Victor Pankratius

IMAGING THE SUPERMASSIVE BLACK HOLE SHADOW AND JET BASE OF M87 WITH THE EVENT HORIZON TELESCOPE

The Event Horizon Telescope (EHT) is a project to assemble a Very Long Baseline Interferometry (VLBI) network of millimeter wavelength dishes that can resolve strong field general relativistic signatures near a supermassive black hole. As planned, the EHT will include enough dishes to enable imaging of the predicted black hole “shadow,” a feature caused by severe light bending at the black hole boundary. The center of M87, a giant elliptical galaxy, presents one of the most interesting EHT targets as it exhibits a relativistic jet, offering the additional possibility of studying jet genesis on Schwarzschild radius scales. Fully relativistic models of the M87 jet that fit all existing observationalconstraintsnowallowhorizon-scaleimagestobegenerated.WeperformrealisticVLBIsimulationsof M87 model images to examine the detectability of the black shadow with the EHT, focusing on a sequence of model images with a changing jet mass load radius. When the jet is launched close to the black hole, the shadow is clearly visible both at 230 and 345 GHz. The EHT array with a resolution of 20‐30 μas resolution (∼2‐4 Schwarzschild radii) is able to image this feature independent of any theoretical models and we show that imaging methods used to process data from optical interferometers are applicable and effective for EHT data sets. We demonstrate that the EHT is also capable of tracing real-time structural changes on a few Schwarzschild radii scales, such as those implicated by very high-energy flaring activity of M87. While inclusion of ALMA in the EHT is critical for shadow imaging, the array is generally robust against loss of a station.

Imaging an event horizon: Mitigation of scattering toward Sagittarius a

The image of the emission surrounding the black hole in the center of the Milky Way is predicted to exhibit the imprint of general relativistic (GR) effects, including the existence of a shadow feature and a photon ring of diameter ~50 microarcseconds. Structure on these scales can be resolved by millimeter-wavelength very long baseline interferometry (VLBI). However, strong-field GR features of interest will be blurred at lambda >= 1.3 mm due to scattering by interstellar electrons. The scattering properties are well understood over most of the relevant range of baseline lengths, suggesting that the scattering may be (mostly) invertible. We simulate observations of a model image of Sgr A* and demonstrate that the effects of scattering can indeed be mitigated by correcting the visibilities before reconstructing the image. This technique is also applicable to Sgr A* at longer wavelengths.

Mobile crowd sensing in space weather monitoring: the mahali project

Space weather refers to the conditions and evolution of Earths near space environment including electron density variations in the ionosphere. This environment is influenced by both the Sun and terrestrial processes, and has an impact on communications, navigation, and terrestrial power systems. The recent discovery of clear signatures in the ionosphere related to tsunamis and earthquakes suggests that the ionosphere itself may serve as a valuable and versatile sensor, registering many types of Earth- and space-based phenomena. To realize this potential, ionospheric electron density must be monitored through a dense wide-area sensor mesh that is expensive to realize with traditional deployments and observation techniques. Crowdsourcing can help pursue this novel direction by providing new capabilities, including an increase in the number of sensors as well as expanding data transport capabilities through participating devices that act as relays. This article describes the Mahali project, which is currently at the beginning of exploring these promising techniques. Mahali uses GPS signals that penetrate the ionosphere for science rather than positioning. A large number of ground-based sensors will be able to feed data through mobile devices into a cloud-based processing environment, enabling a tomographic analysis of the global ionosphere at unprecedented resolution and coverage. This novel approach brings the exploitation of the ionosphere as a global earth system sensor technologically and economically within reach.

Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support

The process of scientific discovery is traditionally assumed to be entirely executed by humans. This article highlights how increasing data volumes and human cognitive limits are challenging this traditional assumption. Relevant examples are found in observational astronomy and geoscience, disciplines that are undergoing transformation due to growing networks of space-based and ground-based sensors. The authors outline how intelligent systems for computer-aided discovery can routinely complement and integrate human scientists in the insight generation loop in scalable ways for next-generation science. The pragmatics of model-based computer-aided discovery systems go beyond feature detection in empirical data to answer fundamental questions, such as how empirical detections fit into hypothesized models and model variants to ease the scientists work of placing large ensembles of detections into a theoretical context. The authors demonstrate successful applications of this paradigm in several areas, including ionospheric studies, volcanics, astronomy, and planetary landing site identification for spacecraft and robotic missions.

Wavelet-based Characterization of Small-scale Solar Emission Features at Low Radio Frequencies

Low radio frequency solar observations using the Murchison Widefield Array have recently revealed the presence of numerous weak short-lived narrowband emission features, even during moderately quiet solar conditions. These nonthermal features occur at rates of many thousands per hour in the 30.72 MHz observing bandwidth, and hence necessarily require an automated approach for their detection and characterization. Here, we employ continuous wavelet transform using a mother Ricker wavelet for feature detection from the dynamic spectrum. We establish the efficacy of this approach and present the first statistically robust characterization of the properties of these features. In particular, we examine distributions of their peak flux densities, spectral spans, temporal spans, and peak frequencies. We can reliably detect features weaker than 1 SFU, making them, to the best of our knowledge, the weakest bursts reported in literature. The distribution of their peak flux densities follows a power law with an index of −2.23 in the 12–155 SFU range, implying that they can provide an energetically significant contribution to coronal and chromospheric heating. These features typically last for 1–2 s and possess bandwidths of about 4–5 MHz. Their occurrence rate remains fairly flat in the 140–210 MHz frequency range. At the time resolution of the data, they appear as stationary bursts, exhibiting no perceptible frequency drift. These features also appear to ride on a broadband background continuum, hinting at the likelihood of them being weak type-I bursts.

Clustering Throughput Optimization on the GPU

Large datasets in astronomy and geoscience often require clustering and visualizations of phenomena at different densities and scales in order to generate scientific insight. We examine the problem of maximizing clustering throughput for concurrent dataset clustering in spatial dimensions. We introduce a novel hybrid approach that uses GPUs in conjunction with multicore CPUs for algorithmic throughput optimizations. The key idea is to exploit the fast memory on the GPU for index searches and optimize I/O transfers in such a way that the low-bandwidth host-GPU bottleneck does not have a significant negative performance impact. To achieve this, we derive two distinct GPU kernels that exploit grid-based indexing schemes to improve clustering performance. To obviate limited GPU memory and enable large dataset clustering, our method is complemented by an efficient batching scheme for transfers between the host and GPU accelerator. This scheme is robust with respect to both sparse and dense data distributions and intelligently avoids buffer overflows that would otherwise degrade performance, all while minimizing the number of data transfers between the host and GPU. We evaluate our approaches on ionospheric total electron content datasets as well as intermediate-redshift galaxies from the Sloan Digital Sky Survey. Our hybrid approach yields a speedup of up to 50x over the sequential implementation on one of the experimental scenarios, which is respectable for I/O intensive clustering.

Exploiting Variant-Based Parallelism for Data Mining of Space Weather Phenomena

This paper studies a form of parallelism termed variant-based parallelism, which exploits commonalities and reuse among variant computations in order to improve multithreading scalability. The problem is motivated by space weather studies that aim to identify changes in the Earths ionosphere caused by auroral activity, tsunamis, and earthquakes. Today it is common to execute cluster algorithm variants with different parameters in order to determine which ones best explain phenomena in empirical data. We propose a novel approach and a set of optimizations to maximize throughput in such clustering algorithms. This is achieved by executing multiple clustering algorithm variants in parallel and developing efficient approaches to concurrently cluster data and maximize the reuse of results from completed variants. We present evaluations on real-world space weather datasets with up to 5 million ionospheric total electron content data points as well as synthetic datasets with up to a million data points. Results show a 1101% performance improvement due to indexing tailored for variant-based clustering, and a 2209% performance improvement when applying all of our proposed optimizations. Our optimizations enable new approaches in computer-aided discovery and could enable the short run times required for early warning systems for natural hazards.

Computer aided detection of transient inflation events at Alaskan volcanoes using GPS measurements from 2005–2015

Abstract Analysis of transient deformation events in time series data observed via networks of continuous Global Positioning System (GPS) ground stations provide insight into the magmatic and tectonic processes that drive volcanic activity. Typical analyses of spatial positions originating from each station require careful tuning of algorithmic parameters and selection of time and spatial regions of interest to observe possible transient events. This iterative, manual process is tedious when attempting to make new discoveries and does not easily scale with the number of stations. Addressing this challenge, we introduce a novel approach based on a computer-aided discovery system that facilitates the discovery of such potential transient events. The advantages of this approach are demonstrated by actual detections of transient deformation events at volcanoes selected from the Alaska Volcano Observatory database using data recorded by GPS stations from the Plate Boundary Observatory network. Our technique successfully reproduces the analysis of a transient signal detected in the first half of 2008 at Akutan volcano and is also directly applicable to 3 additional volcanoes in Alaska, with the new detection of 2 previously unnoticed inflation events: in early 2011 at Westdahl and in early 2013 at Shishaldin. This study also discusses the benefits of our computer-aided discovery approach for volcanology in general. Advantages include the rapid analysis on multi-scale resolutions of transient deformation events at a large number of sites of interest and the capability to enhance reusability and reproducibility in volcano studies.

Radio Array of Portable Interferometric Detectors (RAPID): Development of a deployable multiple application radio array

The Radio Array of Portable Interferometric Detectors (RAPID) is an advanced radio designed for multi-role applications. The system implements a spatially diverse sparse array technology and can be deployed and reconfigured easily. Data are captured at the raw voltage level using the system in the field and processed post-experiment. Signal processing for the system is software defined and uses a scalable Cloud computing architecture. The system builds upon the Square Kilometer Array Low Frequency Aperture antenna (SKALA) in combination with custom hardware for data acquisition on a per antenna basis. The instrument uses physically disconnected elements, a high performance direct digitization receiver, hot swap solid state storage, solar and battery power, and wireless control for interconnection. Schedule based operation can also be used in radio quiet locations or to enable minimally attended operation. RAPID is intended for application as both an Astronomical radio telescope and a Geospace imaging radar system. The high degree of mobility a orded by the system enables a wide variety of interferometric configurations and allows deployment of the instrument at locations which are optimal for specific scientific goals.

Computing in Astronomy: To See the Unseen

Advances in computing have empowered astronomers to explore the universe in greater detail. Software-defined instruments relying on digital data capture and processing are more powerful than ever and continue to bring us new knowledge about the universe and our place in it.