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The dynamics of the surface and inner atmosphere of the red supergiant star Betelgeuse are the subject of numerous high angular resolution and spectroscopic studies. Here, we present three-telescope interferometric data obtained at 11.15 microns wavelength with the Berkeley Infrared Spatial Interferometer (ISI), that probe the stellar surface continuum. We find striking variability in the size, effective temperature, and degree of asymmetry of the star over the years 2006-2009. These results may indicate an evolving shell of optically thick material close to the stellar photosphere.
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We present an interferometric study of the continuum surface of the red supergiant star Betelgeuse at 11.15 microns wavelength, using data obtained with the Berkeley Infrared Spatial Interferometer each year between 2006 and 2010. These data allow an investigation of an optically thick layer within 1.4 stellar radii of the photosphere. The layer has an optical depth of ~1 at 11.15 microns, and varies in temperature between 1900 K and 2800 K and in outer radius between 1.16 and 1.36 stellar radii. Electron-hydrogen atom collisions contribute significantly to the opacity of the layer. The layer has a non-uniform intensity distribution that changes between observing epochs. These results indicate that large-scale surface convective activity strongly influences the dynamics of the inner atmosphere of Betelgeuse, and mass-loss processes.
We present optical spectrophotometry of the red supergiant Betelgeuse from 2020 February 15, during its recent unprecedented dimming episode. By comparing this spectrum to stellar atmosphere models for cool supergiants, as well as spectrophotometry of other Milky Way red supergiants, we conclude that Betelgeuse has a current effective temperature of 3600 +/- 25 K. While this is slightly cooler than previous measurements taken prior to Betelgeuses recent lightcurve evolution, this drop in effective temperature is insufficient to explain Betelgeuses recent optical dimming. We propose that episodic mass loss and an increase in the amount of large-grain circumstellar dust along our sightline to Betelgeuse is the most likely explanation for its recent photometric evolution.
The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a one fits it all model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces of graph dynamics and to quantify temporal changes, this paper suggests to build upon the concept of centrality, a measure of node importance in a network. In particular, we introduce the notion of centrality distance, a natural similarity measure for two graphs which depends on a given centrality, characterizing the graph type. Intuitively, centrality distances reflect the extent to which (non-anonymous) node roles are different or, in case of dynamic graphs, have changed over time, between two graphs. We evaluate the centrality distance approach for five evolutionary models and seven real-world social and physical networks. Our results empirically show the usefulness of centrality distances for characterizing graph dynamics compared to a null-model of random evolution, and highlight the differences between the considered scenarios. Interestingly, our approach allows us to compare the dynamics of very different networks, in terms of scale and evolution speed.
The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a one fits it all model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces of graph dynamics and to quantify temporal changes, this paper suggests to build upon the concept of centrality, a measure of node importance in a network. In particular, we introduce the notion of centrality distance, a natural similarity measure for two graphs which depends on a given centrality, characterizing the graph type. Intuitively, centrality distances reflect the extent to which (non-anonymous) node roles are different or, in case of dynamic graphs, have changed over time, between two graphs. We evaluate the centrality distance approach for five evolutionary models and seven real-world social and physical networks. Our results empirically show the usefulness of centrality distances for characterizing graph dynamics compared to a null-model of random evolution, and highlight the differences between the considered scenarios. Interestingly, our approach allows us to compare the dynamics of very different networks, in terms of scale and evolution speed.
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But is this assumption true? And, in either case, what are the characteristics of such fraudulent behaviors? In this work, we set up honeypots (dummy social network accounts), and buy fake followers (after careful IRB approval). We report the signs of such behaviors including oddities in local network connectivity, account attributes, and similarities and differences across fraud providers. Most valuably, we discover and characterize several types of fraud behaviors. We discuss how to leverage our insights in practice by engineering strongly performing entropy-based features and demonstrating high classification accuracy. Our contributions are (a) instrumentation: we detail our experimental setup and carefully engineered data collection process to scrape Twitter data while respecting API rate-limits, (b) observations on fraud multimodality: we analyze our honeypot fraudster ecosystem and give surprising insights into the multifaceted behaviors of these fraudster types, and (c) features: we propose novel features that give strong (>0.95 precision/recall) discriminative power on ground-truth Twitter data.
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