Comparative Study of Acoustic Parameter Reconstruction by using Optimal Control Method and Inverse Scattering Approach

Reconstruction of acoustic parameter such as acoustic velocity considers as part of inverse problems for mathematical physics and reasonable reconstruction of this parameter will assist solving interrelated problem such as inversion and imaging which are popular in the field of seismic imaging. In this work, we studied and conducted a comparative study between two methods; the optimal control method and inverse scattering approach. In optimal control method we are using conjugate gradient method for reconstructing the desired acoustic parameter while for inverse scattering approach, we are introducing the application of Marchenko integral equation. Furthermore, the numerical results for both approaches are presented for one dimensional problem along with the analysis from this comparative study.

Analysis of aerosol pollution processes in the vicinity of the Irkutsk aluminum plant

The issues of assessing the pollution fields in the vicinity of industrial enterprises are discussed according to the monitoring studies of the snow cover. The formulations of problems of low-parameter reconstruction of concentration fields are considered on the basis of model descriptions of the processes of transport of impurities in the surface layer of the atmosphere. With regard to the Irkutsk aluminum plant, the results of studies of the pollution of its surroundings with aluminum are presented. Using the data of route observations, a numerical reconstruction of the fluoride content in the snow cover was carried out. The quality control of the results obtained is carried out by comparing the measured and calculated concentrations of impurities at the control points of observation.

GROUND AND SATELLITE MONITORING OF POLLUTION PROCESSES ISKITIM-LINEVSK INDUSTRIAL ZONE

The results of a numerical analysis of atmospheric pollution in the vicinity of the industrial site of the Chernorechensky cement plant (CCP) and the territory of Iskitim are presented. The research material was the results of sampling melted snow for 2019-20. The snow index (NDSI), calculated from high-resolution images from the Landsat and Sentinel satellites, was used as satellite data. Statistical relationships between ground-based and satellite observations are given. The general dynamics of changes in the impurity concentration in the snow and NDSI values are revealed. The concentration is calculated on the basis of low-parameter reconstruction models using ground-based measurements. For calculations and visualization, the means of the geographic information system, which was developed earlier, were used. These studies represent the basis for the development of a methodology for a comprehensive analysis of the process of atmospheric pollution using ground-based and satellite observations.

Application of machine learning algorithms in imaging Cherenkov and neutrino astronomy

Over the last decade, machine learning algorithms have become standard analysis tools in astroparticle physics, used by a variety of instruments and for an even larger variety of analyses. While a few characteristic patterns can be observed, the portability of established machine learning-based analysis chains from one experiment to another, remains challenging, as instrument-specific prerequisites and adjustments need to be addressed prior to the application. The use Boosted Decision Trees and other tree-based ensemble methods, has been established, but also recently been challenged by the overall success of Deep Neural Networks. Machine learning has been applied for particle selection and parameter reconstruction, as well as for the extraction of energy spectra. This paper aims at summarizing some of the most common approaches on the application of machine learning in astroparticle physics and at providing brief overview on how they have been applied in practice.