanomaterial. Such calculated results are in good agreement with the available experimental measurements.
Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.
An accurate ground-state intermolecular potential energy surface (PES) was calculated for the HCS + –H 2 complex. The surface was constructed from 3023 ab initio energies, computed with explicitly correlated coupled-cluster theory, CCSD(T)-F12b, with extrapolation to the complete basis set limit (VTZ-F12/VQZ-F12). The new 4D PES was used to compute rovibrational energies, rota- tional constants, and close-coupling quantum scattering calculations at low collision energies. A symmetry-adapted Lanczos algorithm was used to variationally compute the low-lying rovibrational bound states. Rotational constants for states of two isomers were determined from the energy levels and reported in hopes of motivating experiments. The rotationally inelastic state-to-state cross sections of HCS + by collision with para- and ortho-H 2 were computed and compared, and trends and propensities are discussed.
New photoelectron, ultraviolet (UV), and vacuum UV (VUV) spectra have been obtained for bromobenzene by synchrotron study with higher sensitivity and resolution than previous work. This, together with use of ab initio calculations with both configuration interaction and time dependent density functional theoretical methods, has led to major advances in interpretation. The VUV spectrum has led to identification of a considerable number of Rydberg states for the first time. The Franck-Condon (FC) analyses including both hot and cold bands lead to identification of the vibrational structure of both ionic and electronically excited states including two Rydberg states. The UV onset has been interpreted in some detail, and an interpretation based on the superposition of FC and Herzberg-Teller contributions has been performed. In a similar way, the 6 eV absorption band which is poorly resolved is analysed in terms of the presence of two ππ* states of {sup 1}A{sub 1} (higher oscillator strength) and {sup 1}B{sub 2} (lower oscillator strength) symmetries, respectively. The detailed analysis of the vibrational structure of the 2{sup 2}B{sub 1} ionic state is particularly challenging, and the best interpretation is based on equation-of-motion-coupled cluster with singles and doubles computations. A number of equilibrium structures of the ionic and singlet excited states show that the molecular structure is less subject to variation than corresponding studies for iodobenzene. The equilibrium structures of the 3b{sub 1}3s and 6b{sub 2}3s (valence shell numbering) Rydberg states have been obtained and compared with the corresponding ionic limit structures.
We report MP2 and CCSD(T) binding energies with basis sets up to pentuple zeta quality for the m = 2-6, 8 clusters. Or best CCSD(T)/CBS estimates are -4.99 kcal/mol (dimer), -15.77 kcal/mol (trimer), -27.39 kcal/mol (tetramer), -35.9 ± 0.3 kcal/mol (pentamer), -46.2 ± 0.3 kcal/mol (prism hexamer), -45.9 ± 0.3 kcal/mol (cage hexamer), -45.4 ± 0.3 kcal/mol (book hexamer), -44.3 ± 0.3 kcal/mol (ring hexamer), -73.0 ± 0.5 kcal/mol (D 2d octamer) and -72.9 ± 0.5 kcal/mol (S4 octamer). We have found that the percentage of both the uncorrected (dimer) and BSSE-corrected (dimer CP e ) binding energies recovered with respect to the CBS limit falls into a narrow range for each basis set for all clusters and in addition this range was found to decrease upon increasing the basis set. Relatively accurate estimates (within < 0.5%) of the CBS limits can be obtained when using the “ 2/3, 1/3” (for the AVDZ set) or the “½ , ½” (for the AVTZ, AVQZ and AV5Z sets) mixing ratio between dimer e and dimer CP e . Based on those findings we propose an accurate and efficient computational protocol that can be used to estimate accurate binding energies of clusters at the MP2 (for up to 100 molecules) and CCSD(T) (for up to 30 molecules) levels of theory. This work was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest National Laboratory (PNNL) is a multi program national laboratory operated for DOE by Battelle. This research also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. AC02-05CH11231.
Subvirial gravitational collapse is one mechanism by which star clusters may form. Here we investigate whether this mechanism can be inferred from observations of young clusters. To address this question, we have computed smoothed particle hydrodynamics simulations of the initial formation and evolution of a dynamically young star cluster through cold (subvirial) collapse, starting with an ellipsoidal, turbulently seeded distribution of gas, and forming sink particles representing (proto)stars. While the initial density distributions of the clouds do not have large initial mass concentrations, gravitational focusing due to the global morphology leads to cluster formation. We use the resulting structures to extract observable morphological and kinematic signatures for the case of subvirial collapse. We find that the signatures of the initial conditions can be erased rapidly as the gas and stars collapse, suggesting that kinematic observations need to be made early in cluster formation and/or at larger scales, away from the growing cluster core. Our results emphasize that a dynamically young system is inherently evolving on short timescales, so that it can be highly misleading to use current-epoch conditions to study aspects such as star formation rates as a function of local density. Our simulations serve as a starting point for further studies of collapse including other factors such as magnetic fields and stellar feedback.
The Hubble Frontier Fields (HFF) are six clusters of galaxies, all showing indications of recent mergers, which have recently been observed for lensed images. As such they are the natural laboratories to study the merging history of galaxy clusters. In this work, we explore the 2D power spectrum of the mass distribution $P_{\rm M}(k)$ as a measure of substructure. We compare $P_{\rm M}(k)$ of these clusters (obtained using strong gravitational lensing) to that of $\Lambda$CDM simulated clusters of similar mass. In order to compute lensing $P_{\rm M}(k)$, we produced free-form lensing mass reconstructions of HFF clusters, without any light traces mass (LTM) assumption. Moreover, the inferred power at small scales tends to be larger if (i)~the cluster is at lower redshift, and/or (ii)~there are deeper observations and hence more lensed images. In contrast, lens reconstructions assuming LTM show higher power at small scales even with fewer lensed images; it appears the small scale power in the LTM reconstructions is dominated by light information, rather than the lensing data. The average lensing derived $P_{\rm M}(k)$ shows lower power at small scales as compared to that of simulated clusters at redshift zero, both dark-matter only and hydrodynamical. The possible reasons are: (i)~the available strong lensing data are limited in their effective spatial resolution on the mass distribution, (ii)~HFF clusters have yet to build the small scale power they would have at $z\sim 0$, or (iii)~simulations are somehow overestimating the small scale power.
We present an optimized Floyd-Warshall (Floyd-Warshall) algorithm that computes the All-pairs shortest path (APSP) for GPU accelerated clusters. The Floyd-Warshall algorithm due to its structural similarities to matrix-multiplication is well suited for highly parallel GPU architectures. To achieve high parallel efficiency, we address two key algorithmic challenges: reducing high communication overhead and addressing limited GPU memory. To reduce high communication costs, we redesign the parallel (a) to expose more parallelism, (b) aggressively overlap communication and computation with pipelined and asynchronous scheduling of operations, and (c) tailored MPI-collective. To cope with limited GPU memory, we employ an offload model, where the data resides on the host and is transferred to GPU on-demand. The proposed optimizations are supported with detailed performance models for tuning. Our optimized parallel Floyd-Warshall implementation is up to 5x faster than a strong baseline and achieves 8.1 PetaFLOPS/sec on 256~nodes of the Summit supercomputer at Oak Ridge National Laboratory. This performance represents 70% of the theoretical peak and 80% parallel efficiency. The offload algorithm can handle 2.5x larger graphs with a 20% increase in overall running time.
We use a Mg + metal to extend the size regime of water clusters to extrapolate to the bulk limit of the Vertical Detachment Energy (VDE) of the solvated electron to >3,200, a value between one to over two orders of magnitude for clusters previously measured experimentally or computed theoretically. We relate the VDE to the energy difference between the Mg + (H 2 O) $n$ and Mg 2+ (H 2 O) $n$ systems and the metal’s second ionization potential. The extrapolated bulk VDEs of the localized surface electron, which moves away from the metal as n increases, are 1.89 ± 0.01 eV for semiempirical ($n$ ~ 3,200; PM6-D 3 H 4 ) and 1.73 ± 0.03 eV ($n$ ~ 150; HF), 1.83 ± 0.02 eV ($n$ ~ 150; MP2) for ab-initio, in excellent agreement with the 1.6 – 1.8 eV range of experimental results. In conclusion, the VDEs converge from above (larger values) to the bulk limit, in a manner qualitatively different than previous studies and experiments, a fact justifying the “back door” approach to the solvated electron.
Hot carriers generated from the decay of plasmon excitation can be harvested to drive a wide range of physical or chemical processes. However, their generation efficiency is limited by the concomitant phonon-induced relaxation processes by which the energy in excited carriers is transformed into heat. However, simulations of dynamics of nanoscale clusters are challenging due to the computational complexity involved. Here, in this paper, we adopt our newly developed Trajectory Surface Hopping (TSH) nonadiabatic molecular dynamics algorithm to simulate plasmon relaxation in Au 20 clusters, taking the atomistic details into account. The electronic properties are treated within the Linear Response Time-Dependent Tight-binding Density Functional Theory (LR-TDDFTB) framework. The relaxation of plasmon due to coupling to phonon modes in Au 20 beyond the Born–Oppenheimer approximation is described by the TSH algorithm. The numerically efficient LR-TDDFTB method allows us to address a dense manifold of excited states to ensure the inclusion of plasmon excitation. Starting from the photoexcited plasmon states in Au 20 cluster, we find that the time constant for relaxation from plasmon excited states to the lowest excited states is about 2.7 ps, mainly resulting from a stepwise decay process caused by low-frequency phonons of the Au 20 cluster. Furthermore, our simulations show that the lifetime of the phonon-induced plasmon dephasing process is ~10.4 fs and that such a swift process can be attributed to the strong nonadiabatic effect in small clusters. Our simulations demonstrate a detailed description of the dynamic processes in nanoclusters, including plasmon excitation, hot carrier generation from plasmon excitation dephasing, and the subsequent phonon-induced relaxation process.
Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.
Photoelectron spectra of the Mn{sub n}{sup −} anion clusters (n = 2–16) are obtained by anion photoelectron spectroscopy. The electronic and geometrical structures of the anions are computed using density functional theory with generalized gradient approximation and a basis set of triple-ζ quality. The electronic and geometrical structures of the neutral Mn{sub n} clusters have also been computed to estimate the adiabatic electron affinities. The average absolute difference between the computed and experimental vertical detachment energies of an extra electron is about 0.2 eV. Beginning with n = 6, all lowest total energy states of the Mn{sub n}{sup −} anions are ferrimagnetic with the spin multiplicities which do not exceed 8. The computed ionization energies of the neutral Mn{sub n} clusters are in good agreement with previously obtained experimental data. According to the results of our computations, the binding energies of Mn atoms are nearly independent on the cluster charge for n > 6 and possess prominent peaks at Mn{sub 13} and Mn{sub 13}{sup −} in the neutral and anionic series, respectively. The density of states obtained from the results of our computations for the Mn{sub n}{sup −} anion clusters show the metallic character of the anion electronic structures.
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain framework for designing parallel hierarchical agglomerative clustering (HAC) algorithms, and using the framework we obtain novel parallel algorithms for the complete linkage, average linkage, and Ward's linkage criteria. Compared to most previous parallel HAC algorithms, which require quadratic memory, our new algorithms require only linear memory, and are scalable to large data sets. ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required when finding nearest neighbors of clusters, and a caching optimization that stores a subset of previously computed distances, which are likely to be reused. Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.
In the context of high-accuracy computational thermochemistry, the valence coupled cluster with all singles and doubles (CCSD) correlation component of molecular atomization energies presents the most severe basis set convergence problem, followed by the (T) component. In the present paper, we make a detailed comparison, for an expanded version of the W4-11 thermochemistry benchmark, between, on the one hand, orbital-based CCSD/AV{5,6}Z + d and CCSD/ACV{5,6}Z extrapolation, and on the other hand CCSD-F12b calculations with cc-pVQZ-F12 and cc-pV5Z-F12 basis sets. This latter basis set, now available for H–He, B–Ne, and Al–Ar, is shown to be very close to the basis set limit. Apparent differences (which can reach 0.35 kcal/mol for systems like CCl{sub 4}) between orbital-based and CCSD-F12b basis set limits disappear if basis sets with additional radial flexibility, such as ACV{5,6}Z, are used for the orbital calculation. Counterpoise calculations reveal that, while total atomization energies with V5Z-F12 basis sets are nearly free of BSSE, orbital calculations have significant BSSE even with AV(6 + d)Z basis sets, leading to non-negligible differences between raw and counterpoise-corrected extrapolated limits. This latter problem is greatly reduced by switching to ACV{5,6}Z core-valence basis sets, or simply adding an additional zeta to just the valence orbitals. Previous reports that all-electron approaches like HEAT (high-accuracy extrapolated ab-initio thermochemistry) lead to different CCSD(T) limits than “valence limit + CV correction” approaches like Feller-Peterson-Dixon and Weizmann-4 (W4) theory can be rationalized in terms of the greater radial flexibility of core-valence basis sets. For (T) corrections, conventional CCSD(T)/AV{Q,5}Z + d calculations are found to be superior to scaled or extrapolated CCSD(T)-F12b calculations of similar cost. For a W4-F12 protocol, we recommend obtaining the Hartree-Fock and valence CCSD components from CCSD-F12b/cc-pV{Q,5}Z-F12 calculations, but the (T) component from conventional CCSD(T)/aug’-cc-pV{Q,5}Z + d calculations using Schwenke’s extrapolation; post-CCSD(T), core-valence, and relativistic corrections are to be obtained as in the original W4 theory. W4-F12 is found to agree slightly better than W4 with ATcT (active thermochemical tables) data, at a substantial saving in computation time and especially I/O overhead. A W4-F12 calculation on benzene is presented as a proof of concept.
We present cosmological constraints from the Planck PSZ2 cosmological cluster sample, using weak-lensing shear profiles from Dark Energy Survey (DES) data and X-ray observations from the Chandra telescope for the mass calibration. We compute hydrostatic mass estimates for all clusters in the PSZ2 sample with a scaling relation between their Sunyaev-Zeldovich signal and X-ray derived hydrostatic mass, calibrated with the Chandra data. We introduce a method to correct these masses with a hydrostatic mass bias using shear profiles from wide-field galaxy surveys. We simultaneously fit the number counts of the PSZ2 sample and the mass calibration with the DES data, finding $Ω_\text{m}=0.312^{+0.018}_{-0.024}$, $σ_8=0.777\pm 0.024$, $S_8\equiv σ_8 \sqrt{Ω_\text{m} / 0.3}=0.791^{+0.023}_{-0.021}$, and $(1-b)=0.844^{+0.055}_{-0.062}$ for our baseline analysis when combined with BAO data. When considering a hydrostatic mass bias evolving with mass, we find $Ω_\text{m}=0.353^{+0.025}_{-0.031}$, $σ_8=0.751\pm 0.023$, and $S_8=0.814^{+0.019}_{-0.020}$. We verify the robustness of our results by exploring a variety of analysis settings, with a particular focus on the definition of the halo centre used for the extraction of shear profiles. We compare our results with a number of other analyses, in particular two recent analyses of cluster samples obtained from SPT and eROSITA data that share the same mass calibration data set. We find that our results are in overall agreement with most late-time probes, in very mild tension with CMB results (1.6$σ$), and in significant tension with results from eROSITA clusters (2.9$σ$). We confirm that our mass calibration is consistent with the eROSITA analysis by comparing masses for clusters present in both Planck and eROSITA samples, eliminating it as a potential cause of tension.
The oxidation power of permanganates (MnO4–) is known to be strongly dependent on pH values, and is greatly enhanced in acidic solutions, in which, for example, MnO4– can even oxidize Cl– ions to produce Cl2 molecules. Although such dependence has been ascribed due to the different reduced states of Mn affordable in different pH media, a molecular level understanding and characterization of initial redox pair complexes available in different pH solutions is very limited. Herein, we report a comparative study of [MnO4]– and [MnO4•Sol]– (Sol = H2O, KCl, and HCl) anion clusters by negative ion photoelectron spectroscopy (NIPES) and theoretical computations to probe chemical bonding and electronic structures of [MnO4•Sol]– clusters, aimed to obtain a microscopic understanding of how MnO4– interacts with surrounding molecules. Our study shows that H2O behaves as a solvent molecule, KCl is a spectator bound by pure electrostatic interactions, both of which do not influence the MnO4– identity in their respective clusters. In contrast, in [MnO4•HCl]–, the proton is found to interact with both MnO4– and Cl– with appreciable covalent characters, and the frontier MOs of the cluster are comprised of contributions from both MnO4– and Cl– moieties. Therefore the proton serves as a chemical bridge, bringing two negatively charged redox species together to form an intimate redox pair. By adding more H+ to MnO4–, the oxygen atom can be taken away in the form of a water molecule, leaving MnO4– as an electron deficient MnO3+ species, which can subsequently oxidize Cl– ions.