A5101928767- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Particle Detector Development and Performance
- Dark Matter and Cosmic Phenomena
- Cosmology and Gravitation Theories
- Computational Physics and Python Applications
- Neutrino Physics Research
- Astrophysics and Cosmic Phenomena
- Black Holes and Theoretical Physics
- Radiation Detection and Scintillator Technologies
- Anomaly Detection Techniques and Applications
- Atomic and Subatomic Physics Research
- Nuclear reactor physics and engineering
- Distributed and Parallel Computing Systems
- Nuclear physics research studies
- Semiconductor materials and devices
- Particle Accelerators and Free-Electron Lasers
- Medical Imaging Techniques and Applications
- Noncommutative and Quantum Gravity Theories
- Advancements in Semiconductor Devices and Circuit Design
- Gamma-ray bursts and supernovae
- Data-Driven Disease Surveillance
- Radiation Therapy and Dosimetry
- Optical properties and cooling technologies in crystalline materials
University of Rochester
2023-2025
National Research University Higher School of Economics
2024-2025
European Organization for Nuclear Research
2022-2025
Institute of High Energy Physics
2020-2024
A. Alikhanyan National Laboratory
2022-2024
University of Antwerp
2024
Moscow Engineering Physics Institute
2017-2023
Purdue University West Lafayette
2023
Purdue University Northwest
2023
P.N. Lebedev Physical Institute of the Russian Academy of Sciences
2023
Within the framework of CALICE collaboration, our group has characterized Silicon Photomultipliers (SiPMs) from various producers, in order to enhance single cell performances a highly granular analog hadron calorimeter, with particular emphasis on improving linearity response, ensuring environmental stability, calibration portability and reducing parameters spread among different channels. As an outcome, new plastic scintillator tiles coupled KETEK PM1125 SMD SiPM have been commissioned,...
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle acquisition problems avoid loss. In this study, we present semi-supervised spatio-temporal anomaly detection (AD) physics reading channels of Calorimeter (HCAL) CMS using three-dimensional digi-occupancy map DQM. We propose GraphSTAD system, which...
A dedicated single-cell SiPM structure is designed and measured to investigate the radiation damage effects on gain turn-off voltage of SiPMs exposed a reactor neutron fluence up Φ = 5e13 cm−2. The cell has pitch 15μm. dependence are reported. reduction by 19% an increase Voff ≈0.5 V observed after
Identifying outlier behavior among sensors and subsystems is essential for discovering faults facilitating diagnostics in large systems. At the same time, exploring systems with numerous multivariate data sets challenging. This study presents a lightweight interconnection divergence discovery mechanism (LIDD) to identify abnormal multi-system environments. The approach employs analysis technique that first estimates similarity heatmaps each system then applies information retrieval...
The proliferation of sensors brings an immense volume spatio-temporal (ST) data in many domains for various purposes, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process large data, making it challenging expensive to deploy analytics platforms new environments. Transfer learning (TL) mechanisms promise mitigate sparsity model complexity by utilizing pre-trained models task. Despite the triumph TL fields like computer vision natural...
The ever-increasing detector complexity at CERN triggers a call for an increasing level of automation. Since the quality collected physics data hinges on components time data-taking, rapid identification and resolution system anomalies will result in better amount high-quality particle data. Therefore, this study proposes CGVAE, data-driven unsupervised anomaly detection using deep learning model, monitoring from multivariate series sensor CGVAE model is composed variational autoencoder with...
Predictive maintenance is essential for complex industrial systems to foresee anomalies before major system faults or ultimate breakdown. However, the existing efforts on Industry 4.0 predictive monitoring are directed at semi-supervised anomaly detection with limited robustness large systems, which often accompanied by uncleaned and unlabeled data. We address challenge of predicting through data-driven end-to-end deep learning models using early warning symptoms multivariate time series...
The compact muon solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the large hadron collider (LHC) CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle acquisition problems avoid loss. In this study, we present semi-supervised spatio-temporal anomaly detection (AD) physics reading channels of hadronic calorimeter (HCAL) CMS using three-dimensional digi-occupancy map DQM. We propose GraphSTAD system, which...
A dedicated single-cell SiPM structure is designed and measured to investigate the radiation damage effects on gain turn-off voltage of SiPMs exposed a reactor neutron fluence up $\Phi$ = 5e13 cm$^{-2}$. The cell has pitch 15 $\mu$m. dependence are reported. reduction by 19% an increase $V_{off}$ $\approx$0.5 V observed after