A Review of Coronagraphic Observations of Shocks Driven by Coronal Mass Ejections

The existence of shocks driven by Coronal Mass Ejections (CMEs) has always been assumed based on the superalfvenic speeds for some of these events and on indirect evidence such as radio bursts and distant streamer deflections. However, the direct signature of the plasma enhancement at the shock front has escaped detection until recently. Since 2003, work on LASCO observations has shown that CME-driven shocks can be detected by white light coronagraph observations from a few solar radii to at least 20 Rsun. Shock properties, such as the density compression ratio and their direction can be extracted from the data. We review this work here and demonstrate how to recognize the various shock morphologies in the images.We also discuss how the two-viewpoint coronagraph observations from the STEREO mission allow the reconstruction of the 3D envelope of the shock revealing some interesting properties of the shocks (e.g., anisotropic expansion).Comment: 8 pages, 5 figures, to appear in Shocks Waves in Astrophysical Environments, Proceedings of the 8th Annual International Astrophysics Conference (referred), AIP Conf. Pro

Physics of relativistic shocks

Relativistic shocks are usually thought to occur in violent astrophysical explosions. These collisionless shocks are mediated by a plasma kinetic streaming instability, often loosely referred to as the Weibel instability, which generates strong magnetic fields "from scratch" very efficiently. In this review paper we discuss the shock micro-physics and present a recent model of "pre-conditioning" of an initially unmagnetized upstream region via the cosmic-ray-driven Weibel-type instability.Comment: Subm. to proceedings of the Annual International Astrophysics Conference (AIAC-8), Hawaii, 200

Particle Acceleration at Relativistic Shocks in Extragalactic Systems

Diffusive shock acceleration (DSA) at relativistic shocks is expected to be an important acceleration mechanism in a variety of astrophysical objects including extragalactic jets in active galactic nuclei and gamma ray bursts. These sources remain strong and interesting candidate sites for the generation of ultra-high energy cosmic rays. In this paper, key predictions of DSA at relativistic shocks that are salient to the issue of cosmic ray ion and electron production are outlined. Results from a Monte Carlo simulation of such diffusive acceleration in test-particle, relativistic, oblique, MHD shocks are presented. Simulation output is described for both large angle and small angle scattering scenarios, and a variety of shock obliquities including superluminal regimes when the de Hoffman-Teller frame does not exist. The distribution function power-law indices compare favorably with results from other techniques. They are found to depend sensitively on the mean magnetic field orientation in the shock, and the nature of MHD turbulence that propagates along fields in shock environs. An interesting regime of flat spectrum generation is addressed, providing evidence for its origin being due to shock drift acceleration. The impact of these theoretical results on gamma-ray burst and blazar science is outlined. Specifically, Fermi gamma-ray observations of these cosmic sources are already providing significant constraints on important environmental quantities for relativistic shocks, namely the frequency of scattering and the level of field turbulence.Comment: 11 pages, 6 figures, to appear in Proc. of the 8th International Astrophysics Conference "Shock Waves in Space and Astrophysical Environments" (2010), eds. X. Ao, R. Burrows and G. P. Zank (AIP Conf. Proc., New York

Determination of hydroxyl groups in biorefinery resources via quantitative 31P NMR spectroscopy

The analysis of chemical structural characteristics of biorefinery product streams (such as lignin and tannin) has advanced substantially over the past decade, with traditional wet-chemical techniques being replaced or supplemented by NMR methodologies. Quantitative 31P NMR spectroscopy is a promising technique for the analysis of hydroxyl groups because of its unique characterization capability and broad potential applicability across the biorefinery research community. This protocol describes procedures for (i) the preparation/solubilization of lignin and tannin, (ii) the phosphitylation of their hydroxyl groups, (iii) NMR acquisition details, and (iv) the ensuing data analyses and means to precisely calculate the content of the different types of hydroxyl groups. Compared with traditional wet-chemical techniques, the technique of quantitative 31P NMR spectroscopy offers unique advantages in measuring hydroxyl groups in a single spectrum with high signal resolution. The method provides complete quantitative information about the hydroxyl groups with small amounts of sample (~30 mg) within a relatively short experimental time (~30-120 min)

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment

An operational solar wind prediction system transitioning fundamental science to operations

We present in this paper an operational solar wind prediction system. The system is an outcome of the collaborative efforts between scientists in research communities and forecasters at Space Environment Prediction Center (SEPC) in China. This system is mainly composed of three modules: (1) a photospheric magnetic field extrapolation module, along with the Wang-Sheeley-Arge (WSA) empirical method, to obtain the background solar wind speed and the magnetic field strength on the source surface; (2) a modified Hakamada-Akasofu-Fry (HAF) kinematic module for simulating the propagation of solar wind structures in the interplanetary space; and (3) a coronal mass ejection (CME) detection module, which derives CME parameters using the ice-cream cone model based on coronagraph images. By bridging the gap between fundamental science and operational requirements, our system is finally capable of predicting solar wind conditions near Earth, especially the arrival times of the co-rotating interaction regions (CIRs) and CMEs. Our test against historical solar wind data from 2007 to 2016 shows that the hit rate (HR) of the high-speed enhancements (HSEs) is 0.60 and the false alarm rate (FAR) is 0.30. The mean error (ME) and the mean absolute error (MAE) of the maximum speed for the same period are −73.9 km s−1 and 101.2 km s−1, respectively. Meanwhile, the ME and MAE of the arrival time of the maximum speed are 0.15 days and 1.27 days, respectively. There are 25 CMEs simulated and the MAE of the arrival time is 18.0 h

An operational solar wind prediction system transitioning fundamental science to operations

We present in this paper an operational solar wind prediction system. The system is an outcome of the collaborative efforts between scientists in research communities and forecasters at Space Environment Prediction Center (SEPC) in China. This system is mainly composed of three modules: (1) a photospheric magnetic field extrapolation module, along with the Wang-Sheeley-Arge (WSA) empirical method, to obtain the background solar wind speed and the magnetic field strength on the source surface; (2) a modified Hakamada-Akasofu-Fry (HAF) kinematic module for simulating the propagation of solar wind structures in the interplanetary space; and (3) a coronal mass ejection (CME) detection module, which derives CME parameters using the ice-cream cone model based on coronagraph images. By bridging the gap between fundamental science and operational requirements, our system is finally capable of predicting solar wind conditions near Earth, especially the arrival times of the co-rotating interaction regions (CIRs) and CMEs. Our test against historical solar wind data from 2007 to 2016 shows that the hit rate (HR) of the high-speed enhancements (HSEs) is 0.60 and the false alarm rate (FAR) is 0.30. The mean error (ME) and the mean absolute error (MAE) of the maximum speed for the same period are −73.9 km s−1 and 101.2 km s−1, respectively. Meanwhile, the ME and MAE of the arrival time of the maximum speed are 0.15 days and 1.27 days, respectively. There are 25 CMEs simulated and the MAE of the arrival time is 18.0 h

An operational solar wind prediction system transitioning fundamental science to operations

We present in this paper an operational solar wind prediction system. The system is an outcome of the collaborative efforts between scientists in research communities and forecasters at Space Environment Prediction Center (SEPC) in China. This system is mainly composed of three modules: (1) a photospheric magnetic field extrapolation module, along with the Wang-Sheeley-Arge (WSA) empirical method, to obtain the background solar wind speed and the magnetic field strength on the source surface; (2) a modified Hakamada-Akasofu-Fry (HAF) kinematic module for simulating the propagation of solar wind structures in the interplanetary space; and (3) a coronal mass ejection (CME) detection module, which derives CME parameters using the ice-cream cone model based on coronagraph images. By bridging the gap between fundamental science and operational requirements, our system is finally capable of predicting solar wind conditions near Earth, especially the arrival times of the co-rotating interaction regions (CIRs) and CMEs. Our test against historical solar wind data from 2007 to 2016 shows that the hit rate (HR) of the high-speed enhancements (HSEs) is 0.60 and the false alarm rate (FAR) is 0.30. The mean error (ME) and the mean absolute error (MAE) of the maximum speed for the same period are −73.9 km s−1 and 101.2 km s−1, respectively. Meanwhile, the ME and MAE of the arrival time of the maximum speed are 0.15 days and 1.27 days, respectively. There are 25 CMEs simulated and the MAE of the arrival time is 18.0 h