Machine learning (ML) methods began to be used in the MLIT laboratory from the very beginning of its organization in 1966, when one of the main tasks of the LVTA was the automation of film data processing used at that time in physics experiments. This included the problems of automating film measurements and calibration of the then-built scanning machines Spiral Reader and AELT (Automat on...
Different aspects of deep learning applications in the collider physics will be discussed in the talk. The main topic of the talk is the methodology of data analysis optimizations with deep neural networks. Short overview of the methods to search for "new physics" with neural network technique will be presented.
The GRAPES-3 experiment located in Ooty consists of a dense array of 400 plastic scintillator detectors spread over an area of 25,000 $m^2$ and a large area (560 $m^2$) tracking muon telescope. Everyday, the array records about 3 million showers in the energy range of 1 TeV - 10 PeV induced by the interaction of primary cosmic rays in the atmosphere. These showers are reconstructed in order to...
Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical complex allow to observe high energy gamma radiation helping to study many astrophysical objects and processes. TAIGA-ACT enables us to select gamma quanta from the total cosmic radiation flux and recover their primary parameters, such as energy and direction of arrival. The traditional method of processing the resulting...
The TAIGA experimental complex is a hybrid observatory for high-energy gamma-ray astronomy in the range from 10 TeV to several EeV. The complex consists of such installations as TAIGA-IACT, TAIGA-HiSCORE and a number of others. The TAIGA-HiSCORE facility is a set of wide-angle synchronized stations that detect Cherenkov radiation scattered over a large area. With TAIGA-HiSCORE data provides an...
Monte Carlo method is commonly used to simulate Cherenkov telescope images of atmospheric events caused by high-energy particles. We investigate the possibility of augmentation the Monte Carlo-generated sets using other methods. One of these methods is variational autoencoders.
We trained conditional variational autoencoders (CVAE) using a set of Monte Carlo-generated images from one...
Currently, generative adversarial networks (GANs) are a promising tool for image generation in the astronomy domain. Of particular interest are conditional GANs (CGANs), which allow you to divide images into several classes according to the value of some property of the image, and then specify the required class when generating images. In the case of images from Imaging Atmospheric Cherenkov...
The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment under construction with a broad physics program. The main goals of JUNO are the determination of the neutrino mass ordering and the high precision measurement of neutrino oscillation properties. High quality reconstruction of reactor neutrino energy is crucial for the success of the experiment.
The JUNO detector...
Particle tracking is an essential part of any high-energy physics experiment. Well-known tracking algorithms based on the Kalman filter are not scaling well with the amounts of data being produced in modern experiments. In our work we present a particle tracking approach based on deep neural networks for the BM@N experiment and future SPD experiment. We have already applied similar approaches...
Taking into account that, at a Higgs boson mass of 125 GeV, the probability of its decay into bb is greater than the sum of the probabilities of all other decay channels, this channel makes a great contribution to the study of the Higgs boson. A more suitable channel for the production of the Higgs boson for studying it in bb decay is associative production with a vector boson. It was in this...
Machine Learning methods are wildly used for particle identification (PID) in experimental high energy physics nowadays. Particle identification plays an important role in high-energy physics analysis therefore determines the success of the performing an experiment. This determines importance of using machine learning to the PID problem. This report gives a preliminary status of application of...
During the experiment, 9 water bodies located in the Pskov region were studied: the pond of the Mirozhka River, the delta of the Velikaya River, the Kamenka River, lakes Kalatskoye, Teploe, Lesitskoye, Tiglitsy, Chudskoye (Peipsi), Pskovskoye. Water samples with phytoplankton were taken from each water body, and toxicants (CdSO$_4$ or K$_2$Cr$_2$O$_7$) were added at a concentration of 20 μM...
Most modern machine learning models are known as black-box models. By default, these predictors don't provide an explanation as to why a certain event or example has been assigned a particular class or value. Model explainability methods aim to interpret the decision-making process of a black-box model and present it in a way that is easy for researchers to understand. These methods can...
