A5100747779- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Particle Detector Development and Performance
- Computational Physics and Python Applications
- Dark Matter and Cosmic Phenomena
- Cosmology and Gravitation Theories
- Neutrino Physics Research
- Medical Imaging Techniques and Applications
- Distributed and Parallel Computing Systems
- Advanced Data Storage Technologies
- Gamma-ray bursts and supernovae
- Black Holes and Theoretical Physics
- Atomic and Subatomic Physics Research
- Advanced Neural Network Applications
- Neural Networks and Applications
- Astrophysics and Cosmic Phenomena
- Nuclear reactor physics and engineering
- Radiation Detection and Scintillator Technologies
- Parallel Computing and Optimization Techniques
- International Science and Diplomacy
- Stellar, planetary, and galactic studies
- Astronomy and Astrophysical Research
- Explainable Artificial Intelligence (XAI)
- COVID-19 diagnosis using AI
National Institute of Chemical Physics and Biophysics
2014-2025
Institute of High Energy Physics
2022-2024
A. Alikhanyan National Laboratory
2022-2024
University of Antwerp
2024
California Institute of Technology
2020-2023
ETH Zurich
2017-2021
University of Tartu
2010
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced collisions recorded with complex detector systems. Two critical reconstruction charged particle trajectories tracking detectors showers calorimeters. These two have unique challenges characteristics, but both dimensionality, degree sparsity, geometric layouts....
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of event by combining information from calorimeters and trackers, significantly improving detector resolution for jets missing transverse momentum. planned high-luminosity upgrade CERN Large Hadron Collider (LHC), it is necessary revisit existing reconstruction algorithms ensure that both physics computational performance are sufficient in an environment with many...
We provide ingredients and recipes for computing neutrino signals of TeV-scale Dark Matter (DM) annihilations in the Sun. For each annihilation channel DM mass we present energy spectra neutrinos at production, including: state-of-the-art losses primary particles solar matter, secondary neutrinos, electroweak radiation. then after propagation to Earth, including (vacuum matter) flavor oscillations interactions matter. also a numerical computation capture rate These results are available form.
The observation of Higgs boson production in association with a top quark-antiquark pair is reported, based on combined analysis proton-proton collision data at center-of-mass energies √s = 7, 8, and 13 TeV, corresponding to integrated luminosities up 5.1, 19.7, 35.9 fb^(-1), respectively. were collected the CMS detector CERN LHC. results statistically independent searches for bosons produced conjunction decaying pairs W bosons, Z photons, τ leptons, or bottom quark jets are maximize...
Abstract We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard reconstructs stable particles calorimeter clusters and tracks to global event reconstruction that exploits combined information multiple detector subsystems, leading strong improvements quantities such as jets missing transverse energy. have studied possible evolution towards heterogeneous computing platforms GPUs using graph neural network. machine-learned PF model...
We study the impact of ambient fluid on evolution collapsing false vacuum bubbles by simulating dynamics a coupled bubble-particle system. A significant increase in mass particles across bubble wall leads to buildup those inside bubble. show that backreaction slows or even reverses collapse. Consequently, if true become heavier than vacuum, particle-wall interactions always decrease compactness can reach, making their collapse black holes less likely.
Our goal is to calculate the circular velocity curve of Milky Way, along with corresponding uncertainties that quantify various sources systematic uncertainty in a self-consistent manner. The observed rotational velocities are described as minus asymmetric drift. latter by radial axisymmetric Jeans equation. We thus reconstruct between Galactocentric distances from 5 kpc 14 using Bayesian inference approach. estimated error bars Sun's distance and spatial-kinematic morphology tracer stars....
Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at High-Luminosity Large Hadron Collider Future Circular Collider. We study scalable machine learning models for event reconstruction electron-positron collisions based on a full detector simulation. Particle-flow can be formulated as supervised task using tracks calorimeter clusters. compare graph neural network kernel-based transformer demonstrate that we avoid...
Abstract In the European Center of Excellence in Exascale Computing ”Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance resources to perform large-scale hyperparameter optimization using distributed training multiple compute nodes. is part RAISE’s data-driven use cases which leverages HPC cross-methods developed within project. response demand for parallelizable...
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at CERN LHC, has been a focus development light planned Phase-2 running conditions with an increased pileup detector granularity. In recent years, machine learned (MLPF) graph neural network that performs PF reconstruction, explored CMS, possible advantages directly optimizing for physical quantities interest, being highly...
Due to poor observational constraints on the low-mass end of subhalo mass function, detection dark matter (DM) subhalos sub-galactic scales would provide valuable information about nature DM. Stellar wakes, induced by passing DM subhalos, encode inducing perturber and thus serve as an indirect probe for substructure within Milky Way (MW). Our aim is assess viability performance deep learning searches stellar wakes in Galactic halo caused varying mass. We simulate massive objects (subhalos)...
Due to poor observational constraints on the low-mass end of subhalo mass function, detection dark matter (DM) subhalos sub-galactic scales would provide valuable information about nature DM. Stellar wakes, induced by passing DM subhalos, encode (properties) inducing perturber and thus serve as an indirect probe for substructure within Milky Way. Our aim is assess viability performance deep learning searches stellar wakes in Galactic halo caused varying mass. We simulated massive objects...
We study the impact of ambient fluid on evolution collapsing false vacuum bubbles by simulating dynamics a coupled bubble-particle system. A significant increase in mass particles across bubble wall leads to buildup those inside bubble. show that backreaction slows or even reverses collapse. Consequently, if true become heavier than vacuum, particle-wall interactions always decrease compactness can reach making their collapse black holes less likely.
Abstract We study scalable machine learning models for full event reconstruction in high-energy electron-positron collisions based on a highly granular detector simulation. Particle-flow (PF) can be formulated as supervised task using tracks and calorimeter clusters or hits. compare graph neural network kernel-based transformer demonstrate that both avoid quadratic memory allocation computational cost while achieving realistic PF reconstruction. show hyperparameter tuning supercomputer...
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at High-Luminosity Large Hadron Collider Future Circular Collider. We study scalable machine learning models for event reconstruction electron-positron collisions based on a full detector simulation. Particle-flow can be formulated as supervised task using tracks calorimeter clusters. compare graph neural network kernel-based transformer demonstrate that we avoid quadratic...