Taylor expansions and Castell estimates for solutions of stochastic differential equations driven by rough paths

We study the Taylor expansion for the solutions of differential equations driven by $p$-rough paths with $p>2$. We prove a general theorem concerning the convergence of the Taylor expansion on a nonempty interval provided that the vector fields are analytic on a ball centered at the initial point. We also derive criteria that enable us to study the rate of convergence of the Taylor expansion. Finally and this is also the main and the most original part of this paper, we prove Castell expansions and tail estimates with exponential decays for the remainder terms of the solutions of the stochastic differential equations driven by continuous centered Gaussian process with finite $2D~\rho-$variation and fractional Brownian motion with Hurst parameter $H>1/4$.Comment: Final version for publis

Inhomogeneous systematic signals in cosmic shear observations

We calculate the systematic errors in the weak gravitational lensing power spectrum which would be caused by spatially varying calibration (i.e. multiplicative) errors, such as might arise from uncorrected seeing or extinction variations. The systematic error is fully described by the angular two-point correlation function of the systematic in the case of the 2D lensing that we consider here. We investigate three specific cases: Gaussian, ``patchy'' and exponential correlation functions. In order to keep systematic errors below statistical errors in future LSST-like surveys, the spatial variation of calibration should not exceed 3% rms. This conclusion is independently true for all forms of correlation function we consider. The relative size the E- and B-mode power spectrum errors does, however, depend upon the form of the correlation function, indicating that one cannot repair the E-mode power spectrum systematics by means of the B-mode measurements.Comment: 8 pages, 3 figures. Changes reflect PRD published versio

Speckle from phase ordering systems

The statistical properties of coherent radiation scattered from phase-ordering materials are studied in detail using large-scale computer simulations and analytic arguments. Specifically, we consider a two-dimensional model with a nonconserved, scalar order parameter (Model A), quenched through an order-disorder transition into the two-phase regime. For such systems it is well established that the standard scaling hypothesis applies, consequently the average scattering intensity at wavevector _k and time t' is proportional to a scaling function which depends only on a rescaled time, t ~ |_k|^2 t'. We find that the simulated intensities are exponentially distributed, with the time-dependent average well approximated using a scaling function due to Ohta, Jasnow, and Kawasaki. Considering fluctuations around the average behavior, we find that the covariance of the scattering intensity for a single wavevector at two different times is proportional to a scaling function with natural variables mt = |t_1 - t_2| and pt = (t_1 + t_2)/2. In the asymptotic large-pt limit this scaling function depends only on z = mt / pt^(1/2). For small values of z, the scaling function is quadratic, corresponding to highly persistent behavior of the intensity fluctuations. We empirically establish a connection between the intensity covariance and the two-time, two-point correlation function of the order parameter. This connection allows sensitive testing, either experimental or numerical, of existing theories for two-time correlations in systems undergoing order-disorder phase transitions. Comparison between theory and our numerical results requires no adjustable parameters.Comment: 18 pgs RevTeX, to appear in PR

A simple method for the existence of a density for stochastic evolutions with rough coefficients

We extend the validity of a simple method for the existence of a density for stochastic differential equations, first introduced in [DebRom2014], by proving local estimate for the density, existence for the density with summable drift, and by improving the regularity of the density.Comment: Added a section with examples and application

Extended Kramers-Moyal analysis applied to optical trapping

The Kramers-Moyal analysis is a well established approach to analyze stochastic time series from complex systems. If the sampling interval of a measured time series is too low, systematic errors occur in the analysis results. These errors are labeled as finite time effects in the literature. In the present article, we present some new insights about these effects and discuss the limitations of a previously published method to estimate Kramers-Moyal coefficients at the presence of finite time effects. To increase the reliability of this method and to avoid misinterpretations, we extend it by the computation of error estimates for estimated parameters using a Monte Carlo error propagation technique. Finally, the extended method is applied to a data set of an optical trapping experiment yielding estimations of the forces acting on a Brownian particle trapped by optical tweezers. We find an increased Markov-Einstein time scale of the order of the relaxation time of the process which can be traced back to memory effects caused by the interaction of the particle and the fluid. Above the Markov-Einstein time scale, the process can be very well described by the classical overdamped Markov model for Brownian motion.Comment: 14 pages, 18 figure