Comparison of the scintillation noise above different observatories measured with MASS instruments

Scintillation noise is a major limitation of ground base photometric precision. An extensive dataset of stellar scintillation collected at 11 astronomical sites world-wide with MASS instruments was used to estimate the scintillation noise of large telescopes in the case of fast photometry and traditional long-exposure regime. Statistical distributions of the corresponding parameters are given. The scintillation noise is mostly determined by turbulence and wind in the upper atmosphere and comparable at all sites, with slightly smaller values at Mauna Kea and largest noise at Tolonchar in Chile. We show that the classical Young's formula under-estimates the scintillation noise.The temporal variations of the scintillation noise are also similar at all sites, showing short-term variability at time scales of 1 -- 2 hours and slower variations, including marked seasonal trends (stronger scintillation and less clear sky during local winter). Some correlation was found between nearby observatories.Comment: Accepted for publication in Astronomy and Astrophysics, 14 pages, 11 figure

The statistics of the photometric accuracy based on MASS data and the evaluation of high-altitude wind

The effect of stellar scintillation on the accuracy of photometric measurements is analyzed. We obtain a convenient form of estimaton of this effect in the long exposure regime, when the turbulence shift produced by the wind is much larger than the aperture of the telescope. A simple method is proposed to determine index $S_3$ introduced by perture of the Kenyon et al. (2006), directly from the measurements with the Multi Aperture Scintillation Sensor (MASS) without information on vertical profile of the wind. The statistics $S_3$ resulting from our campaign of 2005 -- 2007 at Maidanak observatory is presented. It is shown that these data can be used to estimate high-altitude winds at pressure level 70 -- 100 mbar. Comparison with the wind speed retrieved from the NCEP/NCAR global models shows a good agreement. Some prospects for retrieval of the wind speed profile from the MASS measurements are outlined.Comment: 11 pages, 9 figures, accepted for publication in Astronomy Letter

Evaluation of integral exposure energy load on aural analyzer of miners

The individual exposure integral noise load on workers before the beginning of hearing impairment was determined for a group of 20 male miners who had worked with drilling equipment and harvesters for 8 to 20 years before the onset of the disability. Results show that the total exposure energy load of about 4 kw x h sq m, obtained by miners in the examined group, resulted in occupational injury to the auditory organ (cochlear neuritis) in 75% of the cases. The equivalent energy level of noise computed according to the date of total energy load is roughly 99 db A, which significantly exceeds the permissible amount of 85 db A. There is a correlation (r = 0.77) between the integral exposure energy noise on the aural analyzer in the degree of increase in the total threshold for the mean speech range

Accurate seeing measurements with MASS and DIMM

Astronomical seeing is quantified by a single parameter, turbulence integral, in the framework of the Kolmogorov turbulence model. This parameter can be routinely measured by a Differential Image Motion Monitor, DIMM. A new instrument, Multi-Aperture Scintillation Sensor (MASS), permits to measure the seeing in the free atmosphere above ~0.5km and, together with a DIMM, to estimate the ground-layer seeing. The absolute accuracy of both methods is studied here using analytical theory, numerical simulation, and experiments. A modification of the MASS data processing to compensate for partially saturated scintillation is developed. We find that the DIMM can be severely biased by optical aberrations (e.g. defocus) and propagation. Seeing measurements with DIMM and MASS can reach absolute accuracy of ~10% when their biases are carefully controlled. Pushing this limit to 1% appears unrealistic because the seeing itself is just a model-dependent parameter of a non-stationary random process.Comment: 13 pages, 14 figures. Accepted for publication in MNRA

Building extraction from satellite imagery using a digital surface model

In this paper, two approaches to building extraction from satellite imagery and height data obtained from stereo images or LIDAR are compared. The first approach consists of detecting high-rise objects in a digital surface model and then improving recognition accuracy using segmentation of spectral information. The second approach uses the U-Net convolutional neural network, which showed the best results for the extraction of objects from aerospace images on a number of large datasets. Extensive experiments were carried out to evaluate the dependence of the quality of U-Net-based building extraction on the different data types (including high-resolution satellite images and digital surface model data). Building extraction quality of the trained network was also evaluated on satellite images with different spatial resolutions. © 2018 CEUR-WS. All rights reserved