The lightcurve reconstruction method for measuring the time delay of gravitational lens systems

We propose a new technique to measure the time delay of radio-loud gravitational lens systems, which does not rely on the excessive use of interferometric observations. Instead, the method is based on single-dish flux density monitoring of the (unresolved) lens system's total lightcurve, combined with additional interferometric measurements of the flux density ratio at a few epochs during that monitoring period. The basic idea of the method is to reconstruct the individual image lightcurves from the observed total lightcurve by assuming a range of potential values for the time delay and the magnification ratio of the images. It is then possible to single out the correct reconstruction, and therefore determine the time delay, by checking the consistency of the reconstructed individual lightcurves with the additional interferometric observations. We performed extensive numerical simulations of synthetic lightcurves to investigate the dependence of the performance of this method on various parameters which are involved in the problem. Probably the most promising candidates for applying the method (and also for determining the Hubble constant) are lens systems consisting of multiply imaged compact sources and an Einstein ring, such as B0218+357 from which some of the parameters used for our simulations were adopted.Comment: 26 pages, LaTex, including 23 figures; submitted to Monthly Notices of the Royal Astronomical Society; a version with a higher quality for some of the figures is available at http://www.mpa-garching.mpg.de/Lenses/Preprints/LightCrv.ps.g

Magnetic Field induced Dimensional Crossover Phenomena in Cuprate Superconductors and their Implications

We discuss the occurrence of crossing points in the magnetization - temperature $(m,T$) plane within the framework of critical phenomena. It is shown that in a two-dimensional superconducting slab of thickness $d_{s}$ $m_{z}(\delta)$ versus temperature $T$ curves measured in different fields $\mathbf{H} = H(0,\sin (\delta) ,\cos (\delta))$ will cross at the critical temperature T_c of the slab. In contrast, in a 3D anisotropic bulk superconductor the crossing point occurs in the plot $m_{z}(\delta) /H_{z}^{1/2}$ versus $T$. The experimental facts that 2D crossing point features have been observed in ceramics and in single crystals for $\mathbf{H}$ close to $\mathbf{H} = H(0,0,1)$, but not for $\mathbf{H} = H(0,1,0)$, is explained in terms of an angle-dependent crossover field separating the regions where 2D or 3D thermal fluctuations dominate. The measured 2D-crossing point data are used to estimate one of the fundamental parameters of cuprate superconductors, the minimum thickness of the slab $(d_{s})$, which remains superconducting. Our estimates, based on experimental 2D-crossing point data for single crystals, reveal that this length adopts material dependent values. Therefore, experimental data for T_c and $\lambda_{\Vert}^{2}(T=0)$, plotted in terms of T_c versus $1/\lambda_{\Vert}^{2}(T=0)$ will not tend to a straight line with universal slope as the underdoped limit is approached. Implications for magnetic torque measurements are also worked out

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201

Schulisches Lernen im sozialökologischen Kontext

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