A5009224085- Particle Accelerators and Free-Electron Lasers
- Particle accelerators and beam dynamics
- Magnetic confinement fusion research
- Scientific Computing and Data Management
- Software System Performance and Reliability
- Stochastic Gradient Optimization Techniques
- Superconducting Materials and Applications
- Sparse and Compressive Sensing Techniques
- Optimization and Search Problems
- Microwave and Dielectric Measurement Techniques
- Markov Chains and Monte Carlo Methods
- Atomic and Subatomic Physics Research
- Advanced NMR Techniques and Applications
- Prostate Cancer Diagnosis and Treatment
- Rare-earth and actinide compounds
- Physics of Superconductivity and Magnetism
- Particle Detector Development and Performance
- Advanced Data Storage Technologies
- Environmental Monitoring and Data Management
- Simulation Techniques and Applications
- Advanced Bandit Algorithms Research
- Business Process Modeling and Analysis
- Atomic and Molecular Physics
- Advanced Condensed Matter Physics
- Colorectal Cancer Screening and Detection
Brookhaven National Laboratory
2002-2022
RIKEN BNL Research Center
2002-2006
A new experiment is described to detect a permanent electric dipole moment of the proton with sensitivity $10^{-29}e\cdot$cm by using polarized "magic" momentum $0.7$~GeV/c protons in an all-electric storage ring. Systematic errors relevant are discussed and techniques address them presented. The measurement sensitive physics beyond Standard Model at scale 3000~TeV.
Abstract In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found U.S. Department of Veterans Affairs, largest integrated healthcare system United States, we developed an automated, personalized prediction model to support clinical decision-making process for localized prostate cancer patients. This method combines representative power deep learning and analytical...
Due to the sheer volume of data it is typically impractical analyze detailed performance an HPC application running at-scale. While conventional small-scale benchmarking and scaling studies are often sufficient for simple applications, many modern workflow-based applications couple multiple elements with competing resource demands complex inter-communication patterns which cannot easily be studied in isolation at small scale. This work discusses Chimbuko, a analysis framework that provides...
Stochastic Gradient Descent (SGD) is the most popular algorithm for training deep neural networks (DNNs). As larger and datasets cause longer times, on distributed systems common SGD variants, mainly asynchronous synchronous SGD, are widely used. Asynchronous communication efficient but suffers from accuracy degradation due to delayed parameter updating. Synchronous becomes intensive when number of nodes increases regardless its advantage. To address these issues, we introduce Layered...
Longitudinal phase space tomography has evolved into a powerful diagnostic tool in the particle accelerator domain. A computer code been developed order to visualize dynamic effects and measure machine parameters longitudinal space. This is capable of dealing with turn-by-turn parameter changes, for example, during rf rebucketing when bunch rotated minimize length. We describe reconstruction show its application as Relativistic Heavy Ion Collider.
The paper presents an interface encompassing the RHIC online ramp model and UAL off-line simulation framework. resulting consolidating facility aims to minimize gap between design operation data as well facilitate analysis of performance future upgrades. is based on Accelerator Description Exchange Format (ADXF[1]) that represents a snapshot model. same approach also considered for integrating AGS modeling environments.
Simulation of high intensity accelerators leads to the solution Poisson equation, calculate space charge forces in presence acceleration chamber walls. We reduced problem "two-and-a-half" dimensions for long particle bunches, characteristic large circular-accelerators, and applied results tracking code Orbit.
Because of the limits input/output systems currently impose on high-performance computing systems, a new generation workflows that include online data reduction and analysis is emerging. Diagnosing their performance requires sophisticated capabilities due to complexity execution patterns underlying hardware, no tool could handle voluminous trace needed detect potential problems. This work introduces Chimbuko, framework provides real-time, distributed, in situ anomaly detection. Data volumes...