Integrated cost risk and cluster analyses
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The fuzzy partitioned clustering method was applied to predict tropopause height only using microwave information with an eye towards using it on real data under cloudy conditions. In the second stage stratified by tropopause regression temperature retrievals included using only the three or four microwave channels for each 40 mb range. The first step in the experiment is the fuzzy partitioned clustering of the microwave brightness temperatures. This method is a combination of standard hard clustering and discriminant analysis. The fuzzy partitioned clustering uses all the generated probabilities of membership of each pattern vector in any of the given clusters. These probabilities are generated by discriminant analysis to locate the correct cluster. The ultimate goal of standard discriminant analysis is to provide the unique (correct) cluster to which the pattern vector belongs. It was only the maximum of all the generated probabilities. The method uses all the probabilities and weight the regressions generated within each cluster. These regression formulas predict the tropopause height from the microwave brightness temperatures. In the second step the microwave regression temperature retrievals are stratified by tropopause height every 40 mb. The control experiment is defined, the data are stratified by land/ocean, summer/winter, and latitude bands.
A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.
ABSTRACT We present a novel simulation-based cosmological analysis of galaxy–galaxy lensing and galaxy redshift-space clustering. Compared to analysis methods based on perturbation theory, our simulation-based approach allows us to probe a much wider range of scales, $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, including highly non-linear scales, and marginalizes over astrophysical effects such as assembly bias. We apply this framework to data from the Baryon Oscillation Spectroscopic Survey LOWZ sample cross-correlated with state-of-the-art gravitational lensing catalogues from the Kilo Degree Survey and the Dark Energy Survey. We show that gravitational lensing and redshift-space clustering when analysed over a large range of scales place tight constraints on the growth-of-structure parameter $S_8 = \sigma _8 \sqrt{\Omega _{\rm m} / 0.3}$. Overall, we infer S8 = 0.792 ± 0.022 when analysing the combination of galaxy–galaxy lensing and projected galaxy clustering and S8 = 0.771 ± 0.027 for galaxy redshift-space clustering. These findings highlight the potential constraining power of full-scale studies over studies analysing only large scales and also showcase the benefits of analysing multiple large-scale structure surveys jointly. Our inferred values for S8 fall below the value inferred from the CMB, S8 = 0.834 ± 0.016. While this difference is not statistically significant by itself, our results mirror other findings in the literature whereby low-redshift large-scale structure probes infer lower values for S8 than the CMB, the so-called S8-tension.
We present a joint analysis of galaxy clustering and galaxy–galaxy lensing measurements from BOSS galaxies using a simulation-based emulation method combined with a halo occupation distribution model. Our emulators are constructed with the Aemulus ν simulations, a suite of wνCDM N-body simulations with massive neutrinos as independent particle species. We combine small-scale analysis of clustering from 0.1 to 60.2 h −1 Mpc and lensing from 1.7 to 60.2 h −1 Mpc to perform cosmological constraints. We split the BOSS galaxies into three redshift bins to measure their clustering and employ galaxies from Dark Energy Camera Legacy Survey and Hyper Suprime-Cam as source galaxies to measure lensing separately. We find that the addition of lensing significantly improves the constraining power on $S_8 = σ_8(Ω_m/0.3)^{0.5}$, with a weak improvement for fσ 8 . Our results of fσ 8 indicate tensions of around 1σ−4σ below the results of the cosmic microwave background observations of Planck. For S 8 , our results are also lower than Planck, and the tension can be mitigated when considering possible systematics in lensing measurement. As a by-product, our analysis prefers a nonzero neutrino mass but without strong significance, with the constraining power dominated by the clustering. Given the accuracy and precision of our model and the observational data, it is anticipated that larger and higher-quality spectroscopic data sets will improve the constraints on this fundamental property in the near future.
As the market for cloud computing continues to grow, an increasing number of users are deploying applications as microservices. The shift introduces unique challenges in identifying and addressing performance issues, particularly within large and complex infrastructures. To address this challenge, we propose a methodology that unveils temporal performance deviations in microservices by clustering containers based on their performance characteristics at different time intervals. Showcasing our methodology on the Alibaba dataset, we found both stable and dynamic performance patterns, providing a valuable tool for enhancing overall performance and reliability in modern application landscapes.
The precise estimation of the mass of galaxy clusters is a major issue for cosmology. Large galaxy cluster surveys rely on scaling laws that relate cluster observables to their masses. From the high-resolution observations of ~45 galaxy clusters with the NIKA2 and XMM-Newton instruments, the NIKA2 Sunyaev-Zel’dovich Large Program should provide an accurate scaling relation between the thermal Sunyaev-Zel’dovich effect and the hydrostatic mass. In this paper we present an exhaustive analysis of the hydrostatic mass of the well-known galaxy cluster CL J1226.9+3332, the highest-redshift cluster in the NIKA2 Sunyaev-Zel’dovich Large Program at z = 0.89. We combined the NIKA2 observations with thermal Sunyaev-Zel’dovich data from the NIKA, Bolocam, and MUSTANG instruments and XMM-Newton X-ray observations, and tested the impact of the systematic effects on the mass reconstruction. We conclude that slight differences in the shape of the mass profile can be crucial when defining the integrated mass at R 500 , which demonstrates the importance of the modelling in the mass determination. We prove the robustness of our hydrostatic mass estimates by showing the agreement with all the results found in the literature. Another key factor for cosmology is the bias of the masses estimated assuming the hydrostatic equilibrium hypothesis. Based on the lensing convergence maps from the Cluster Lensing And Supernova survey with Hubble (CLASH) data, we obtain the lensing mass estimate for CL J1226.9+3332. From this we are able to measure the hydrostatic-to-lensing mass bias for this cluster, which spans from 1–b HSE/lens ~0.7 to 1, presenting the impact of data sets and mass reconstruction models on the bias.
We analyze the cooling and feedback properties of 48 galaxy clusters at redshifts 0.4 < z < 1.3 selected from the South Pole Telescope (SPT) catalogs to evolve like the progenitors of massive and well-studied systems at z ~ 0. We estimate the radio power at the brightest cluster galaxy (BCG) location of each cluster from an analysis of Australia Telescope Compact Array data. Assuming that the scaling relation between the radio power and active galactic nucleus (AGN) cavity power P cav observed at low redshift does not evolve with redshift, we use these measurements in order to estimate the expected AGN cavity power in the core of each system. We estimate the X-ray luminosity within the cooling radius L cool of each cluster from a joint analysis of the available Chandra X-ray and SPT Sunyaev–Zel'dovich (SZ) data. This allows us to characterize the redshift evolution of the P cav /L cool ratio. When combined with low-redshift results, these constraints enable investigations of the properties of the feedback–cooling cycle across 9 Gyr of cluster growth. We model the redshift evolution of this ratio measured for cool-core clusters by a log-normal distribution Log-N (α + βz, σ 2 ) and constrain the slope of the mean evolution to β = -0.05 ± 0.47. This analysis improves the constraints on the slope of this relation by a factor of two. We find no evidence of redshift evolution of the feedback–cooling equilibrium in these clusters, which suggests that the onset of radio-mode feedback took place at an early stage of cluster formation. High values of P cav /L cool are found at the BCG location of noncool-core clusters, which might suggest that the timescales of the AGN feedback cycle and the cool core–noncool core transition are different. This work demonstrates that the joint analysis of radio, SZ, and X-ray data solidifies the investigation of AGN feedback at high redshifts.
From a survey of ten U.S. Navy flight simulators a large number (N > 1,600 exposures) of self-reports of motion sickness symptomatology were obtained. Using these data, scoring algorithms were derived, which permit examination of groups of individuals that can be scored either for 1) their total sickness experience in a particular device; or, 2) according to three separable symptom clusters which emerged from a Factor Analysis. Scores from this total score are found to be proportional to other global motion sickness symptom checklist scores with which they correlate (r = 0.82). The factors that surfaced from the analysis include clusters of symptoms referable as nausea, oculomotor disturbances, and disorientation (N, 0, and D). The factor scores may have utility in differentiating the source of symptoms in different devices. The present chapter describes our experience with the use of both of these types of scores and illustrates their use with examples from flight simulators, space sickness and virtual environments.
Certain arrangements of local features in a scene tend to group together and to be seen as units. It is suggested that in some instances, this phenomenon might be interpretable as a process of cluster detection in a graph-structured space derived from the scene. This idea is illustrated using a class of scenes that contain only horizontal and vertical line segments.
This grant provided support for the reduction, analysis and interpretation of of hard X-ray (HXR, for short) observations of the cluster of galaxies RXJO658--5557 scheduled for the week of August 23, 2002 under the RXTE Cycle 7 program (PI Vahe Petrosian, Obs. ID 70165). The goal of the observation was to search for and characterize the shape of the HXR component beyond the well established thermal soft X-ray (SXR) component. Such hard components have been detected in several nearby clusters. distant cluster would provide information on the characteristics of this radiation at a different epoch in the evolution of the imiverse and shed light on its origin. We (Petrosian, 2001) have argued that thermal bremsstrahlung, as proposed earlier, cannot be the mechanism for the production of the HXRs and that the most likely mechanism is Compton upscattering of the cosmic microwave radiation by relativistic electrons which are known to be present in the clusters and be responsible for the observed radio emission. Based on this picture we estimated that this cluster, in spite of its relatively large distance, will have HXR signal comparable to the other nearby ones. The planned observation of a relatively The proposed RXTE observations were carried out and the data have been analyzed. We detect a hard X-ray tail in the spectrum of this cluster with a flux very nearly equal to our predicted value. This has strengthen the case for the Compton scattering model. We intend the data obtained via this observation to be a part of a larger data set. We have identified other clusters of galaxies (in archival RXTE and other instrument data sets) with sufficiently high quality data where we can search for and measure (or at least put meaningful limits) on the strength of the hard component. With these studies we expect to clarify the mechanism for acceleration of particles in the intercluster medium and provide guidance for future observations of this intriguing phenomenon by instrument on GLAST. The details of the nonthermal particle population has important implications for the theories of cluster formation, mergers and evolution. The results of this work were first presented at the High Energy Division meeting of the American astronomical Society at Mt. Tremblene, Canada (Petrosian et al. 2003). and in an invited review talk at the General Assembly of the International Astronomical Union at Sydney, Australia (Petrosian, 2003). A paper describe the observations, the data analysis and its implication is being prepared for publication in the Astrophysical Journal.
Redshifts are acquired for a complete sample of 351 Abell clusters with tenth-ranked galaxy magnitudes (m10) less than or equal to 16.5, including 115 entirely new cluster redshifts. Analysis of the spatial distribution of these clusters reveals no clustering on scales larger than 75/h Mpc. The correlation length is 20.0 (+/-4.3)/h Mpc, consistent with the results from other surveys. The frequency of voids with radii of order 60/h Mpc or less is consistent with the form and amplitude of the observed two-point correlation function. There is no significant difference between the clustering properties of clusters with RC = 0 and RC not less than 1. A percolation analysis yields 23 superclusters, 17 of which are new. The superclusters are not significantly elongated in the radial direction; large-scale peculiar motions are of order 1000 km/s or less.
We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set {σ 8 , H 0 , Ω m , w 0 , w a } using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the w 0 w a CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following 1σ frequentist confidence intervals for ΛCDM (w 0 w a CDM): σ 8 = 0.863 +0.048 -0.040 , H 0 = 68.96 +0.81 -0.80 km s -1 Mpc -1 , Ω m = 0.3034 ± 0.0110 (σ 8 = 0.782 +0.060 -0.036 , H 0 = 63.7 +4.2 -2.0 km s -1 Mpc -1 , Ω m = 0.378 +0.024 -0.047 , w 0 = -0.16 +0.10 -0.50 , w a = -3.0 +1.7 ), corresponding to 0.8σ, 0.3σ, 0.7σ (2.1σ, 4.1σ, 6.5σ, 6.3σ, 6.6σ) shifts between the maximum likelihood estimate and the Bayesian posterior mean for ΛCDM (w 0 w a CDM) respectively.
We report the investigation of merging galaxy clusters that exhibit radio relics is strengthening our understanding of the formation and evolution of galaxy clusters, the nature of dark matter, the intracluster medium, and astrophysical particle acceleration. Each merging cluster provides only a single view of the cluster formation process, and the variety of merging clusters is vast. Clusters hosting double radio relics are rare and extremely important because they allow tight constraints on the merger scenario. We present a weak-lensing and X-ray analysis of MACS J1752.0+4440 (z = 0.365) and ZWCL 1856.8+6616 (z = 0.304), two double radio relic clusters. Our weak-lensing mass estimates show that each cluster is a major merger with approximately 1:1 mass ratio. The total mass of MACS J1752.0+4440 (ZWCL 1856.8+6616) is M 200 =14.7 $_{-3.3}+{+3.8}$ x 10 14 M ⊙ M 200 = 2.4 $_{-0.7}^{+0.9}$ 10 14 M ⊙ ). We find that these two clusters have comparable features in their weak-lensing and gas distributions, even though the systems have vastly different total masses. From the likeness of the X-ray morphologies and the remarkable symmetry of the radio relics, we propose that both systems underwent nearly head-on collisions. However, revelations from the hot-gas features and our multiwavelength data analysis suggest that ZWCL 1856.8+6618 is likely at a later merger phase than MACS J1752.0+4440. We postulate that the SW radio relic in MACS J1752.0+4440 is a result of particle reacceleration.
Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.
Plasmaspheric plumes have been routinely observed by CLUSTER and IMAGE. The CLUSTER mission provides high time resolution four-point measurements of the plasmasphere near perigee. Total electron density profiles can be derived from the plasma frequency and/or from the spacecraft potential (note that the electron spectrometer is usually not operating inside the plasmasphere); ion velocity is also measured onboard these satellites (but ion density is not reliable because of instrumental limitations). The EUV imager onboard the IMAGE spacecraft provides global images of the plasmasphere with a spatial resolution of 0.1 RE every 10 minutes; such images acquired near apogee from high above the pole show the geometry of plasmaspheric plumes, their evolution and motion. We present coordinated observations for 3 plume events and compare CLUSTER in-situ data (panel A) with global images of the plasmasphere obtained from IMAGE (panel B), and with numerical simulations for the formation of plumes based on a model that includes the interchange instability mechanism (panel C). In particular, we study the geometry and the orientation of plasmaspheric plumes by using a four-point analysis method, the spatial gradient. We also compare several aspects of their motion as determined by different methods: (i) inner and outer plume boundary velocity calculated from time delays of this boundary observed by the wave experiment WHISPER on the four spacecraft, (ii) ion velocity derived from the ion spectrometer CIS onboard CLUSTER, (iii) drift velocity measured by the electron drift instrument ED1 onboard CLUSTER and (iv) global velocity determined from successive EUV images. These different techniques consistently indicate that plasmaspheric plumes rotate around the Earth, with their foot fully co-rotating, but with their tip rotating slower and moving farther out.
The development of the phase-imaging ion-cyclotron resonance (PI-ICR) technique for use in Penning trap mass spectrometry (PTMS) increased the speed and precision with which PTMS experiments can be carried out. In PI-ICR, data sets of the locations of individual ion hits on a detector are created showing how ions cluster together into spots according to their cyclotron frequency. Ideal data sets would consist of a single, 2D-spherical spot with no other noise, but in practice data sets typically contain multiple spots, non-spherical spots, or significant noise, all of which can make determining the locations of spot centers non-trivial. A method for assigning groups of ions to their respective spots and determining the spot centers is therefore essential for further improving precision and confidence in PI-ICR experiments. Here, we present the class of Gaussian mixture model (GMM) clustering algorithms as an optimal solution. We show that on simulated PI-ICR data, several types of GMM clustering algorithms perform better than other clustering algorithms over a variety of typical scenarios encountered in PI-ICR. The mass spectra of 163Gd, 163 mGd, 162 Tb, and 162 mTb measured using PI-ICR at the Canadian Penning trap mass spectrometer were checked using GMMs, producing results that were in close agreement with the previously published values.
SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.