Genesis and impulsive evolution of the 2017 September 10 coronal mass ejection

The X8.2 event of 10 September 2017 provides unique observations to study the genesis, magnetic morphology and impulsive dynamics of a very fast CME. Combining GOES-16/SUVI and SDO/AIA EUV imagery, we identify a hot ($T\approx 10-15$ MK) bright rim around a quickly expanding cavity, embedded inside a much larger CME shell ($T\approx 1-2$ MK). The CME shell develops from a dense set of large AR loops ($\gtrsim$0.5 $R_s$), and seamlessly evolves into the CME front observed in LASCO C2. The strong lateral overexpansion of the CME shell acts as a piston initiating the fast EUV wave. The hot cavity rim is demonstrated to be a manifestation of the dominantly poloidal flux and frozen-in plasma added to the rising flux rope by magnetic reconnection in the current sheet beneath. The same structure is later observed as the core of the white light CME, challenging the traditional interpretation of the CME three-part morphology. The large amount of added magnetic flux suggested by these observations explains the extreme accelerations of the radial and lateral expansion of the CME shell and cavity, all reaching values of $5 - 10$ km s$^{-2}$. The acceleration peaks occur simultaneously with the first RHESSI $100-300$ keV hard X-ray burst of the associated flare, further underlining the importance of the reconnection process for the impulsive CME evolution. Finally, the much higher radial propagation speed of the flux rope in relation to the CME shell causes a distinct deformation of the white light CME front and shock.Comment: Accepted for publication in the Astrophysical Journa

Spectral collocation methods

This review covers the theory and application of spectral collocation methods. Section 1 describes the fundamentals, and summarizes results pertaining to spectral approximations of functions. Some stability and convergence results are presented for simple elliptic, parabolic, and hyperbolic equations. Applications of these methods to fluid dynamics problems are discussed in Section 2

Exchange Rate Smoothing in Hungary

The paper proposes a structural empirical model capable of examining exchange rate smoothing in the small, open economy of Hungary. The framework assumes the existence of an unobserved and changing implicit exchange rate target. The central bank is assumed to use interest rate policy to obtain this preferred rate in the medium term, while market participants are assumed to form rational expectations about this target and influence exchange rates accordingly. The paper applies unobserved variable method – Kalman filtering – to estimate this implicit exchange rate target, and simultaneously estimate an interest rate rule and an exchange rate equation consistent with this target. The results provide evidence for exchange rate smoothing in Hungary by providing an estimated smooth implicit exchange rate target development and by showing significant interest rate response to the deviation of the exchange rate from this target. The method also provides estimates for the ceteris paribus exchange rate effects of expected and unexpected interest rate changes.exchange rate smoothing, interest rate rules, Kalman filter

COLLISIONAL VERSUS COLLISIONLESS MATTER: A ONE-DIMENSIONAL ANALYSIS OF GRAVITATIONAL CLUSTERING

We present the results of a series of one-dimensional N-body and hydrodynamical simulations which have been used for testing the different clustering properties of baryonic and dark matter in an expanding background. Initial Gaussian random density perturbations with a power-law spectrum $P(k) \propto k^n$ are assumed. We analyse the distribution of density fluctuations and thermodynamical quantities for different spectral indices $n$ and discuss the statistical properties of clustering in the corresponding simulations. At large scales the final distribution of the two components is very similar while at small scales the dark matter presents a lumpiness which is not found in the baryonic matter. The amplitude of density fluctuations in each component depends on the spectral index $n$ and only for $n=-1$ the amplitude of baryonic density fluctuations is larger than that in the dark component. This result is also confirmed by the behaviour of the bias factor, defined as the ratio between the r.m.s of baryonic and dark matter fluctuations at different scales: while for $n=1,\ 3$ it is always less than unity except at very large scales where it tends to one, for $n=-1$ it is above 1.4 at all scales. All simulations show also that there is not an exact correspondence between the positions of largest peaks in dark and baryonic components, as confirmed by a cross-correlation analysis. The final temperatures depend on the initial spectral index: the highest values are obtained for $n=-1$ and are in proximity of high density regions.Comment: 7 pages Latex (MN style) + 10 figures in postscript files, uuencoded submitted to MNRA

Estimating Macroeconomic Models: A Likelihood Approach

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility.

Non-Iterative, Feature-Preserving Mesh Smoothing

With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.Singapore-MIT Alliance (SMA