A5019865926- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Computational Physics and Python Applications
- Cosmology and Gravitation Theories
- Neutrino Physics Research
- Distributed and Parallel Computing Systems
- Radiation Detection and Scintillator Technologies
- Medical Imaging Techniques and Applications
- Atomic and Subatomic Physics Research
- Advanced Data Storage Technologies
- Big Data Technologies and Applications
- Particle Accelerators and Free-Electron Lasers
- Superconducting Materials and Applications
- Black Holes and Theoretical Physics
- Digital Radiography and Breast Imaging
- Nuclear Physics and Applications
- Gamma-ray bursts and supernovae
- Advanced X-ray and CT Imaging
- COVID-19 diagnosis using AI
- Nuclear physics research studies
- Laser-Plasma Interactions and Diagnostics
- Aerodynamics and Acoustics in Jet Flows
SLAC National Accelerator Laboratory
2019-2025
University of Colorado System
2024-2025
University of Colorado Boulder
2024-2025
Northern Illinois University
2023-2024
A. Alikhanyan National Laboratory
2024
Atlas Scientific (United States)
2024
European Organization for Nuclear Research
2023
Rutherford Appleton Laboratory
2023
The University of Adelaide
2022-2023
University of British Columbia
2016-2019
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range modern machine learning approaches. Unlike most methods they rely low-level input, for instance calorimeter output. While their network architectures are vastly different, performance is comparatively similar. In general, find that these new approaches extremely powerful and great fun.
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by an ordered sequence constituents as training inputs. Unlike the majority previous approaches, strategy does not result in loss information during pixelisation calculation high level features. The classification method...
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at Large Hadron Collider (LHC). Recent Deep Learning developments this area include treatment calorimeter activation as an image or supplying a list jet constituent momenta to fully connected network. This latter approach lends itself well use Recurrent Neural Networks. In work applicability architectures incorporating Long Short-Term Memory (LSTM)...
We report on the mass measurements of several neutron-rich $\mathrm{Rb}$ and $\mathrm{Sr}$ isotopes in $A\ensuremath{\approx}100$ region with TITAN Penning-trap spectrometer. By using highly charged ions charge state $q=10+$, masses $^{98,99}\mathrm{Rb}$ $^{98--100}\mathrm{Sr}$ have been determined a precision 6--12 keV, making their uncertainty negligible for $r$-process nucleosynthesis network calculations. The $^{101}\mathrm{Sr}$ has directly first time eight times higher than previous...