Interrelation of equivariant Gaussian processes and convolutional neural networks

Currently there exists rather promising new trend in machine leaning (ML) based on the relationship between neural networks (NN) and Gaussian processes (GP), including many related subtopics, e.g., signal propagation in NNs, theoretical derivation of learning curve for NNs, QFT methods in ML, etc. An important feature of convolutional neural networks (CNN) is their equivariance (consistency) with respect to the symmetry transformations of the input data. In this work we establish a relationship between the many-channel limit for CNNs equivariant with respect to two-dimensional Euclidean group with vector-valued neuron activations and the corresponding independently introduced equivariant Gaussian processes (GP).Comment: 5 pages. Presented at the ACAT 2021: 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Daejeon, Kr, 29 Nov - 3 Dec 202

Application of convolutional neural networks for data analysis in TAIGA-HiSCORE experiment

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. TAIGA-HiSCORE data provides an opportunity to reconstruct shower characteristics, such as shower energy, direction of arrival, and axis coordinates. The main idea of the work is to apply convolutional neural networks to analyze HiSCORE events, considering them as images. The distribution of registration times and amplitudes of events recorded by HiSCORE stations is used as input data. The paper presents the results of using convolutional neural networks to determine the characteristics of air showers. It is shown that even a simple model of convolutional neural network provides the accuracy of recovering EAS parameters comparable to the traditional method. Preliminary results of air shower parameters reconstruction obtained in a real experiment and their comparison with the results of traditional analysis are presented

Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks

Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between the events caused by gamma rays and by hadrons and to infer the parameters of the event such as the energy of the primary particle. We use convolutional neural networks (CNNs) to analyze Monte Carlo-simulated images from the telescopes of the TAIGA experiment. The analysis includes selection of the images corresponding to the showers caused by gamma rays and estimating the energy of the gamma rays. We compare performance of the CNNs using images from a single telescope and the CNNs using images from two telescopes as inputs.Comment: In Proceedings of 5th International Workshop on Deep Learning in Computational Physics (DLCP2021), 28-29 June, 2021, Moscow, Russi

Vector Leptoquark Pair Production in e+e−e^+e^- Annihilation

The cross section for vector leptoquark pair production in $e^+e^-$ annihilation is calculated for the case of finite anomalous gauge boson couplings $\kappa_{\gamma, Z}$ and $\lambda_{\gamma, Z}$. The minimal cross section is found to behave $\propto \beta^7$, leading to weaker mass bounds in the threshold range than in models studied previously.Comment: 10 pages Latex, including 5 eps-figure

Leptoquark Pair Production in \gamma\gamma Scattering: Threshold Resummation

The possibilities to pair-produce leptoquarks in photon-photon collisions are discussed. QCD threshold corrections lead to a strong enhancement of the production cross section. Suitably long-lived leptoquarks (\Gamma_\Phi \lsim 100 \MeV) may form Leptoquarkonium states.Comment: 7 pages LATEX, 2 eps figures. Contribution to the Proceedings of the \gamma \gamma Workshop, 200

Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes

High-energy particles hitting the upper atmosphere of the Earth produce extensive air showers that can be detected from the ground level using imaging atmospheric Cherenkov telescopes. The images recorded by Cherenkov telescopes can be analyzed to separate gamma-ray events from the background hadron events. Many of the methods of analysis require simulation of massive amounts of events and the corresponding images by the Monte Carlo method. However, Monte Carlo simulation is computationally expensive. The data simulated by the Monte Carlo method can be augmented by images generated using faster machine learning methods such as generative adversarial networks or conditional variational autoencoders. We use a conditional variational autoencoder to generate images of gamma events from a Cherenkov telescope of the TAIGA experiment. The variational autoencoder is trained on a set of Monte Carlo events with the image size, or the sum of the amplitudes of the pixels, used as the conditional parameter. We used the trained variational autoencoder to generate new images with the same distribution of the conditional parameter as the size distribution of the Monte Carlo-simulated images of gamma events. The generated images are similar to the Monte Carlo images: a classifier neural network trained on gamma and proton events assigns them the average gamma score 0.984, with less than 3% of the events being assigned the gamma score below 0.999. At the same time, the sizes of the generated images do not match the conditional parameter used in their generation, with the average error 0.33