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Regular version of the site
Of all publications in the section: 3
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Article
Kornilov M.V. Astronomy and Computing. 2016. Vol.  16. P. 131-139.

A concept of the ground-based optical astronomical observation efficiency is considered in this paper. We believe that a telescope efficiency can be increased by properly allocating observation tasks with respect to the current environment state and probability to obtain the data with required properties under the current conditions. An online observations scheduling is assumed to be an essential part for raising the efficiency. The short-term online scheduling is treated as the discrete optimisation problems which are stated using several abstraction levels. The optimisation problems are solved using the parallel depth-bounded discrepancy search (PDDS) algorithm by Moisan et al. (2014). Some aspects of the algorithm performance are discussed. The presented algorithm is a core of open-source chelyabinsk C++ library which is planned to be used at 2.5 m telescope of Sternberg Astronomical Institute of Lomonosov Moscow State University.

Added: Feb 7, 2019
Article
Kornilov M., Malanchev K. Astronomy and Computing. 2019. Vol. 26. P. 61-67.

FITS (Flexible Image Transport System) is a common format for astronomical data storage. It was first standardised in the early 1980s. Even though astronomical data is now processed mostly using software, visual data inspection by a human is still important during equipment or software commissioning and while observing. We present Fips, a cross-platform FITS file viewer open source software. To the best of our knowledge, it is for the first time that the image rendering algorithms are implemented mostly on GPU (graphics processing unit). We show that it is possible to implement a fully-capable FITS viewer using OpenGL interface. We also emphasise the advantages of using GPUs for efficient image handling.

Added: Feb 7, 2019
Article
Dobryakov S., Malanchev K., Derkach D. et al. Astronomy and Computing. 2021. Vol. 35. P. 1-10.

We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during training. Despite being trained on a relatively small sample, the method shows good results on real data from the Open Supernovae Catalog. We also investigate model transfer from the PLAsTiCC simulations train dataset to real data application, and the reverse, and find the performance significantly decreases in both cases, highlighting the existing differences between simulated and real data.

Added: Apr 20, 2021