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At least 361 records · Page 20

WFPC2 observations of the double cluster NGC 1850 in the Large Magellanic Cloud

Hubble Space Telescope-Wide Field/Planetary Camera-2 (HST-WFPC2) optical and ultraviolet imaging observations of the young double cluster NGC 1850 in the Large Magellanic Cloud (LMC) are presented. The main cluster, NGC 1850A, is a globular-like cluster and has an age of 50 +/- 10 Myr, while the subcluster, NGC 1850B, which is more loosely distributed, is very young at 4.3 +/- 0.9 Myr. Its young age is confirmed by the detection of a pre-main-sequence population of stars associated to it. The two clusters have considerably different IMF slopes, with the main cluster having a flat slope (f(m) proportional to m(exp -1.4 +/- 0.2)) and the young cluster a much steeper one (f(m) proportional to m(exp -2.6 +/- -0.1)). The LMC field star population displays a broad range of ages, from approximately 0.5 Gyr up to more than 4 Gyr.

Gilmozzi, R.↗

Electron scattering and the distances to cluster cooling flows

Cluster cooling flows have electron-scattering optical depths which are estimated to be about 0.01. Many of the cluster-dominant galaxies centered in cluster cooling flows have active nuclei, and about 1 percent of the luminosity from the active nuclei should appear as diffuse, scattered radiation. It is shown here that detection of this scattered radiation at radio wavelengths can provide a measure of the distance to the cluster that is independent of the Hubble constant. Observations of the scattered surface brightness profile can also be used to test for clumpiness in the gas, for variations in the central radio source on time scales less than 100,000 yr, and for the degree of beaming of radiation from the central source.

Wise, Michael W.↗

Uhuru observations of an X-ray burst at high galactic latitude centered on the X-ray globular cluster NGC 1851

An X-ray burst from a 0.5 x 10 deg region centered near NGC 1851 (MX 0513-40) was detected in observations made with the Uhuru X-ray observatory on September 19, 1972. The source was observed for 15 seconds with a peak rate of 0.5 that of the Crab (2-6 keV) and was not detected above 0.01 of the Crab in observations 4.5 minutes before or after this event. During the observation, the spectrum developed a low-energy cutoff. NGC 1851 is the second globular cluster identified as a source of X-ray bursts, which suggests that all X-ray bursts may originate in globular clusters, some of which may be optically obscured.

Forman, W.↗

Observational and Numerical Diagnostics of Galaxy Cluster Outer Regions

Aims. We present the analysis of a local (z = 0.04 - 0.2) sample of 31 galaxy clusters with the aim of measuring the density of the X-ray emitting gas in cluster outskirts. We compare our results with numerical simulations to set constraints on the azimuthal symmetry and gas clumping in the outer regions of galaxy clusters. Methods. We exploit the large field-of-view and low instrumental background of ROSAT/PSPC to trace the density of the intracluster gas out to the virial radius. We perform a stacking of the density profiles to detect a signal beyond r(sub 200) and measure the typical density and scatter in cluster outskirts. We also compute the azimuthal scatter of the profiles with respect to the mean value to look for deviations from spherical symmetry. Finally, we compare our average density and scatter profiles with the results of numerical simulations. Results. As opposed to several recent results, we observe a steepening of the density profiles beyond approximately 0.3r(sub 500). Comparing our density profiles with simulations, we find that non-radiative runs predict too steep density profiles, whereas runs including additional physics and/or gas clumping are in better agreement with the observed gas distribution. We note a systematic difference between cool-core and non-cool core clusters beyond approximately 0.3r(sub 200), which we explain by a different distribution of the gas in the two classes. Beyond approximately r(sub 500), galaxy clusters deviate significantly from spherical symmetry, with only little differences between relaxed and disturbed systems. We find good agreement between the observed and predicted scatter profiles, but only when the 1% densest clumps are filtered out in the simulations. Conclusions. The general trend of steepening density around the virial radius indicates that the shallow density profiles found in several recent works were probably obtained along particular directions (e.g., filaments) and are not representative of the typical behavior of clusters. Comparing our results with numerical simulations, we find that non-radiative simulations fail to reproduce the gas distribution, even well outside cluster cores. Therefore, a detailed treatment of gas cooling, star formation, clumping, and AGN feedback is required to construct realistic models of cluster outer regions.

Eckert, D.↗

The Perseus and Coma clusters of galaxies at energies above 20 keV

Observations of the Perseus and Coma clusters of galaxies were made on June 21, 1974, with a balloon-borne X-ray telescope (energies of approximately 20-150 keV). No positive detection was made. The data favor a thermal bremsstrahlung mechanism for the X-ray production in the Perseus cluster of galaxies over the inverse Compton mechanism. In the case of the Coma cluster of galaxies, the data are inconclusive with respect to determining the origin of the X-rays.

Scheepmaker, A.↗

The Atacama Cosmology Telescope: arcminute-resolution maps of 18,000 square degrees of the microwave sky from ACT 2008-2018 data combined with Planck

This paper presents a maximum-likelihood algorithm for combining sky maps with disparate sky coverage, angular resolution and spatially varying anisotropic noise into a single map of the sky. We use this to merge hundreds of individual maps covering the 2008–2018 ACT observing seasons, resulting in by far the deepest ACT maps released so far. We also combine the maps with the full Planck maps, resulting in maps that have the best features of both Planck and ACT: Planck’s nearly white noise on intermediate and large angular scales and ACT’s high-resolution and sensitivity on small angular scales. The maps cover over 18 000 square degrees, nearly half the full sky, at 100, 150 and 220 GHz. They reveal 4 000 optically-confirmed clusters through the Sunyaev Zel’dovich effect (SZ) and 18 500 point source candidates at > 5σ, the largest single collection of SZ clusters and millimeter wave sources to date. The multi-frequency maps provide millimeter images of nearby galaxies and individual Milky Way nebulae, and even clear detections of several nearby stars. Other anticipated uses of these maps include, for example, thermal SZ and kinematic SZ cluster stacking, CMB cluster lensing and galactic dust science. The method itself has negligible bias. However, due to the preliminary nature of some of the component data sets, we caution that these maps should not be used for precision cosmological analysis. The maps are part of ACT DR5, and will be made available on LAMBDA no later than three months after the journal publication of this article, along with an interactive sky atlas.

Sigurd Naess↗

The X-ray spectrm of the Coma Cluster of galaxies

The HEAO 1 A-2 and OSO 8 observations of the Coma Cluster of galaxies in the 2-60 keV energy band are presented. The spectrum is not well fitted by an isothermal bremsstrahlung model with an Fe line. To model the spectrum more accurately, we have performed fits of polytropic models and find that the gas is well fitted by an equation of state with polytropic index ranging between 1.38 and 1.6. This analysis reduces the total cluster mass by a factor of 3-5 below the optically derived virial mass. Using the best-fitting parameters, the gas is found to be a significant fraction (25 percent) of the total cluster mass. The Fe K-alpha line blend is strongly detected, from which we infer 0.18-0.36 solar abundance in the X-ray emitting gas. The Fe K-beta line blend is also weakly detected. The spectrum is not consistent with the existence of a hard X-ray tail at the flux level reported by Bazzano el al. (1984). In addition, the HEAO I scanning mode data place strong constraints on possible contaminating sources in the region surrounding Coma.

Henriksen, M. J.↗

Rocket Science for the Internet

Rainfinity, a company resulting from the commercialization of Reliable Array of Independent Nodes (RAIN), produces the product, Rainwall. Rainwall runs a cluster of computer workstations, creating a distributed Internet gateway. When Rainwall detects a failure in software or hardware, traffic is shifted to a healthy gateway without interruptions to Internet service. It more evenly distributes workload across servers, providing less down time.

Source record↗

Evaluation of NASA’s Remote-Sensing Capabilities in Coastal Environments

This work represents the second part of a two-part study set up by the National Aeronautics and Space Administration (NASA) and Bureau of Ocean Energy Management (BOEM) in 2017. The study summarizes a three-year investigation on the feasibility of using satellite remote sensing to monitor emissions of oil and gas operations over the outer continental shelf (OCS) to determine if they have negative impacts on coastal air quality (AQ). The target pollutant is nitrogen dioxide (NO2), for which BOEM conducts regular emissions surveys. The major data source for this report is a May 2019 oceanographic cruise (“SCOAPE” = Satellite Coastal and Oceanic Atmospheric Pollution Experiment) conducted in the Gulf of Mexico (GoM) off Louisiana with the Research Vessel Point Sur, loaded with a suite of trace-gas analyzers and a direct-sun remote sensor Pandora spectrometer that measures column NO2. The SCOAPE cruise, augmented by in situ NO2 and Pandora measurements at Cocodrie (Louisiana; 29.25° N, 90.66° W) showed that (1) under cloud-free conditions, satellites can detect elevated column NO2 amounts near isolated large platforms and from clusters of smaller operations; (2) satellite (the European Space Agency’s TROPOspheric Monitoring Instrument [TROPOMI]) total column (TC) NO2 agrees well with TC NO2 from ground-based Pandora spectrometers (to 11–18%), with the satellite biased low when pollution levels are higher; (3) in general, NO2 measured in situ and in column amounts by Pandora or TROPOMI is greater over coastal Louisiana than over the OCS; (4) the extent to which Pandora or satellite measurements correlate with surface NO2 is highly variable.

NO2↗

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

The Future of Exoplanet Direct Detection

Diffraction fundamentally limits our ability to image and characterize exoplanets. Currant and planned coronagraphic searches for exoplanets are making incredible strides but are fundamentally limited by the inner working angle of a few λ/D. Some crucial topics, such as demographics of exoplanets within the first 50 Myr and the infrared characterization of terrestrial planets, are beyond the reach of the single aperture angular resolution for the foreseeable future. Interferometry offers some advantages in exoplanet detection and characterization and we explore in this white paper some of the potential scientific breakthroughs possible. We demonstrate here that investments in "exoplanet interferometry" could open up new possibilities for speckle suppression through spatial coherence, a giant boost in astrometric precision for determining exoplanet orbits, ability to take a census of young giant exoplanets (clusters <50 Myr age), and an unrivaled potential for infrared nulling from space to detect terrestrial planets and search for atmospheric biomarkers. All signs point to an exciting future for exoplanets and interferometers, albeit a promise that will take decades to fulfill.

John Monnier↗

Reactive Collision Avoidance Algorithm

The reactive collision avoidance (RCA) algorithm allows a spacecraft to find a fuel-optimal trajectory for avoiding an arbitrary number of colliding spacecraft in real time while accounting for acceleration limits. In addition to spacecraft, the technology can be used for vehicles that can accelerate in any direction, such as helicopters and submersibles. In contrast to existing, passive algorithms that simultaneously design trajectories for a cluster of vehicles working to achieve a common goal, RCA is implemented onboard spacecraft only when an imminent collision is detected, and then plans a collision avoidance maneuver for only that host vehicle, thus preventing a collision in an off-nominal situation for which passive algorithms cannot. An example scenario for such a situation might be when a spacecraft in the cluster is approaching another one, but enters safe mode and begins to drift. Functionally, the RCA detects colliding spacecraft, plans an evasion trajectory by solving the Evasion Trajectory Problem (ETP), and then recovers after the collision is avoided. A direct optimization approach was used to develop the algorithm so it can run in real time. In this innovation, a parameterized class of avoidance trajectories is specified, and then the optimal trajectory is found by searching over the parameters. The class of trajectories is selected as bang-off-bang as motivated by optimal control theory. That is, an avoiding spacecraft first applies full acceleration in a constant direction, then coasts, and finally applies full acceleration to stop. The parameter optimization problem can be solved offline and stored as a look-up table of values. Using a look-up table allows the algorithm to run in real time. Given a colliding spacecraft, the properties of the collision geometry serve as indices of the look-up table that gives the optimal trajectory. For multiple colliding spacecraft, the set of trajectories that avoid all spacecraft is rapidly searched on-line. The optimal avoidance trajectory is implemented as a receding-horizon model predictive control law. Therefore, at each time step, the optimal avoidance trajectory is found and the first time step of its acceleration is applied. At the next time step of the control computer, the problem is re-solved and the new first time step is again applied. This continual updating allows the RCA algorithm to adapt to a colliding spacecraft that is making erratic course changes.

Scharf, Daniel↗

Detections of hard X-ray emissions from bright early-type galaxies with ASCA

Five bright elliptical galaxies in the Virgo Cluster, NGC 4365, NGC 4374 (M84), NGC 4406 (M86), NGC 4472 (M49), and NGC 4636, were observed with ASCA. In addition to the extended thermal X-ray emission of temperature kT approximately 1 keV, harder X-rays with color temperature kT greater than or equal to 2 keV were detected from all of them. The 2-10 keV luminosities of this hard component for the five galaxies, integrated within 5 min, are distributed within a relatively narrow range of (1-4) x 10(exp 40) ergs/s. The hard X-ray component is primarily attributed to the integrated emission from discrete X-ray sources. In NGC 4406 and NGC 4374 the data indicate that the hard component is contributed additionally by foreground/background emission from the hot intracluster medium (ICM) of the Virgo Cluster. The hard component of NGC 4472 seems also contributed by the Virgo ICM emission, but in this case there is evidence that the ICM brightness is locally enhanced within approximately 10 min of NGC 4472.

Matsushita, K.↗

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗

Gamma rays from globular clusters

Globular clusters are known to contain a relatively large number of pulsars whose individual and collective emission in the X-ray and gamma-ray energy bands may be detectable by the instruments on board the Compton Gamma-Ray Observatory (CGRO), ROSAT, and possibly SIGMA. We discuss the several types of high-energy emission expected from isolated and interacting binary pulsars in globular clusters. Individual or collective high-energy emission from isolated pulsars is expected to be too low to be detected with current instruments. However, a class of high-luminosity hidden millisecond pulsars enshrouded in the evaporating material from irradiated companion stars can produce unpulsed shock emission detectable by the high-sensitivity instruments of ROSAT and CGRO. Establishing upper limits of high-energy emission from globular clusters will be valuable in constraining models for the formation of cluster millisecond pulsars.

Tavani, Marco↗

The Soft X-Ray Emission in a Large Sample of Galaxy Clusters with ROSAT PSPC

The study of soft X-ray emission of 38 X-ray selected galaxy clusters observed by ROSAT PSPC indicates that the soft excess phenomenon may be a common occurrence in galaxy clusters. Excess soft X-ray radiation, above the contribution from the hot intra-cluster medium, is evident in a large fraction of sources, and is clearly detected with large statistical significance in the deepest observations. The investigation relies on new, high resolution 21 cm HI observations. The sample selection also features analysis of infrared images, to further ensure reliability of results with respect to the characteristics of Galactic absorption. The possibility of background or calibration effects as cause of the excess emission is likewise investigated; a detailed analysis of the distribution of the excess emission with respect to detector position and Galactic HI column density shows that the excess emission is a genuine celestial phenomenon. We find evidence for a preferential distribution of the soft excess emission at distances larger than approx. 150-200 kpc from the centers of clusters; this behavior may be naturally explained in the context of a non-thermal Inverse-Compton scenario. Alternatively, we propose that the phenomenon maybe caused by thermal emission of very large-scale 'warm' filaments seen in recent hydrodynamic simulations. This new interpretation relieves the very demanding requirements of either the traditional intra-cluster 'warm' gas and the non-thermal scenarios. We also investigate the possibility of the soft excess originating from unresolved, X-ray faint cluster galaxies.

Bonamente, Massimiliano↗

VV 5-32-63/64 - The 'supermassive double galaxy' in Abell 1775 revisited

Spectroscopy is presented for galaxies in A 1775, the cluster containing VV 5-32-63/64, an apparent double galaxy which has long been considered a gravitationally bound system with supermassive components (10 to the 13th solar masses). However, this model has run afoul of recent radio observations, which detected an extremely long straight radio tail emanating from the eastern galaxy in the pair. The present observations indicate that if both galaxies are members of the same cluster, the cluster radial-velocity dispersion (1522 km/sec) is large enough to account for the velocity difference between the two VV galaxies (1650 km/sec). It is therefore suggested that the VV galaxies are not gravitationally bound and need not be supermassive. The large velocity dispersion measured for A 1775 also strains suggested velocity dispersion vs X-ray luminosity relations for clusters of galaxies, as A 1775 has not yet been detected at X-ray wavelengths.

Hintzen, P.↗