arXiv subjects

A. Maevskiy

Publications and source records attributed to A. Maevskiy.

4 recordsLinked to original sources

arXiv2025

Inverse Design of Broadband Antennas for Terahertz Devices Based on 2D Materials

Terahertz (THz) technology, a cornerstone of next-generation high-speed communication and sensing, has long been hindered by impedance mismatch challenges that limit device performance and applicability. These challenges become particularly pronounced when ultrasensitive two-dimensional (2D) materials are employed as the device substrate in the THz range, further complicating their integration into real-world applications. Furthermore, conventional antenna designs often fail to provide adequate matching across the extensive THz spectrum. In this work, we tackle these challenges using a procedural generation algorithm to design THz broadband antennas that satisfy specific performance criteria. Namely, the developed inverse design methodology enables customization for the target impedance value, bandwidth, and contact topology requirements. The proposed antenna achieves an improvement of up to 40\% in power transfer efficiency compared to traditional bow-tie antennas under realistic operating conditions. High-fidelity electromagnetic simulations validate these results, confirming the design's practicality for THz applications. This work addresses critical limitations of existing antenna designs and advances the feasibility of high-frequency applications in both communication and sensing.

physics.app-ph↗

arXiv2024

Local-time formula for dissipation in solid ionic electrolytes

When ions move through solids, they interact with the solid's constituent atoms and cause them to vibrate around their equilibrium points. This vibration, in turn, modifies the potential landscape through which the mobile ions travel. Because the present-time potential depends on past interactions, the coupling is inherently non-local in time, making its numerical and analytical treatment challenging. For sufficiently slow-moving ions, we linearize the phonon spectrum to show that these non-local effects can be ignored, giving rise to a drag-like force. Unlike the more familiar drag coefficient in liquids, the drag takes on a matrix form due to the crystalline structure of the framework. We numerically simulate trajectories and dissipation rates using both the time-local and non-local formulas to validate our simplification. The time-local formula dramatically reduces the computational cost of calculating the motion of mobile particle through a crystalline framework and clearly connects the properties of the material to the drag experienced by the particle.

cond-mat.mtrl-sci↗

arXiv2022

Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector

The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant amount of computing resources to run. Often this may not be afforded and less resource-intensive approaches are desired. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as the basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works more than an order of magnitude faster compared to the detailed simulation without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared.

physics.ins-det↗

arXiv2021

Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks

High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adversarial Network - a deep learning technique allowing for implicit estimation of the population distribution for a given set of objects. This approach lets us learn and then sample from the distribution of raw detector responses, conditioned on the parameters of the charged particle tracks. To evaluate the quality of the proposed model, we integrate a prototype into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator, with a speed-up of at least an order of magnitude. The prototype is trained on the responses from the inner part of the detector and, once expanded to the full detector, should be ready for use in physics tasks.

physics.ins-det↗