Development and initial operating characteristics of the 20 megawatt linear plasma accelerator facility

A 20-megawatt linear plasma accelerator facility, a steady flow, Faraday-type plasma accelerator facility for high velocity aerodynamic testing, was constructed, developed, and brought to an operational status. The accelerator has a 63.5-mm-square and 0.5-meter-long channel and utilizes nitrogen-seeded with 2 % mole fraction of cesium vapor. Modification of the original accelerator design characteristics and the improvements necessary to make the arc heater a suitable plasma source are described. The measured accelerator electrode current distribution and the electrode-wall potential distributions are given. The computed and the measured values are in good agreement. Measured pitot pressure indicates that an accelerator exit velocity of 9.2 km/sec, is obtained with 30 of the 36 electrode pairs powered and corresponds to a velocity increase to about 2 1/4 times the computed entrance velocity. The computed stagnation enthalpy at the accelerator exit is 92 MJ/kg, and the mass density corresponds to an altitude of about 58 km. The 92 MJ/kg stagnation enthalpy corresponds to a kinetic energy content at low temperature equivalent to a velocity of 13.6 km/sec

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA