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Register a new userExpectation Maximization and the retrieval of the atmospheric extinction coefficients by inversion of Raman lidar data
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We consider the problem of retrieving the aerosol extinction coefficient from Raman lidar measurements. This is an ill--posed inverse problem that needs regularization, and we propose to use the Expectation--Maximization (EM) algorithm to provide stable solutions. Indeed, EM is an iterative algorithm that imposes a positivity constraint on the solution, and provides regularization if iterations are stopped early enough. We describe the algorithm and propose a stopping criterion inspired by a statistical principle. We then discuss its properties concerning the spatial resolution. Finally, we validate the proposed approach by using both synthetic data and experimental measurements; we compare the reconstructions obtained by EM with those obtained by the Tikhonov method, by the Levenberg-Marquardt method, as well as those obtained by combining data smoothing and numerical derivation.
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A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for optimal design of LiDAR scans and retrieval of the velocity statistical moments is proposed. The LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional spaces and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning LiDARs. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for optimal design of LiDAR experiments and efficient application of the LiSBOA for the post-processing of LiDAR measurements. The optimal design of LiDAR scans is formulated as a two cost-function optimization problem including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the LiDAR scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme.
The CTA is the next generation of ground based very high energy gamma ray Imaging Atmospheric Cherenkov Telescopes. Since observations with this technique are affected by atmospheric conditions, an accurate knowledge of the atmospheric properties is fundamental to improve the precision and duty cycle of the CTA. Measurements of absorption and scattering properties of the atmosphere due to aerosols and molecules can be used in the event reconstruction or in MODTRAN, an analytical code designed to model the propagation of electromagnetic radiation. MODTRAN output is used as an input for the air shower simulation and Cherenkov light production, giving the optical depth profiles that together with the refractive index allow the proper simulation of the gamma ray induced signals and a correct measurement of the primary energy from the detected signals. The ARCADE Raman Lidar will be used for the on site characterization of the aerosol attenuation profiles of the UV light. The collected data will be used in preparation for the full operation of the array, providing nightly information about the aerosol properties such as the vertical aerosol optical depth and the water vapour mixing ratio with an altitude resolution better than 100 m from about 400 m to 10 km above ground level. These measurements will help to define the needs for Monte Carlo simulations of the shower development and of the detector response. This instrument will also be used for the intercalibration of the future Raman Lidars that are expected to operate at the CTA sites. This contribution includes a description of the ARCADE Lidar and the characterization of the performance of the system. The system is expected to be shipped to the northern site of the CTA (La Palma) before the end of 2017, to acquire data locally for 1 year before being moved to the southern site (Chile).
The atmospheric turbulence is the main factor that influences quantum properties of propagating optical signals and may sufficiently degrade the performance of quantum communication protocols. The probability distribution of transmittance (PDT) for free-space channels is the main characteristics of the atmospheric links. Applying the law of total probability, we derive the PDT by separating the contributions from turbulence-induced beam wandering and beam-spot distortions. As a result, the obtained PDT varies from log-negative Weibull to truncated log-normal distributions depending on the channel characteristics. Moreover, we show that the method allows one to consistently describe beam tracking, a procedure which is typically used in practical long-distance free-space quantum communication. We analyze the security of decoy-state quantum key exchange through the turbulent atmosphere and show that beam tracking does not always improves quantum communication.
Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network. Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities. We evaluate our method on the (sequential) perceptual grouping task and find that it is able to accurately recover the constituent objects. We demonstrate that the learned representations are useful for next-step prediction.
Free-space quantum links have clear practical advantages which are unaccessible with fiber-based optical channels --- establishing satellite-mediated quantum links, communications through hardly accessible regions, and communications with moving objects. We consider the effect of the atmospheric turbulence on properties such as quadrature squeezing, entanglement, Bell nonlocality, and nonclassical statistics of photocounts, which are resources for quantum communications. Depending on the characteristics of the given channels, we study the efficiency of different techniques, which enable to preserve these quantum features---post-, pre-selection, and adaptive methods. Furthermore, we show that copropagation of nonclassically-correlated modes, which is used in some communication scenarios, has clear advantages in free-space links.
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