Valeriy V. Gavrishchaka

Interactions between dust grains in a dusty plasma

Dust grains in plasma acquire a large negative charge, and can constitute a strongly coupled system. If the plasma is stationary, the plasma-mediated electrostatic potential around a single grain can be calculated by orbital-motion-limited (OML) theory, including ion absorption at the grain surface. This potential is repulsive at all ranges, and falls off as r−2 at long range. Nonlinear modifications occur when there are several grains, but the interaction is still repulsive. If the plasma is streaming by the grains, each grain generates a wake field potential which can be calculated via linear response theory, and which attracts other grains to stationary points behind the grain. There is in addition an attractive force between grains, due to ion-impact momentum deposition. In certain parameter regimes, this “shadowing” force can yield a weak net attraction at long range. Trapped-ion effects are significant at high plasma density, but have not yet been calculated.

Trapped ion effect on shielding, current flow, and charging of a small object in a plasma

The problem of electrostatic shielding around a small spherical collector immersed in nonflowing plasma, and the related problem of electron and ion flow to the collector, date to the origins of plasma physics. Calculations have typically neglected collisions, on the grounds that the mean free path is long compared to the Debye length. However, it has long been suspected that negative-energy trapped ions, created by occasional collisions, could be important. This paper presents self-consistent analytic calculations of the density and distribution function of trapped and untrapped ions, the potential profile, the ion and electron current to the collector, and the floating potential and charge of the collector. Under typical conditions for dust grains immersed in a discharge plasma, trapped ions are found to dominate the shielding near the grain, substantially increase the ion current to the grain, and suppress the floating potential and grain charge, even when the mean free path is much greater than the Debye length.

Velocity‐shear‐driven ion‐cyclotron waves and associated transverse ion heating

Recent sounding rocket experiments, such as SCIFER, AMICIST, and ARCS-4, and satellite data from FAST, Freja, DE-2, and HILAT, provide compelling evidence of a correlation between small-scale spatial plasma inhomogeneities, broadband low-frequency waves, and transversely heated ions. These naturally arising, localized inhomogeneities can lead to sheared cross-magnetic-field plasma flows, a situation that has been shown to have potential for instability growth. Experiments performed in the Naval Research Laboratorys Space Physics Simulation Chamber demonstrate that broadband waves in the ion-cyclotron frequency range can be driven solely by a transverse, localized electric field, without the dissipation of a field-aligned current. Significant perpendicular ion energization resulting from these waves has been measured. Detailed comparisons with both theoretical predictions and space observations of electrostatic waves found in the presence of sheared cross-magnetic-field plasma flow are made.

Dispersive properties of a magnetized plasma with a field‐aligned drift and inhomogeneous transverse flow

Electrostatic fluctuations driven by the combination of a magnetic‐field‐aligned electron current and a localized transverse electric field are investigated. Characteristic parameters, such as scale length and magnitude of the sheared E×B velocity, magnitude of the magnetic‐field‐aligned current, and temperature ratio τ≡Ti/Te are varied to include conditions associated with electrostatic waves driven entirely by magnetic‐field‐aligned current, driven entirely by transverse electric field, and driven by a combination of magnetic‐field‐aligned current and transverse electric field. It is shown that, in contrast to the homogeneous case of current‐driven modes, the modes in the presence of a transverse‐velocity shear can be unstable in a wider range of temperature ratio τ and they are broadband in frequency. Using a simplified model, numerical solutions of the nonlocal dispersion relation, and physical arguments, cases of stabilization and destabilization due to the inhomogeneous energy‐density driven instabi...

Low Frequency Oscillations in A Plasma with Spatially Variable Field-Aligned Flow

The effects of a transverse gradient in the plasma flow velocity parallel to the ambient magnetic field are analyzed. A transverse velocity gradient in the parallel ion flow, even in small magnitude, can increase the parallel phase speed of the ion-acoustic waves sufficiently to reduce ion Landau damping. This results in a significantly lower threshold current for the current driven ion acoustic instability. Ion flow gradients can also give rise to a new class of ion cyclotron waves via inverse cyclotron damping. A broadband wave spectrum with multiple cyclotron harmonics is possible. A combination of the multiple cyclotron harmonic waves can result in spiky electric field structures with their peaks separated by an ion cyclotron period. A spatial gradient in the parallel electron flow is also considered but it is found to play a minimal role in the low frequency regime. Relevance of these to natural plasma environments is discussed.

Support vector machine as an efficient framework for stock market volatility forecasting

Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data.

Support vector machine as an efficient tool for high-dimensional data processing: Application to substorm forecasting

The support vector machine (SVM) has been used to model solar wind-driven geomagnetic substorm activity characterized by the auroral electrojet (AE) index. The focus of the present study, which is the first application of the SVM to space physics problems, is reliable prediction of large-amplitude substorm events from solar wind and interplanetary magnetic field data. This forecasting problem is important for many practical applications as well as for further understanding of the overall substorm dynamics. SVM has been trained on symbolically encoded AE index time series to perform supercritical/subcritical classification with respect to an application-dependent threshold. It is shown that SVM performance can be comparable to or even superior to that of the neural networks model. The advantages of the SVM-based techniques are expected to be much more pronounced in future space weather forecasting models, which will incorporate many types of high-dimensional, multiscale input data once real time availability of this information becomes technologically feasible.

Perpendicular ion heating by velocity‐shear‐driven waves

Perpendicular ion heating resulting from velocity-shear-driven ion-cyclotron waves has been measured for the first time. The experiment was performed in the Naval Research Laboratorys Space Physics Simulation Chamber (SPSC) under plasma conditions approaching those in the natural space environment. Sheared cross-field flow is induced by a controllable, inhomogeneous, transverse, DC electric field (LE ∼ (1–2)ρi) created without drawing significant levels of magnetic-field aligned current. Mode frequency data suggest that the most efficient heating occurs when the Doppler shifted frequency in the ion frame is located near a harmonic of the ion-cyclotron frequency.

Velocity-shear-induced ion-cyclotron turbulence: Laboratory identification and space applications

Laboratory measurements are reported that identify a new class of plasma oscillation driven by the inhomogeneity in wave energy density caused by transverse‐velocity shear [Ganguli et al., Phys. Fluids 31, 823 (1988)]. The experiments concentrate on a branch of oscillation in the ion‐cyclotron range of frequencies that results from the coupling of the magnetic‐field‐aligned current and the inhomogeneous dc electric field localized in a layer thicker than the ion gyroradius. The observed transition between the well‐known current‐driven electrostatic ion‐cyclotron mode and this inhomogeneous energy–density‐driven mode is related to the ratio of the azimuthal and axial Doppler shifts. The mode characteristics associated with the two instabilities have significantly different properties. For conditions of large transverse‐velocity shear, turbulence is generated with a broadband, spiky spectrum around the ion‐cyclotron frequency at small values of the magnetic‐field‐aligned current. The experimental identification is reinforced with numerical results from a nonlocal eigenvalue condition.

Volatility forecasting from multiscale and high-dimensional market data

Abstract Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identified. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of effectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale effects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can effectively work with high-dimensional inputs to account for volatility long-memory and multiscale effects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging field of high-frequency finance and in multivariate models for portfolio risk management.