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--- dlcp2023:abstracts [14/06/2023 21:21] – [44. Определение особых точек кривой накопления флуоресценции методами машинного обучения без учителя] stepanova
+++ dlcp2023:abstracts [05/03/2025 17:33] (current) – ↷ Links adapted because of a move operation 156.59.198.135
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 ====== Book of Abstracts ======
-//Draft June5, 2023 //
+{{ dlcp2023:dlcp2023-abstracts.pdf |Book of Abstracts}}
 ===== Plenary reports =====
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+Imaging Atmospheric Cherenkov Telescope (IACT) capture images of extensive air showers (EAS) generated by gamma rays and cosmic rays (charged particles) as they interact with the atmosphere. The much more frequent events associated with charged particles form the main background in the search for sources of gamma radiation, and therefore the sensitivity of IACTs to a large extent depends on the ability to distinguish between these two types of events. A conclusion about the properties of a primary high-energy particle can be drawn from the images in the telescope camera of the EAS which it initiated. In addition to classification (gamma or charged particle), its properties, such as energy and direction of arrival, can be estimated.
+Recently, along with the previously developed special methods of appropriate data processing to identify events with gamma rays and determine their parameters, methods based on deep learning have been successfully applied. These techniques, as applied to the analysis of IACT data, are the main subject of this  survey report. After very short background information on Cherenkov telescopes and methods for analyzing their data without using deep learning, we discuss the existing deep learning methods for analyzing IACT data, both for classifying the types of detected particles and background rejection, as well as for determining EAS parameters. Showers initiated by gamma rays accounts for an insignificant fraction of the total number of observed EASs, while the background of showers induced by cosmic rays (charged particles) predominates. Therefore, the problem of effective background rejection by deep learning methods is extremely important and is discussed in details. Obtaining the parameters of the reconstructed gamma-ray events, such as the energy and direction of arrival, is the central task of this entire area of research, since it allows one to obtain information about the physical processes in the sources of these gamma rays and, as a result, about the important physical processes in the Universe. We also consider the possibility of using generative neural networks for fast modeling of images of gamma and proton events in IACT cameras, which can significantly speed up and improve the efficiency of experimental data processing. In addition, we shortly discuss two specialized free software packages developed for analyzing IACT data using deep learning methods (available on git-sites). The CTLearn software provides a backend for training neural networks to reconstruct IACT events using TensorFlow. The goal of the GammaLearn project is to find and build optimal neural networks for classifying gamma/cosmic rays and reconstructing gamma-ray parameters in combination with user tools for their easy launch and suitability for various experiments. In the conclusions the main  learnings from the works considered in the review are presented.
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 A.Vlaskina (SINP MSU, Moscow), A.Demichev (SINP MSU, Moscow), Ju.Dubenskaya (SINP MSU, Moscow), E.Gres (IGU, Irkutsk), S.Polyakov (SINP MSU, Moscow), D.Zhurov (IGU, Irkutsk)
+The paper presents preliminary results on determining the direction of the EAS axis in experiments representing an array of Cherenkov detectors. An example of such a facility is the HiSCORE facility deployed near Baikal lake as part of the TAIGA experiment. Two approaches have been considered. One is based on the representation of the time of registration of signal arrival by stations in the form of an image, which is processed using a convolutional network. Another approach is to allocate a subset of the same number of triggered stations in each events, which includes data on the location of stations relative to each other and the relative time of signal registration. The analysis is performed using a fully connected deep network. It was shown in the work that both approaches give approximately the same accuracy. In the future, we propose to optimize the architecture of both networks and the process of their training to improve the accuracy of predicting EAS parameters.
+The work was supported by RSF, grant no.22-21-00442. The work was done using the data of UNU "Astrophysical Complex of MSU-ISU» (agreement EB-075-15-2021-675)
 ==== 27. The use of conditional variational autoencoders for simulation of EASs images from IACTs ====
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 //Poster//
-__V.Kalninsky__ (SPbSU, St.-Petersburg)
+__V.Kalnitsky__ (SPbSU, St.-Petersburg)
 The problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We have proposed an approximation of the signum function by a smooth spline. We are controlling the difference between the soft connective and the associative connective. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks.