Деркач Денис Александрович

MPD physics performance studies in Bi+Bi collisions at √sNN = 9.2 GeV

2025 · ARTICLE · en

TheMulti-Purpose Detector (MPD) is one of the three experiments of the Nuclotron Ion Collider-fAcility (NICA) complex, which is currently under construction at the Joint Institute for Nuclear Research in Dubna. With collisions of heavy ions in the collider mode, the MPD will cover the energy range √sNN = 4 − 11 GeV to scan the high baryon-density region of the QCD phase diagram. With expected statistics of 50–100 million events collected during the first run, MPD will be able to study a number of observables, including measurements of light hadrons and (hyper)nuclei production, particle flow, correlations and fluctuations, have a first look at dielectron production, and modification of vector-meson properties in dense matter. In this paper, we present selected results of the physics feasibility studies for theMPD experiment in Bi+Bi collisions at √sNN = 9.2 GeV, the system considered as one of the first available at the NICA collider.

Conceptual design report of the Super Tau-Charm Facility: the accelerator

2025 · ARTICLE · en

Electron–positron colliders operating in the GeV center-of-mass range, or tau-charm energy region, have been proved to enable competitive frontier research due to several unique features. With the progress of high-energy physics in the last two decades, a new-generation Tau-Charm factory, called the Super Tau-Charm Facility (STCF), has been actively promoted by the particle physics community in China. STCF has the potential to address fundamental questions such as the essence of color confinement and the matter–antimatter asymmetry within the next decades. The main design goals of the STCF are a center-of-mass energy ranging from 2 to 7 GeV and a luminosity surpassing 5 × 1034 cm−2 s−1 that is optimized at a center-of-mass energy of 4 GeV, which is approximately 50 times that of the currently operating Tau-Charm factory—BEPCII. The STCF accelerator has two main parts: a double-ring collider with a crab-waist collision scheme and an injector that provides top-up injections for both electron and positron beams. As a typical third-generation electron–positron circular collider, the STCF accelerator faces many challenges in both accelerator physics and technology. In this paper, the conceptual design of the STCF accelerator complex is presented, including the ongoing efforts and plans for technological research and development, as well as the required infrastructure. The STCF project aims to secure support from the Chinese central government for its construction during the 15th Five-Year Plan (2026–2030).

Learning to hear broken motors: Signature-guided data augmentation for induction motor diagnostics

2025 · ARTICLE · en

The application of machine learning algorithms in the intelligent diagnosis of three-phase engine has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced machine learning techniques. In our study, we innovate by combining machine learning algorithms with a novel unsupervised anomaly generation methodology that takes into account the engine physics model. We propose Signature-Guided Data Augmentation, an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals. Guided by Motor Current Signature Analysis, our approach creates diverse and realistic anomalies without resorting to computationally intensive simulations. The proposed method is a novel training and data-augmentation framework. Our approach achieved 99% accuracy and 0.97 macro-F score for binary fault detection and 86% accuracy and 0.88 macro-F score for multiclass classification across varying loads and phases. This hybrid approach leverages the strengths of both supervised machine learning and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with wide industrial application. The findings highlight the potential of our approach to contribute significantly to the field of engine diagnostics, offering a robust and efficient solution for real-world applications.

RatanSunPy: A robust preprocessing pipeline for RATAN-600 solar radio observations data

2025 · ARTICLE · en

The advancement of observational technologies and software for processing and visualizing spectro-polarimetric microwave data obtained with the RATAN-600 radio telescope opens new opportunities for studying the physical characteristics of solar plasma at the levels of the chromosphere and corona. These levels remain some difficult to detect in the ultraviolet and X-ray ranges. The development of such methods allows for more precise investigation of the fine structure and dynamics of the solar atmosphere, thereby deepening our understanding of the processes occurring in these layers. The obtained data also can be utilized for diagnosing solar plasma and forecasting solar activity. However, using RATAN-600 data requires extensive data processing and familiarity with the RATAN-600. This paper introduces RatanSunPy, an open-source Python package developed for accessing, visualizing, and analyzing multi-band radio observations of the Sun from the RATAN-600 solar complex. The package offers comprehensive data processing functionalities, including direct access to raw data, essential processing steps such as calibration and quiet Sun normalization, and tools for analyzing solar activity. This includes automatic detection of local sources, identifying them with NOAA (National Oceanic and Atmospheric Administration) active regions, and further determining parameters for local sources and active regions. By streamlining data processing workflows, RatanSunPy enables researchers to investigate the fine structure and dynamics of the solar atmosphere more efficiently, contributing to advancements in solar physics and space weather forecasting.

The LHCb ultra-fast simulation option, Lamarr design and validation

2024 · ARTICLE · en

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. The evolution of technologies and techniques for simulation production is then mandatory to meet the upcoming needs for the analysis of most of the data collected by the LHCb experiment. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two order of magnitude speed-up of the simulation phase.

Overview and theoretical prospects for CKM matrix and CP violation from the UTfit Collaboration

2024 · ARTICLE · en

Deep Learning Approaches for LHCb ECAL Reconstruction

2024 · ARTICLE · en

Calorimeters are a crucial component for most detectors mounted on modern colliders. Their tasks include identifying and measuring the energy of photons and neutral hadrons, recording energetic hadronic jets, and contributing to the identification of electrons, muons, and charged hadrons. To fulfill these many tasks while keeping costs reasonable, the calorimeter construction requires good and thoughtful balancing with other components of the detector. Much harder operation conditions during LHC’s high luminosity Run 5 and beyond bring new technological and computational challenges. This requires optimization of technologies, layouts, readouts, reconstruction algorithms to achieve the best overall physics performance for the limited cost. In the traditional approach, the reconstruction of the physical objects in the calorimeter must be matched to the calorimetric showers simulation used. We present a deep learning-based approach to help utilize raw simulated calorimetric data of varying degrees of detail.

Astronomical Data Approximation Based on Neural Network Models

2024 · ARTICLE · en

In this study, we apply shallow neural networks, bayesian neural networks, and normalizing flows to approximate light curves of astronomical objects. The study shows that the approximation quality of the proposed methods outperform the existing ap- proaches based on Gaussian processes. We assess the quality of solution using two physics-motivated analyses: supernovae type Ia classification and bolometric intensity peak estimation. For both problems, convolutional neural networks are trained on ap- proximated light curves. The results show that the proposed methods help to improve the quality of supernovae type identification and increase the accuracy of the intensity peak estimation compared to the Gaussian processes model.

Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning

2024 · ARTICLE · en

In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein distance for loss calculation, this innovative approach requires a significantly lower number of events and resources to achieve high precision, minimizing absolute errors effectively. Our work extends the operational lifespan of calorimeters, thereby ensuring the accuracy and reliability of data in the long term, and is particularly beneficial for experiments where data integrity is crucial for scientific discovery.

Neutron Reconstruction in the BM@N Experiment Using Machine Learning

2024 · CHAPTER · en