Simulated Clusters Research Articles

DESI mock challenge: constructing DESI galaxy catalogues based on FastPM simulations

ABSTRACT Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large-scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a halo occupation distribution (HOD) model upon the FastPM simulations, such that the resulting galaxy clustering reproduces high-resolution N-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements – monopole and quadrupole – to a precision required by the DESI Year 1 emission line galaxy sample down to non-linear scales, i.e. $k\lt 0.5\, h\, \mathrm{Mpc}^{-1}$ or $s\gt 10\, \mathrm{Mpc}\, h^{-1}$. Furthermore, we compute covariance matrices based on the resulting FastPM galaxy clustering – monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate (e.g. power spectrum) on the covariance matrix of the Fourier counterpart (e.g. correlation function). We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between 2 and 10 per cent. Consequently, using the current pipeline we can precisely reproduce the clustering of N-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.

GALAXY CLUSTER MERGERS: THE USE OF THE COMPUTER MODELING

We show the possibilities of the methods of computer modeling of the galaxy cluster merger process with different initial parameters for the investigation of the features of the clusters after colliding. We considered the galaxy clusters as evolving objects including through collisions. Computer modeling of galaxy cluster mergers and comparing its results with observational data makes it possible to determine the evolutionary status of real clusters with complex internal structures. Within the study, we analyzed in detail a series of models of the galaxy clusters’ merger consequences by ZuHone under different initial conditions like mass ratio, initial collision parameter, or plasma coefficient β. The considered time intervals were from 0 to 4.8 billion years, from 0 to 6 billion years, and from 0 to 10 billion years, depending on the initial conditions. Based on the images of the simulated galaxy clusters, maps of the distribution of the total mass density and X-ray radiation were created and compared with observations. We showed a good perspective to use this catalog for studying galaxy clusters having compound inner structure.

Mock Observations: Formation and Evolution of Diffuse Light in Galaxy Groups and Clusters in the IllustrisTNG Simulations

In this paper, by analyzing mock images from the IllustrisTNG100-1 simulation, we examine the properties of diffuse light and compare them to those of central and satellite galaxies. Our findings suggest that the majority of the diffuse light originates from satellites. This claim is supported by the similarity between the age and metallicity distributions of the diffuse light and those of the satellites. Notably, the color distribution of the diffuse light gradually evolves to resemble that of the centrals at lower redshifts, suggesting a coevolution or passive process. The radial profiles of the diffuse light reveal distinct trends, with the inner regions displaying a relatively flat distribution and the outer regions showing a descending pattern. This finding suggests that the formation of the diffuse light is influenced by both major mergers and stellar tidal stripping. Moreover, strong correlations are found between the stellar mass of the diffuse light and the overall stellar mass of the satellites, as well as between the stellar mass of the diffuse light and the number of satellites within groups or clusters. These relationships can be described by power-law and logarithmic functions. Overall, the diffuse light components predominantly originate from satellites with intermediate ages and metallicities. These satellites typically fall within the stellar mass range and the color range −1 < [g − r]0.1 < 0. As the redshift decreases, the growth of the diffuse light is primarily influenced by the redder satellites, while the most massive and reddest satellites have minimal roles in its growth.

Observational bias and young massive cluster characterization − II. Can Gaia accurately observe young clusters and associations?

ABSTRACT Observations of clusters suffer from issues such as completeness, projection effects, resolving individual stars, and extinction. As such, how accurate measurements and conclusions are likely to be? Here, we take cluster simulations (Westerlund2- and Orion-type), synthetically observe them to obtain luminosities, accounting for extinction, and the inherent limits of Gaia, then place them within the real Gaia DR3 catalogue. We then attempt to rediscover the clusters at distances of between 500 and 4300 pc. We show the spatial and kinematic criteria that are best able to pick out the simulated clusters, maximizing completeness, and minimizing contamination. We then compare the properties of the ‘observed’ clusters with the original simulations. We looked at the degree of clustering, the identification of clusters and subclusters within the data sets, and whether the clusters are expanding or contracting. Even with a high level of incompleteness (e.g. $\lt 2~{{\ \rm per\ cent}}$ stellar members identified), similar qualitative conclusions tend to be reached compared to the original data set, but most quantitative conclusions are likely to be inaccurate. Accurate determination of the number, stellar membership, and kinematic properties of subclusters are the most problematic to correctly determine, particularly at larger distances due to the disappearance of cluster substructure as the data become more incomplete, but also at smaller distances where the misidentification of asterisms as true structure can be problematic. Unsurprisingly, we tend to obtain better quantitative agreement of properties for our more massive Westerlund2-type cluster. We also make optical style images of the clusters over our range of distances.

Catalogue of model star clusters in the Milky Way and M31 galaxies

ABSTRACT Detailed understanding of the formation and evolution of globular clusters (GCs) has been recently advanced through a combination of numerical simulations and analytical models. We employ a state-of-the-art model to create a comprehensive catalogue of simulated clusters in three Milky Way (MW) and three Andromeda (M31) analogue galaxies. Our catalogue aims to connect the chemical and kinematic properties of GCs to the assembly histories of their host galaxies. We apply the model to a selected sample of simulated galaxies that closely match the virial mass, circular velocity profile, and defining assembly events of the MW and M31. The resulting catalogue has been calibrated to successfully reproduce key characteristics of the observed GC systems, including total cluster mass, mass function, metallicity distribution, radial profile, and velocity dispersion. We find that clusters in M31 span a wider range of age and metallicity, relative to the MW, possibly due to M31’s recent major merger. Such a merger also heated up the in-situ GC population to higher orbital energy and introduced a large number of ex-situ clusters at large radii. Understanding the impacts of galaxy mergers and accretion on the GC populations is crucial for uncovering the galaxy assembly histories.

The localization of galaxy groups in close proximity to galaxy clusters using cosmic web nodes

ABSTRACT We investigate the efficacy of using the cosmic web nodes identified by the DisPerSE topological filament finder to systematically identify galaxy groups in the infall regions around massive clusters. The large random motions and infall velocities of galaxies in the regions around clusters complicate the detection and characterisation of substructures through normal group-finding algorithms. Yet understanding the co-location of galaxies within filaments and/or groups is a key part of understanding the role of environment on galaxy evolution, particularly in light of next-generation wide-field spectroscopic surveys. Here we use simulated massive clusters from TheThreeHundred collaboration and compare the derived group catalogues, (haloes with σv &amp;gt; 300 h−1 km s−1) with the critical points from DisPerSE, ran on haloes with more than 100 particles. We find that in 3D, 56 per cent of DisPerSE nodes are correctly identified as groups (purity) while 68 per cent of groups are identified as nodes (completeness). The fraction of matches increases with group mass and with distance from the host cluster centre. This rises to a completeness of 100 per cent for the most massive galaxy groups (M &amp;gt; 1014 M⊙) in 3D, or 63 per cent when considering the projected 2D galaxy distribution. When a perfect match occurs between a cosmic web node and a galaxy group, the DisPerSE node density (δ) serves as an estimate of the group’s mass, albeit with significant scatter. We conclude that the use of a cosmic filament finder shows promise as a useful and straightforward observational tool for disentangling substructure within the infall regions of massive clusters.

The three hundred project: thermodynamical properties, shocks, and gas dynamics in simulated galaxy cluster filaments and their surroundings

ABSTRACT Using cosmological simulations of galaxy cluster regions from The Three Hundred project, we study the nature of gas in filaments feeding massive clusters. By stacking the diffuse material of filaments throughout the cluster sample, we measure average gas properties such as density, temperature, pressure, entropy and Mach number and construct one-dimensional profiles for a sample of larger, radially oriented filaments to determine their characteristic features as cosmological objects. Despite the similarity in velocity space between the gas and dark matter accretion patterns on to filaments and their central clusters, we confirm some differences, especially concerning the more ordered radial velocity dispersion of dark matter around the cluster and the larger accretion velocity of gas relative to dark matter in filaments. We also study the distribution of shocked gas around filaments and galaxy clusters, showing that the surrounding shocks allow an efficient internal transport of material, suggesting a laminar infall. The stacked temperature profile of filaments is typically colder towards the spine, in line with the cosmological rarefaction of matter. Therefore, filaments are able to isolate their inner regions, maintaining lower gas temperatures and entropy. Finally, we study the evolution of the gas density–temperature phase diagram of our stacked filament, showing that filamentary gas does not behave fully adiabatically through time but it is subject to shocks during its evolution, establishing a characteristic z = 0, entropy-enhanced distribution at intermediate distances from the spine of about $1{-}2\, h^{-1}\,$ Mpc for a typical galaxy cluster in our sample.

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IntroductionSteady-state evoked potentials (ssEP) provide objective tools for studying brain function in different experimental conditions. Frequency components of brain response to repetitive stimuli have been analyzed using Tcirc2 statistic; however, Tcirc2 statistic is limited to comparisons between two means. Here, we present a generalized version of Tcirc2 statistic which enables us to compare multiple means of Fourier estimates corresponding to multiple conditions within participant(s) or multiple groups of participants. MethodsFrequency components of brain response are extracted from ssEP data using Fourier transform. Discrete Fourier measurements at frequency of interest are represented on the complex plane for statistical analyses. We present a new statistic called Fcirc statistic to compare three or more clusters of Fourier estimates whether they have equal or unequal variances or/and numbers of samples. Fcirc statistic derives from Welch’s test but for multiple comparisons. ResultsWe demonstrate the validity of Fcirc statistic using simulated and empirical clusters of Fourier estimates with equal and unequal variances and numbers of samples. Type-I error remains 0.05 for all the conditions. Furthermore, we illustrate that the probability of achieving a significant difference among multiple means when the true means are unequal depends on the total length of ssEP data but is independent of the duration chosen for performing Fourier transform on a fixed length of ssEP data. ConclusionFcirc statistic is useful for multiple intra- and inter-participant and group comparisons of brain response at any frequency component extracted from ssEP data whether the group means have equal or unequal variances.

A persistent excess of galaxy-galaxy strong lensing observed in galaxy clusters

Context. Previous studies have revealed that the estimated probability of galaxy-galaxy strong lensing in observed galaxy clusters exceeds the expectations from the Λ cold dark matter cosmological model by one order of magnitude. Aims. We aim to understand the origin of this excess by analyzing a larger set of simulated galaxy clusters, and investigating how the theoretical expectations vary under different adopted prescriptions and numerical implementations of star formation and feedback in simulations. Methods. We performed a ray-tracing analysis of 324 galaxy clusters from the THREE HUNDRED project, comparing the GADGET-X and GIZMO-SIMBA runs. These simulations, which start from the same initial conditions, were performed with different implementations of hydrodynamics and galaxy formation models tailored to match different observational properties of the intracluster medium and cluster galaxies. Results. We find that galaxies in the GIZMO-SIMBA simulations develop denser stellar cores than their GADGET-X counterparts. Consequently, their probability for galaxy-galaxy strong lensing is higher by a factor of ∼3. This increment is still insufficient to fill the gap with observations as a discrepancy by a factor ∼4 still persists. In addition, we find that several simulated galaxies have Einstein radii that are too large compared to observations. Conclusions. We conclude that a persistent excess of galaxy-galaxy strong lensing exists in observed galaxy clusters. The origin of this discrepancy with theoretical predictions is still unexplained in the framework of the cosmological hydrodynamical simulations. This might signal a hitherto unknown issue with either the simulation methods or our assumptions regarding the standard cosmological model.

Exploring the origins of mega radio halos

We present a first attempt to investigate the origin of radio-emitting electrons in the newly discovered class of mega radio halos in clusters of galaxies. We study the evolution of relativistic electrons accreted by the external regions of a simulated cluster of galaxies at high resolution, including the effect of radiative losses and turbulent reacceleration acting on relativistic electrons. We conclude that turbulent reacceleration induced by structure formation, if sufficiently prolonged, has the potential to produce a large reservoir of radio-emitting electrons in the large regions illuminated by mega radio halos observed by LOFAR.

Unexpected enhancement of sulfuric acid-driven new particle formation by alcoholic amines: The role of ion-induced nucleation

New particle formation (NPF) contributes more than half of the global aerosol. Diethanolamine (DEA) and methyldiethanolamine (MDEA) are the most common amines used to remove CO2 and H2S, which are lost to the atmosphere from CO2 chemical absorbers, livestock and consumer products and are involved in sulfuric acid (SA)-driven NPF. Ion-induced nucleation (IIN) is an important nucleation pathway for NPF. We investigated the role of IIN on DEA and MDEA enhancing SA-driven NPF using density functional method (DFT), molecular dynamics (MD) simulation and atmospheric cluster dynamics code (ACDC). The effects of SO42−, H3O+, NH4+, HSO4−, NO3−, ammonia, methylamine, dimethylamine, trimethylamine and water (W) on the nucleation of SA-DEA were further investigated. The enhancement ability of DEA is greater than that of dimethylamine (DMA) and MDEA. Participation in SA-based NPF is a removal pathway for DEA and MDEA. DEA-SA clusters are generated that not only aggregate DEA and SA molecules, but also increase further growth of atmospheric ions. The very low Gibbs formation free energy highlights the importance of ion-induced nucleation for SA-based NPF. The order of the ability of common atmospheric ions to increase the (SA)(DEA) cluster nucleation is SO42− > H3O+ > NH4+ > HSO4− > NO3−. The addition of 20 water molecules increases the (SA)(DEA)9 cluster from 1.882 nm to 2.053 nm, promoting SA-based NPF. The atmospheric ions accelerate the aggregation rate of the (SA)5(DEA)5 cluster within 15 ns?

Painting baryons on to N-body simulations of galaxy clusters with image-to-image deep learning

ABSTRACT Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can bypass this challenge, but it requires large suites of simulations spanning a range of cosmologies and models for directly observable quantities. In this work, we devise a U-net – an image-to-image machine learning algorithm – to ‘paint’ the illustristng model of baryons on to dark matter-only (DMO) simulations of galaxy clusters. Using 761 galaxy clusters with M200c ≳ 1014 M⊙ from the TNG300 simulation at z &amp;lt; 1, we train the algorithm to read in maps of projected dark matter mass and output maps of projected gas density, temperature, and X-ray flux. Despite being trained on individual images, the model reproduces the true scaling relation and scatter for the MDM–LX, as well as the distribution functions of the cluster X-ray luminosity and gas mass. For just one decade in cluster mass, the model reproduces three orders of magnitude in LX. The model is biased slightly high when using dark matter maps from the DMO simulation. The model performs well on inputs from TNG300-2, whose mass resolution is eight times coarser; further degrading the resolution biases the predicted luminosity function high. We conclude that U-net-based baryon painting is a promising technique to build large simulated cluster catalogues, which can be used to improve cluster cosmology by combining existing full-physics and large N-body simulations.

What multiple images say about the large-scale mass maps of galaxy clusters

ABSTRACT All lens modelling methods, simply parametrized, hybrid, and free-form, use assumptions to reconstruct galaxy clusters with multiply imaged sources, though the nature of these assumptions (priors) can differ considerably between methods. This raises an important question in strong lens modelling: how much information about the mass model comes from the lensed images themselves, and how much is a consequence of model priors. One way to assess the relative contributions of the lensing data versus model priors is to estimate global lens properties through images alone, without any prior assumptions about the mass distribution. This is our approach. We use 200 mock cluster lenses, half of which have substructures, which vary from clumpy and compact to smooth and extended, a simulated cluster Ares, and real clusters Abell 1689 and RX J1347.5−1145 to show that the centre, ellipticity, and position angle can be estimated quite well, and nearly perfectly for weakly substructured clusters, implying that the recovery of these properties is largely driven by the images, not priors. However, the correlation between the true and image-estimated amount of substructure has a lot of scatter, suggesting that multiple images do not uniquely constrain substructure. Therefore, in general, lens model priors have a stronger effect on smaller scales. Our analysis partly explains why reconstructions using different methodologies can produce qualitatively different mass maps on substructure scales. Our analysis is not meant to aide or replace lens inversion methods, but only to investigate what cluster properties are constrained with multiple images.

Significance mode analysis (SigMA) for hierarchical structures

We present a new clustering method, significance mode analysis (SigMA), for extracting co-spatial and co-moving stellar populations from large-scale surveys such as ESA Gaia. The method studies the topological properties of the density field in the multidimensional phase space. We validated SigMA on simulated clusters and find that it outperforms competing methods, especially in cases where many clusters are closely spaced. We applied the new method to Gaia DR3 data of the closest OB association to Earth, Scorpio-Centaurus (Sco-Cen), and find more than 13 000 co-moving young objects, about 19% of which have a substellar mass. SigMA finds 37 co-moving clusters in Sco-Cen. These clusters are independently validated by their narrow Hertzsprung-Russell diagram sequences and, to a certain extent, by their association with massive stars too bright for Gaia, and are hence unknown to SigMA. We compared our results with similar recent work and find that the SigMA algorithm recovers richer populations, is able to distinguish clusters with velocity differences down to about 0.5 km s−1, and reaches cluster volume densities as low as 0.01 sources pc−3. The 3D distribution of these 37 coeval clusters implies a larger extent and volume for the Sco-Cen OB association than typically assumed in the literature. Additionally, we find the association more actively star-forming and dynamically complex than previously thought. We confirm that the star-forming molecular clouds in the Sco-Cen region, namely, Ophiuchus, L134/L183, Pipe Nebula, Corona Australis, Lupus, and Chamaeleon, are part of the Sco-Cen association. The application of SigMA to Sco-Cen demonstrates that advanced machine learning tools applied to the superb Gaia data allows an accurate census of the young populations to be constructed, which in turn allows us to quantify their dynamics and recreate the recent star formation history of the local Milky Way.

Lensing in the Blue. II. Estimating the Sensitivity of Stratospheric Balloons to Weak Gravitational Lensing

The Superpressure Balloon-borne Imaging Telescope (SuperBIT) is a diffraction-limited, wide-field, 0.5 m, near-infrared to near-ultraviolet observatory designed to exploit the stratosphere’s space-like conditions. SuperBIT’s 2023 science flight will deliver deep, blue imaging of galaxy clusters for gravitational lensing analysis. In preparation, we have developed a weak-lensing measurement pipeline with modern algorithms for PSF characterization, shape measurement, and shear calibration. We validate our pipeline and forecast SuperBIT survey properties with simulated galaxy cluster observations in SuperBIT’s near-UV and blue bandpasses. We predict imaging depth, galaxy number (source) density, and redshift distribution for observations in SuperBIT’s three bluest filters; the effect of lensing sample selections is also considered. We find that, in three hours of on-sky integration, SuperBIT can attain a depth of b = 26 mag and a total source density exceeding 40 galaxies per square arcminute. Even with the application of lensing-analysis catalog selections, we find b-band source densities between 25 and 30 galaxies per square arcminute with a median redshift of z = 1.1. Our analysis confirms SuperBIT’s capability for weak gravitational lensing measurements in the blue.

Chance-Constrained Dispatching of Integrated Energy Systems Considering Source–Load Uncertainty and Photovoltaic Absorption

Because of their renewable and non-polluting characteristics in power production, distributed photovoltaics have been developed, but they have also been criticized for the volatility of their output power. In this paper, an integrated energy system optimal dispatching model is proposed to improve the local absorption capacity of distributed photovoltaics. First, an integrated energy system consisting of electricity, heat, cooling, gas, and hydrogen is modeled, and a mathematical model of the system is constructed. After that, the uncertainty of distributed photovoltaic power and load demand is modeled, and a typical scenario data set is generated through Monte Carlo simulation and K-means clustering. Finally, an optimal dispatching model of the integrated energy system is constructed to minimize the daily operating cost, including energy consumption, equipment operation and maintenance, and curtailment penalty costs, as the optimization objective. In the objective, a segmented curtailment penalty cost is Introduced. Moreover, this paper presents a chance constraint to convert the optimization problem containing uncertain variables into a mixed integer linear programming problem, which can reduce the difficulty of the solution. The case shows that the proposed optimal dispatching model can improve the ability of photovoltaics to be accommodated locally. At the same time, due to the introduction of the segmented curtailment penalty cost, the system improves the absorption of distributed photovoltaic generation at peak tariff intervals and enhances the economy of system operation.

Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy

Rational design and advanced energy management considering multiple uncertainties are imperative for the superior integrated energy system (IES). This work proposed a novel two-layer stochastic multiple scenario optimization framework for the collaborative optimization of capacity and operation of IES. To improve the accuracy of probability density estimation, the improved kernel density estimation (KDE) was employed to obtain the probability density distributions of wind speed, sunlight and multi-demands. Then the scenario sets were generated by Latin hypercube sampling (LHS) simulation and self-organization map (SOM) clustering. To decouple the output, efficiency and part load factor of devices during operation, the following optimal contribution rate (FOCR) strategy was proposed, which could actively adjust the output ratio of energy conversion devices to realize the flexible energy supply. The developed optimization methodology was used for a case study in an office building. The results indicate that the improved KDE for probability distribution estimation of uncertainties achieves the accuracy percentage enhancement, for the average value, of 37.1% and 49.6% for root-mean-square error (RMSE), respectively, compared with the conventional KDE and parametric model. Considering energy saving, economy and environmental indicators, the performance of the FOCR strategy is superior to those of the three traditional strategies.

Calculation of the recombination radii between various point defects and defect clusters in nickel via kinetic Activation Relaxation Technique

The recombination radii between various point defects and defect clusters are essential input parameters for mathematical and computational models of radiation-induced damage evolution. In this work, such recombination radii is calculated by the kinetic Activation Relaxation Technique (k-ART)—an off-lattice, self-learning kinetic Monte Carlo algorithm—in conjunction with molecular dynamics simulations. We found vacancy clusters exhibit larger average recombination radii with interstitials than single vacancies, reaching as large as 2.2a0 (a0 is the lattice parameter of nickel). Such recombination enhancement can be attributed to the intensified distortion near cluster surfaces. Unlike vacancy clusters, free surfaces feature smaller recombination radii than single vacancies. Their recombination radii are correlated with the angle between surface normal and <110> directions. Finally, the recombination radii of interstitial clusters are larger than vacancy clusters and strongly depends on the cluster configurations. Overall, the recombination radii of most internal defect clusters range from ∼2a0 to ∼2.4a0. Because previous research has found that recombination radii affect the density-size distribution of point defect clusters but not the fraction of a particular defect, our analysis suggests that given an accurate initial estimate of cluster density-size distributions, choosing a uniform recombination radius for different clusters in object kinetic Monte Carlo simulations should be sufficient to predict subsequent microstructural evolution.

Hybrid Tasmanian Devil and Improved Simulated Annealing-Based Clustering Algorithm for Improving Network Longevity in Wireless Sensor Networks (WSNs)

Hybrid Tasmanian Devil and Improved Simulated Annealing-Based Clustering Algorithm for Improving Network Longevity in Wireless Sensor Networks (WSNs)

A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions

Two of the most critical issues encountered in the day-ahead scheduling of regional integrated energy systems are the uncertainty of renewable energy resources and complexity of load demand. Furthermore, varying operating conditions also pose challenges for economical day-ahead scheduling. This paper proposes a scenario-based day-ahead scheduling approach for regional integrated energy systems to minimize operating costs by mining historical data. A hybrid dynamic energy hub model with variable efficiency that integrates an extreme gradient boosting (XGBoost) algorithm and analytical formulation is proposed. We developed a scenario-based scheduling optimization model in which climate data and load data are predicted using XGBoost and the probability distributions of their predicted errors are estimated using a Gaussian mixture model. Monte Carlo simulation and K-means clustering were used to generate and reduce scenarios and a success-history-based adaptive differential evolution algorithm was adopted to search for optimal solutions for day-ahead scheduling. Furthermore, a weighted average electricity purchasing strategy was adopted to address uncertainty and further improve operating economy by adjusting the output of gas turbines and electricity purchasing for actual scheduling. Case studies were conducted to verify that the proposed approach can reduce daily operating costs and enhance the operating economy of regional integrated energy systems.