On 2014 April 23, the Swift satellite responded to a hard X-ray transient detected by its Burst Alert Telescope, which turned out to be a stellar flare from a nearby, young M dwarf binary DGCVn. We utilize observations at X-ray, UV, optical, and radio wavelengths to infer the properties of two large flares. The X-ray spectrum of the primary outburst can be described over the 0.3-100 keV bandpass by either a single very high-temperature plasma or a nonthermal thick-target bremsstrahlung model, and we rule out the nonthermal model based on energetic grounds. The temperatures were the highest seen spectroscopically in a stellar flare, at T X of 290 MK. The first event was followed by a comparably energetic event almost a day later. We constrain the photospheric area involved in each of the two flares to be >10 20 cm 2 , and find evidence from flux ratios in the second event of contributions to the white light flare emission in addition to the usual hot, T∼10 4 K blackbody emission seen in the impulsive phase of flares. The radiated energy in X-rays and white light reveal these events to be the two most energetic X-ray flares observed from an M dwarf, with X-ray radiated energies in the 0.3-10 keV bandpass of 4×10 35 and 9×10 35 erg, and optical flare energies at E V of 2.8×10 34 and 5.2×10 34 erg, respectively. The results presented here should be integrated into updated modeling of the astrophysical impact of large stellar flares on close-in exoplanetary atmospheres.

ABSTRACT:This paper presents a new approach to simultaneous detection and tracking of vehicles moving through an intersection in aerial images acquired by an unmanned aerial vehicle (UAV). Detailed analysis of spatial and temporal utilization of an intersection is an important step for its design evaluation and further traffic inspection. Traffic flow at intersections is typically very dynamic and requires continuous and accurate monitoring systems. Conventional traffic surveillance relies on a set of fixed cameras or other detectors, requiring a high density of the said devices in order to monitor the intersection in its entirety and to provide data in sufficient quality. Alternatively, a UAV can be converted to a very agile and responsive mobile sensing platform for data collection from such large scenes. However, manual vehicle annotation in aerial images would involve tremendous effort. In this paper, the proposed combination of vehicle detection and tracking aims to tackle the problem of automatic traffic analysis at an intersection from visual data. The presented method has been evaluated in several real-life scenarios.

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