699 citations · 2.7k across the 97 of their papers we have counts for
101 papers
Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
Fedor Buzaev, Dmitry Efremenko, Egor Bugaev +4
Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training. However, their performance suffers fro…
Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov +3
The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-dr…
Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
Egor Bugaev, Fedor Buzaev, Dmitry Efremenko +2
This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models. Building…
ML-based Fast Simulation of FARICH Responses
Foma Shipilov, Alexander Barnyakov, Artem Ivanov +1
A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern particle physics simulation sof…
RL-ABC: Reinforcement Learning for Accelerator Beamline Control
Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin +2
Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning f…
Production of Λ hyperons in 4.0A GeV and 4.5A GeV carbon-nucleus interactions at the Nuclotron
S. Afanasiev, G. Agakishiev, A. Aleksandrov +207
The BM@N experiment (Baryonic Matter at the Nuclotron) is the first fixed-target experiment at the JINR NICA accelerator complex. In this work, data on the interactions of a carbon…
Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin +3
Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task…
Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
Valeriya Kirova, Dzerassa Kadieva, Daniil Vlasenko +2
We analyze functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP) to match brain activities during a range of cognitive tasks. Our findings demon…
Approach to Finding a Robust Deep Learning Model
Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the im…
Production of protons, deuterons and tritons in argon-nucleus interactions at 3.2 AGeV
S. Afanasiev, G. Agakishiev, A. Aleksandrov +200
Results of the BM@N experiment at the Nuclotron/NICA complex on the production of protons, deuterons and tritons in interactions of an argon beam of 3.2 AGeV with fixed targets of…
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
Anwar Ibrahim, Denis Derkach, Alexey Petrenko +2
Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods ar…
Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning
S. Ali, A. S. Ryzhikov, D. A. Derkach +2
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…
Technical Design Report of the Spin Physics Detector at NICA
The SPD Collaboration, V. Abazov, V. Abramov +411
The Spin Physics Detector collaboration proposes to install a universal detector in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the…
The BM@N spectrometer at the NICA accelerator complex
S. Afanasiev, G. Agakishiev, E. Aleksandrov +208
BM@N (Baryonic Matter at Nuclotron) is the first experiment operating and taking data at the Nuclotron/NICA ion-accelerating complex.The aim of the BM@N experiment is to study inte…
What Machine Learning Can Do for Focusing Aerogel Detectors
Foma Shipilov, Alexander Barnyakov, Viktor Bobrovnikov +2
Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector locatio…
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
Max Aehle, Lorenzo Arsini, R. Belén Barreiro +27
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models…
SND@LHC: The Scattering and Neutrino Detector at the LHC
LHC Collaboration
SND@LHC is a compact and stand-alone experiment designed to perform measurements with neutrinos produced at the LHC in the pseudo-rapidity region of . The experimen…
GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response
Alexander Rogachev, Fedor Ratnikov
High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation res…
Generative Surrogates for Fast Simulation: TPC Case
Fedor Ratnikov, Artem Maevskiy, Alexander Zinchenko +3
Simulation of High Energy Physics experiments is widely used, necessary for both detector and physics studies. Detailed Monte-Carlo simulation algorithms are often limited due to t…
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach
Arsenii Gavrikov, Yury Malyshkin, Fedor Ratnikov
Large-scale detectors consisting of a liquid scintillator target surrounded by an array of photo-multiplier tubes (PMTs) are widely used in the modern neutrino experiments: Borexin…
On Scaling of Hall-Effect Thrusters Using Neural Nets
Yegor V. Plyashkov, Andrey A. Shagayda, Dmitrii A. Kravchenko +2
Hall-effect thrusters (HETs) are widely used for modern near-earth spacecraft propulsion and are vital for future deep-space missions. Methods of modeling HETs are developing rapid…
Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector
A. Maevskiy, F. Ratnikov, A. Zinchenko +3
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…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment
SHiP Collaboration
In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion…