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1. ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements

Author

Vora, Neel R., Xu, Yilun, Hashim, Akel, Fruitwala, Neelay, Nguyen, Ho Nam, Liao, Haoran, Balewski, Jan, Rajagopala, Abhi, Nowrouzi, Kasra, Ji, Qing, Whaley, K. Birgitta, Siddiqi, Irfan, Nguyen, Phuc, and Huang, Gang

Subjects
Quantum Physics
Abstract

Similar to reading the transistor state in classical computers, identifying the quantum bit (qubit) state is a fundamental operation to translate quantum information. However, identifying quantum state has been the slowest and most susceptible to errors operation on superconducting quantum processors. Most existing state discrimination algorithms have only been implemented and optimized "after the fact" - using offline data transferred from control circuits to host computers. Real-time state discrimination is not possible because a superconducting quantum state only survives for a few hundred us, which is much shorter than the communication delay between the readout circuit and the host computer (i.e., tens of ms). Mid-circuit measurement (MCM), where measurements are conducted on qubits at intermediate stages within a quantum circuit rather than solely at the end, represents an advanced technique for qubit reuse. For MCM necessitating single-shot readout, it is imperative to employ an in-situ technique for state discrimination with low latency and high accuracy. This paper introduces QubiCML, a field-programmable gate array (FPGA) based system for real-time state discrimination enabling MCM - the ability to measure the state at the control circuit before/without transferring data to a host computer. A multi-layer neural network has been designed and deployed on an FPGA to ensure accurate in-situ state discrimination. For the first time, ML-powered quantum state discrimination has been implemented on a radio frequency system-on-chip FPGA platform. The deployed lightweight network on the FPGA only takes 54 ns to complete each inference. We evaluated QubiCML's performance on superconducting quantum processors and obtained an average accuracy of 98.5% with only 500 ns readout. QubiCML has the potential to be the standard real-time state discrimination method for the quantum community.

Published
2024

2. Long-lived oscillations of metastable states in neutral atom systems

Author

Darbha, Siva, Kornjača, Milan, Liu, Fangli, Balewski, Jan, Hirsbrunner, Mark R., Lopes, Pedro L. S., Wang, Sheng-Tao, Van Beeumen, Roel, Klymko, Katherine, and Camps, Daan

Subjects
Quantum Physics, Condensed Matter - Quantum Gases, Condensed Matter - Statistical Mechanics, High Energy Physics - Theory, Physics - Atomic Physics
Abstract

Metastable states arise in a range of quantum systems and can be observed in various dynamical scenarios, including decay, bubble nucleation, and long-lived oscillations. The phenomenology of metastable states has been examined in quantum many-body systems, notably in 1D ferromagnetic Ising spin systems and superfluids. In this paper, we study long-lived oscillations of metastable and ground states in 1D antiferromagnetic neutral atom chains with long-range Rydberg interactions. We use a staggered local detuning field to achieve confinement. Using theoretical and numerical models, we identify novel spectral signatures of quasiparticle oscillations distinct to antiferromagnetic neutral atom systems and interpret them using a classical energy model of short-range meson repulsion. Finally, we evaluate the experimental accessibility of our proposed setup on current neutral-atom platforms and discuss experimental feasibility and constraints., Comment: 15 pages, 5 figures

Published
2024
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3. False vacuum decay and nucleation dynamics in neutral atom systems

Author

Darbha, Siva, Kornjača, Milan, Liu, Fangli, Balewski, Jan, Hirsbrunner, Mark R., Lopes, Pedro L. S., Wang, Sheng-Tao, Van Beeumen, Roel, Camps, Daan, and Klymko, Katherine

Subjects
Quantum Physics, Condensed Matter - Quantum Gases, Condensed Matter - Statistical Mechanics, High Energy Physics - Theory, Physics - Atomic Physics
Abstract

Metastable states of quantum many-body systems with confinement offer a means to simulate false vacuum phenomenology, including non-equilibrium dynamical processes like decay by nucleation, in truncated limits. Recent work has examined the decay process in 1D ferromagnetic Ising spins and superfluids. In this paper, we study nucleation dynamics in 1D antiferromagnetic neutral atom chains with Rydberg interactions, using both numerical simulations and analytic modeling. We apply a staggered local detuning field to generate the metastable and ground states. Our efforts focus on two dynamical regimes: decay and annealing. In the first, we corroborate the phenomenological decay rate scaling and determine the associated parameter range for the decay process; in the second, we uncover and elucidate a procedure to anneal the metastable state from the initial to the final system, with intermediate nucleation events. We further propose experimental protocols to prepare the required states and perform quenches on near-term neutral atom quantum simulators, examining the experimental feasibility of our proposed setup and parameter regime., Comment: 15 pages, 8 figures

Published
2024
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4. Engineering quantum states with neutral atoms

Author

Balewski, Jan, Kornjača, Milan, Klymko, Katherine, Darbha, Siva, Hirsbrunner, Mark R., Lopes, Pedro L. S., Liu, Fangli, and Camps, Daan

Subjects
Quantum Physics
Abstract

Aquila, an analog quantum simulation platform developed by QuEra Computing, supports control of the position and coherent evolution of up to 256 neutral atoms. This study details novel experimental protocols designed for analog quantum simulators that generate Bell state entanglement far away from the blockade regime, construct a $Z_2$ state with a defect induced by an ancilla, and optimize the driving fields schedule to prepare excited states with enhanced fidelity. We additionally evaluate the effectiveness of readout error mitigation techniques in improving the fidelity of measurement results. All experiments were executed on Aquila from QuEra and facilitated by the AWS Braket interface. Our experimental results closely align with theoretical predictions and numerical simulations. The insights gained from this study showcase Aquila's capabilities in handling complex quantum simulations and computations, and also pave the way for new avenues of research in quantum information processing and physics that employ programmable analog hardware platforms., Comment: 7 pages, 6 figures, IEEE QCE24 - QTEM Best Paper Award - 1st Place

Published
2024

5. Quantum Computing and Visualization: A Disruptive Technological Change Ahead

Author

Bethel, E. Wes, Amankwah, Mercy G., Balewski, Jan, Van Beeumen, Roel, Camps, Daan, Huang, Daniel, and Perciano, Talita

Subjects
Quantum Physics, Computer Science - Emerging Technologies
Abstract

The focus of this Visualization Viewpoints article is to provide some background on Quantum Computing (QC), to explore ideas related to how visualization helps in understanding QC, and examine how QC might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging as a promising pathway to overcome the growth limits in classical computing. In some cases, QC platforms offer the potential to vastly outperform the familiar classical computer by solving problems more quickly or that may be intractable on any known classical platform. As further performance gains for classical computing platforms are limited by diminishing Moore's Law scaling, QC platforms might be viewed as a potential successor to the current field of exascale-class platforms. While present-day QC hardware platforms are still limited in scale, the field of quantum computing is robust and rapidly advancing in terms of hardware capabilities, software environments for developing quantum algorithms, and educational programs for training the next generation of scientists and engineers. After a brief introduction to QC concepts, the focus of this article is to explore the interplay between the fields of visualization and QC. First, visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states. Second, there are a number of ways in which the field of visual data exploration and analysis may potentially benefit from this disruptive new technology though there are challenges going forward., Comment: Accepted for publication in IEEE Computer Graphics and Applications, 43(6), Nov/Dec, 2023

Published
2023

6. Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Author

Balewski, Jan, Amankwah, Mercy G, Van Beeumen, Roel, Bethel, E Wes, Perciano, Talita, and Camps, Daan

Subjects
Quantum Physics, Physical Sciences
Abstract

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

Published
2024

7. Efficient Quantum Counting and Quantum Content-Addressable Memory for DNA similarity

Author

Balewski, Jan, Camps, Daan, Klymko, Katherine, and Tritt, Andrew

Subjects
Quantum Physics, Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods
Abstract

We present QCAM, a quantum analogue of Content-Addressable Memory (CAM), useful for finding matches in two sequences of bit-strings. Our QCAM implementation takes advantage of Grover's search algorithm and proposes a highly-optimized quantum circuit implementation of the QCAM oracle. Our circuit construction uses the parallel uniformly controlled rotation gates, which were used in previous work to generate QBArt encodings. These circuits have a high degree of quantum parallelism which reduces their critical depth. The optimal number of repetitions of the Grover iterator used in QCAM depends on the number of true matches and hence is input dependent. We additionally propose a hardware-efficient implementation of the quantum counting algorithm (HEQC) that can infer the optimal number of Grover iterations from the measurement of a single observable. We demonstrate the QCAM application for computing the Jaccard similarity between two sets of k-mers obtained from two DNA sequences.

Published
2023

8. Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs

Author

Balewski, Jan, Amankwah, Mercy G., Van Beeumen, Roel, Bethel, E. Wes, Perciano, Talita, and Camps, Daan

Subjects
Quantum Physics
Abstract

Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes, QCrank and QBArt, which have a high degree of quantum parallelism through uniformly controlled rotation gates. QCrank encodes a sequence of real-valued data as rotations of the data qubits, allowing for high storage density. QBArt directly embeds a binary representation of the data in the computational basis, requiring fewer quantum measurements and lending itself to well-understood arithmetic operations on binary data. We present several applications of the proposed encodings for different types of data. We demonstrate quantum algorithms for DNA pattern matching, Hamming weight calculation, complex value conjugation, and retrieving an O(400) bits image, all executed on the Quantinuum QPU. Finally, we use various cloud-accessible QPUs, including IBMQ and IonQ, to perform additional benchmarking experiments., Comment: 27 pages, 14 figures, 4 tables

Published
2023

9. Quantum Computing and Visualization: A Disruptive Technological Change Ahead

Author

Bethel, E Wes, Amankwah, Mercy G, Balewski, Jan, Van Beeumen, Roel, Camps, Daan, Huang, Daniel, Perciano, Talita, and Rhyne, Theresa-Marie

Subjects
Information and Computing Sciences, Graphics, Augmented Reality and Games, Human-Centred Computing, Computer Vision and Multimedia Computation, Training, Visualization, Quantum algorithm, Quantum entanglement, Data visualization, Quantum state, Performance gain, Artificial Intelligence and Image Processing, Electrical and Electronic Engineering, Software Engineering, Computer vision and multimedia computation, Graphics, augmented reality and games, Human-centred computing
Abstract

The focus of this Visualization Viewpoints article is to provide some background on quantum computing (QC), to explore ideas related to how visualization helps in understanding QC, and examine how QC might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging as a promising pathway to overcome the growth limits in classical computing. In some cases, QC platforms offer the potential to vastly outperform the familiar classical computer by solving problems more quickly or that may be intractable on any known classical platform. As further performance gains for classical computing platforms are limited by diminishing Moore's Law scaling, QC platforms might be viewed as a potential successor to the current field of exascale-class platforms. While present-day QC hardware platforms are still limited in scale, the field of quantum computing is robust and rapidly advancing in terms of hardware capabilities, software environments for developing quantum algorithms, and educational programs for training the next generation of scientists and engineers. After a brief introduction to QC concepts, the focus of this article is to explore the interplay between the fields of visualization and QC. First, visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states. Second, there are a number of ways in which the field of visual data exploration and analysis may potentially benefit from this disruptive new technology though there are challenges going forward.

Published
2023

10. MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonça, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St., Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, and Yin, Junqi

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of hardware resources and massive scale-out capabilities. There is a critical need to understand fair and effective benchmarking of machine learning applications that are representative of real-world scientific use cases. MLPerf is a community-driven standard to benchmark machine learning workloads, focusing on end-to-end performance metrics. In this paper, we introduce MLPerf HPC, a benchmark suite of large-scale scientific machine learning training applications driven by the MLCommons Association. We present the results from the first submission round, including a diverse set of some of the world's largest HPC systems. We develop a systematic framework for their joint analysis and compare them in terms of data staging, algorithmic convergence, and compute performance. As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling, enabling overall $>10 \times$ (end-to-end) performance improvements through system scaling. Notably, our analysis shows a scale-dependent interplay between the dataset size, a system's memory hierarchy, and training convergence that underlines the importance of near-compute storage. To overcome the data-parallel scalability challenge at large batch sizes, we discuss specific learning techniques and hybrid data-and-model parallelism that are effective on large systems. We conclude by characterizing each benchmark with respect to low-level memory, I/O, and network behavior to parameterize extended roofline performance models in future rounds.

Published
2021

11. Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications

Author

Wang, Kewei, Lee, Sunwoo, Balewski, Jan, Sim, Alex, Nugent, Peter, Agrawal, Ankit, Choudhary, Alok, Wu, Kesheng, and Liao, Wei-Keng

Subjects
Information and Computing Sciences, Machine Learning, Bioengineering, Generic health relevance, Deep Learning, Transfer Learning, Multi-resolution Data
Abstract

Neural networks are powerful solutions to many scientific applications; however, they usually require long model training time due to large training data sets or large model size. Research has been focused on developing numerical optimization algorithms and parallel processing to reduce the training time. In this work, we propose a multi-resolution strategy that can reduce the training time by training the model with the reduced-resolution data samples at the beginning and later switching to the original resolution data samples. This strategy is motivated by the observation that coarser versions of many applications can be solved faster than their denser counterparts, and the solution to a coarser problem could be used to initialize the solution to the denser problem. When applying the idea to neural network training, coarse data can have a similar effect on the learning curves at the early stage as the dense data but requires less time. Once the curves no longer improve significantly, our strategy switches to using the data in original resolution. The key in this process is the ability to generate multiple resolutions of a problem automatically, which could usually be done with scientific applications with spatial and temporal continuity. We use two real-world scientific applications, CosmoFlow and DeepCAM, to evaluate the proposed mixed-resolution training strategy. Our experiment results demonstrate that the proposed training strategy effectively reduces the end-to-end training time while achieving a comparable accuracy to that of the training only with the original data. While maintaining the same model accuracy, our multi-resolution training strategy reduces the end-to-end training time up to 30% and 23% for CosmoFlow and DeepCAM, respectively.

Published
2022

12. Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes

Author

Andreopoulos, William B, Geller, Alexander M, Lucke, Miriam, Balewski, Jan, Clum, Alicia, Ivanova, Natalia N, and Levy, Asaf

Subjects
Microbiology, Biological Sciences, Bioinformatics and Computational Biology, Genetics, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Infectious Diseases, Emerging Infectious Diseases, Animals, Chromosomes, Bacterial, Deep Learning, Genome, Bacterial, Plasmids, Reproducibility of Results, Sequence Analysis, DNA, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC-ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.

Published
2022

13. Automatic Qubit Characterization and Gate Optimization with QubiC

Author

Xu, Yilun, Huang, Gang, Balewski, Jan, Naik, Ravi K., Morvan, Alexis, Mitchell, Brad, Nowrouzi, Kasra, Santiago, David I., and Siddiqi, Irfan

Subjects
Quantum Physics
Abstract

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose mutli-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of $4.9(1.1) \times 10^{-4}$ and $1.4(3) \times 10^{-2}$, respectively., Comment: Added funding agency

Published
2021

14. Asynchronous I/O Strategy for Large-Scale Deep Learning Applications

Author

Lee, Sunwoo, Kang, Qiao, Wang, Kewei, Balewski, Jan, Sim, Alex, Agrawal, Ankit, Choudhary, Alok, Nugent, Peter, Wu, Kesheng, and Liao, Wei-keng

Subjects
I/O, Deep Learning, Parallelization
Abstract

Many scientific applications have started using deep learning methods for their classification or regression problems. However, for data-intensive scientific applications, I/O performance can be the major performance bottleneck. In order to effectively solve important real-world problems using deep learning methods on High-Performance Computing (HPC) systems, it is essential to address the poor I/O performance issue in large-scale neural network training. In this paper, we propose an asynchronous I/O strategy that can be generally applied to deep learning applications. Our I/O strategy employs an I/O -dedicated thread per process, that performs I/O operations independently of the training progress. The I/O thread reads many training samples at once to reduce the total number of I/O operations per epoch. Given the fixed amount of training data, the fewer the I/O operations per epoch, the shorter the overall I/O time. The I/O operations are also overlapped with the computations using the double-buffering method. We evaluate our I/O strategy using two real-world scientific applications, CosmoFlow and Neuron-Inverter. Our experimental results demonstrate that the proposed I/O strategy significantly improves the scaling performance without affecting the regression performance.

Published
2021

15. QubiC: An open source FPGA-based control and measurement system for superconducting quantum information processors

Author

Xu, Yilun, Huang, Gang, Balewski, Jan, Naik, Ravi, Morvan, Alexis, Mitchell, Bradley, Nowrouzi, Kasra, Santiago, David I., and Siddiqi, Irfan

Subjects
Quantum Physics
Abstract

As quantum information processors grow in quantum bit (qubit) count and functionality, the control and measurement system becomes a limiting factor to large scale extensibility. To tackle this challenge and keep pace with rapidly evolving classical control requirements, full control stack access is essential to system level optimization. We design a modular FPGA (field-programmable gate array) based system called QubiC to control and measure a superconducting quantum processing unit. The system includes room temperature electronics hardware, FPGA gateware, and engineering software. A prototype hardware module is assembled from several commercial off-the-shelf evaluation boards and in-house developed circuit boards. Gateware and software are designed to implement basic qubit control and measurement protocols. System functionality and performance are demonstrated by performing qubit chip characterization, gate optimization, and randomized benchmarking sequences on a superconducting quantum processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit process fidelities are measured to be 0.9980$\pm$0.0001 and 0.948$\pm$0.004 by randomized benchmarking. With fast circuit sequence loading capability, the QubiC performs randomized compiling experiments efficiently and improves the feasibility of executing more complex algorithms., Comment: Final published version on IEEE Transactions on Quantum Engineering

Published
2020
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16. Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Author

Alexander, Francis J, Ang, James, Bilbrey, Jenna A, Balewski, Jan, Casey, Tiernan, Chard, Ryan, Choi, Jong, Choudhury, Sutanay, Debusschere, Bert, DeGennaro, Anthony M, Dryden, Nikoli, Ellis, J Austin, Foster, Ian, Cardona, Cristina Garcia, Ghosh, Sayan, Harrington, Peter, Huang, Yunzhi, Jha, Shantenu, Johnston, Travis, Kagawa, Ai, Kannan, Ramakrishnan, Kumar, Neeraj, Liu, Zhengchun, Maruyama, Naoya, Matsuoka, Satoshi, McCarthy, Erin, Mohd-Yusof, Jamaludin, Nugent, Peter, Oyama, Yosuke, Proffen, Thomas, Pugmire, David, Rajamanickam, Sivasankaran, Ramakrishniah, Vinay, Schram, Malachi, Seal, Sudip K, Sivaraman, Ganesh, Sweeney, Christine, Tan, Li, Thakur, Rajeev, Van Essen, Brian, Ward, Logan, Welch, Paul, Wolf, Michael, Xantheas, Sotiris S, Yager, Kevin G, Yoo, Shinjae, and Yoon, Byung-Jun

Subjects
Bioengineering, Affordable and Clean Energy, Machine learning, exascale computing, reinforcement learning, active learning, high-performance computing for machine learning, machine learning for high-performance computing, Distributed Computing
Abstract

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

Published
2021

17. The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

Author

Oyama, Yosuke, Maruyama, Naoya, Dryden, Nikoli, McCarthy, Erin, Harrington, Peter, Balewski, Jan, Matsuoka, Satoshi, Nugent, Peter, and Van Essen, Brian

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy., Comment: 12 pages, 10 figures

Published
2020

18. Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models

Author

Ladd, Alexander, Kim, Kyung Geun, Balewski, Jan, Bouchard, Kristofer, and Ben-Shalom, Roy

Subjects
Information and Computing Sciences, Biomedical and Clinical Sciences, Neurosciences, Machine Learning, Bioengineering, Neurological, biophysical neuron model, high performance computing, evolutionary algorithms, non-convex optimization, strong scaling, weak scaling, electrophysiology, Cognitive Sciences, Applied computing, Machine learning
Abstract

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.

Published
2021

20. STAR Data Reconstruction at NERSC/Cori, an adaptable Docker container approach for HPC

Author

Mustafa, Mustafa, Balewski, Jan, Lauret, Jérôme, Porter, Jefferson, Canon, Shane, Gerhardt, Lisa, Hajdu, Levente, and Lukascsyk, Mark

Subjects
Physics - Data Analysis, Statistics and Probability, High Energy Physics - Experiment
Abstract

As HPC facilities grow their resources, adaptation of classic HEP/NP workflows becomes a need. Linux containers may very well offer a way to lower the bar to exploiting such resources and at the time, help collaboration to reach vast elastic resources on such facilities and address their massive current and future data processing challenges. In this proceeding, we showcase STAR data reconstruction workflow at Cori HPC system at NERSC. STAR software is packaged in a Docker image and runs at Cori in Shifter containers. We highlight two of the typical end-to-end optimization challenges for such pipelines: 1) data transfer rate which was carried over ESnet after optimizing end points and 2) scalable deployment of conditions database in an HPC environment. Our tests demonstrate equally efficient data processing workflows on Cori/HPC, comparable to standard Linux clusters., Comment: 7 pages, 4 figures, The 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016

Published
2017
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21. Long-lived oscillations of false and true vacuum states in neutral atom systems

Author

Darbha, Siva, Kornjača, Milan, Liu, Fangli, Balewski, Jan, Hirsbrunner, Mark R., Lopes, Pedro, Wang, Sheng-Tao, Van Beeumen, Roel, Klymko, Katherine, Camps, Daan, Darbha, Siva, Kornjača, Milan, Liu, Fangli, Balewski, Jan, Hirsbrunner, Mark R., Lopes, Pedro, Wang, Sheng-Tao, Van Beeumen, Roel, Klymko, Katherine, and Camps, Daan

Abstract

Metastable false vacuum states arise in a range of quantum systems and can be observed in various dynamical scenarios, including decay, bubble nucleation, and long-lived oscillations. False vacuum phenomenology has been examined in quantum many-body systems, notably in 1D ferromagnetic Ising spin systems and superfluids. In this paper, we study long-lived oscillations of false and true vacuum states in 1D antiferromagnetic neutral atom chains with long-range Rydberg interactions. We use a staggered local detuning field to achieve confinement. Using theoretical and numerical models, we identify novel spectral signatures of quasiparticle oscillations distinct to antiferromagnetic neutral atom systems and interpret them using a classical energy model of deconfinement from Rydberg tails. Finally, we evaluate the experimental accessibility of our proposed setup on current neutral-atom platforms and discuss experimental feasibility and constraints., Comment: 14 pages, 5 figures

Published
2024

25. Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.

Author

Ladd, Alexander, Ladd, Alexander, Kim, Kyung Geun, Balewski, Jan, Bouchard, Kristofer, Ben-Shalom, Roy, Ladd, Alexander, Ladd, Alexander, Kim, Kyung Geun, Balewski, Jan, Bouchard, Kristofer, and Ben-Shalom, Roy

Abstract

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.

Published
2022

26. Single spin asymmetry A N in polarized proton–proton elastic scattering at √ s = 200 GeV

Author

Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. Department of Physics, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross Cameron, Hays-Wehle, James Prewitt, Leight, William Axel, Redwine, Robert P, Seele, Joseph Patrick, Steadman, Stephen G., van Nieuwenhuizen, Gerrit J, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. Department of Physics, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross Cameron, Hays-Wehle, James Prewitt, Leight, William Axel, Redwine, Robert P, Seele, Joseph Patrick, Steadman, Stephen G., and van Nieuwenhuizen, Gerrit J

Abstract

We report a high precision measurement of the transverse single spin asymmetry AN at the center of mass energy s=200GeV in elastic proton-proton scattering by the STAR experiment at RHIC. The AN was measured in the four-momentum transfer squared t range 0.003≤|t|≤0.035(GeV/c)2, the region of a significant interference between the electromagnetic and hadronic scattering amplitudes. The measured values of AN and its t-dependence are consistent with a vanishing hadronic spin-flip amplitude, thus providing strong constraints on the ratio of the single spin-flip to the non-flip amplitudes. Since the hadronic amplitude is dominated by the Pomeron amplitude at this s, we conclude that this measurement addresses the question about the presence of a hadronic spin flip due to the Pomeron exchange in polarized proton-proton elastic scattering. © 2013 Elsevier B.V.

Published
2022

27. Design and operation of a windowless gas target internal to a solenoidal magnet for use with a megawatt electron beam

Author

Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. Department of Physics, Lee, S., Corliss, Ross Cameron, Friščic, I., Balewski, Jan T., Bernauer, Jan Christopher, Epstein, Charles S., Fisher, P., Hasell, Douglas K., Johnston, R, Milner, Richard G, Moran, P, Palumbo, Daniel C., Steadman, Stephen G, Vogel, C., Wang, Y., Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. Department of Physics, Lee, S., Corliss, Ross Cameron, Friščic, I., Balewski, Jan T., Bernauer, Jan Christopher, Epstein, Charles S., Fisher, P., Hasell, Douglas K., Johnston, R, Milner, Richard G, Moran, P, Palumbo, Daniel C., Steadman, Stephen G, Vogel, C., and Wang, Y.

Abstract

© 2019 Elsevier B.V. A windowless hydrogen gas target of nominal thickness 1019 cm−2 is an essential component of the DarkLight experiment, which is designed to utilize the megawatt electron beam at an Energy Recovery Linac (ERL). The design of such a target is challenging because the pressure drops by many orders of magnitude between the central, high-density section of the target and the surrounding beamline, resulting in laminar, transitional, and finally molecular flow regimes. The target system was assembled and operated at Jefferson Lab's Low Energy Recirculator Facility (LERF) in 2016, and subsequently underwent several revisions and calibration tests at MIT Bates in 2017. The system at dynamic equilibrium was simulated in COMSOL to provide a better understanding of its optimal operation at other working points. We have determined that a windowless gas target with sufficiently high density for DarkLight's experimental needs is feasible in an ERL environment.

Published
2022

31. MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, primary, Emani, Murali, additional, Balma, Jacob, additional, Drescher, Lukas, additional, Drozd, Aleksandr, additional, Fink, Andreas, additional, Fox, Geoffrey, additional, Kanter, David, additional, Kurth, Thorsten, additional, Mattson, Peter, additional, Mu, Dawei, additional, Ruhela, Amit, additional, Sato, Kento, additional, Shirahata, Koichi, additional, Tabaru, Tsuguchika, additional, Tsaris, Aristeidis, additional, Balewski, Jan, additional, Cumming, Ben, additional, Danjo, Takumi, additional, Domke, Jens, additional, Fukai, Takaaki, additional, Fukumoto, Naoto, additional, Fukushi, Tatsuya, additional, Gerofi, Balazs, additional, Honda, Takumi, additional, Imamura, Toshiyuki, additional, Kasagi, Akihiko, additional, Kawakami, Kentaro, additional, Kudo, Shuhei, additional, Kuroda, Akiyoshi, additional, Martinasso, Maxime, additional, Matsuoka, Satoshi, additional, Mendonca, Henrique, additional, Minami, Kazuki, additional, Ram, Prabhat, additional, Sawada, Takashi, additional, Shankar, Mallikarjun, additional, John, Tom St., additional, Tabuchi, Akihiro, additional, Vishwanath, Venkatram, additional, Wahib, Mohamed, additional, Yamazaki, Masafumi, additional, and Yin, Junqi, additional

Published
2021
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32. Storage 2020: A Vision for the Future of HPC Storage

Author

Lockwood, Glenn, primary, Hazen, Damien, additional, Koziol, Quincey, additional, Canon, R., additional, Antypas, Katie, additional, Balewski, Jan, additional, Balthaser, Nicholas, additional, Bhimji, Wahid, additional, Botts, James, additional, Broughton, Jeff, additional, Butler, Tina, additional, Butler, Gregory, additional, Cheema, Ravi, additional, Daley, Christopher, additional, Declerck, Tina, additional, Gerhardt, Lisa, additional, Hurlbert, Wayne, additional, Kallback-Rose, Kristy, additional, Leak, Stephen, additional, Lee, Jason, additional, Lee, Rei, additional, Liu, Jianlin, additional, Lozinskiy, Kirill, additional, Paul, David, additional, Prabhat, None, additional, Snavely, Cory, additional, Srinivasan, Jay, additional, Stone Gibbins, Tavia, additional, and Wright, Nicholas, additional

Published
2017
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33. Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data

Author

Muszynski, Grzegorz, Prabhat, Balewski, Jan, Kashinath, Karthik, Wehner, Michael, Kurlin, Vitaliy, and SOC, IEEE COMP

Subjects
010504 meteorology & atmospheric sciences, business.industry, Event (computing), Northern Hemisphere, Pattern recognition, Atmospheric model, 010501 environmental sciences, Blocking (statistics), 01 natural sciences, Convolutional neural network, Data modeling, Pattern recognition (psychology), Classifier (linguistics), Artificial intelligence, business, 0105 earth and related environmental sciences
Abstract

In this paper, we address a problem of atmospheric blocking pattern recognition in global climate model simulation data. Understanding blocking events is a crucial problem to society and natural infrastructure, as they often lead to weather extremes, such as heat waves, heavy precipitation, and the unusually poor air condition. Moreover, it is very challenging to detect these events as there is no physics-based model of blocking dynamic development that could account for their spatiotemporal characteristics. Here, we propose a new two-stage hierarchical pattern recognition method for detection and localisation of atmospheric blocking events in different regions over the globe. For both the detection stage and localisation stage, we train five different architectures of a convolutional neural network (CNN) based classifier and regressor. The results show the general pattern of the atmospheric blocking detection performance increasing significantly for the deep CNN architectures. In contrast, we see the estimation error of event location decreasing significantly in the localisation problem for the shallow CNN architectures. We demonstrate that CNN architectures tend to achieve the highest accuracy for blocking event detection and the lowest estimation error of event localisation in regions of the Northern Hemisphere than in regions of the Southern Hemisphere.

Published
2021

34. LERF - New life for the jefferson laboratory FEL

Author

Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Bernauer, Jan Christopher, Bessuille, Jason C., Corliss, Ross Cameron, Cowan, R., Epstein, C, Fisher, P, Friščić, I, Hasell, Douglas K., Ihloff, Ernest E, Kelsey, James E, Lee, S., Moran, Peter William, Milner, R, Palumbo, Daniel C., Steadman, Stephen G, Tschalär, C., Vidal, Chris, Wang, Y., Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Bernauer, Jan Christopher, Bessuille, Jason C., Corliss, Ross Cameron, Cowan, R., Epstein, C, Fisher, P, Friščić, I, Hasell, Douglas K., Ihloff, Ernest E, Kelsey, James E, Lee, S., Moran, Peter William, Milner, R, Palumbo, Daniel C., Steadman, Stephen G, Tschalär, C., Vidal, Chris, and Wang, Y.

Abstract

© ERL 2017, the 59th ICFA Advanced Beam Dynamics Workshop on Energy Recovery Linacs.All right reserved. In 2012 Jefferson Laboratory's energy recovery linac (ERL) driven Free Electron Laser successfully completed a transmission test in which high current CW beam (4.3 mA at 100 MeV) was transported through a 2 mm aperture for 7 hours with beam losses as low as 3 ppm. The purpose of the run was to mimic an internal gas target for DarkLight [1] - an experiment designed to search for a dark matter particle. The ERL was not run again until late 2015 for a brief re-commissioning in preparation for the next phase of DarkLight. In the intervening years, the FEL was rebranded as the Low Energy Recirculator Facility. In 2016 several weeks of operation were allocated to configure the machine for DarkLight with the purpose of exercising - for the first time - an internal gas target in an ERL. Despite a number of challenges, including the inability to energy recover without losses (precluding CW operation), beam was delivered to a target of thickness 1018 cm-2 which represents a three order of magnitude increase in thickness from previous internal target experiments. Details of the machine configuration and operational experience will be discussed.

Published
2021

35. Erratum: Azimuthal anisotropy at the relativistic heavy ion collider: the first and fourth harmonics [Phys. Rev. Lett. 92, 062301 (2004)]

Author

Adams, Joseph R., Adler, Clemens, Aggarwal, Madan Mohan, Ahammed, Zubayer, Amonett, John, Anderson, Bryon D., Anderson, Mike, Arkhipkin, Dmitry A., Averichev, Georgy S., Badyal, S. K., Balewski, Jan, Barannikova, Olga, Barnby, Lee Stuart, Baudot, Jérome, Bekele, Selemon, Belaga, Victoria Vladimirovna, Bellwied, Rene, Berger, Jens, Bezverkhny, B. I., Bhardwaj, S., Bhaskar, P., Bhati, Ashok Kumar, Bichsel, Hans, Billmeier, Anja, Bland, Leslie C., Blyth, Charles O., Bonner, Billy E., Botje, Michiel, Boucham, Abdelkrim, Brandin, Andrei V., Bravar, Alessandro, Cadman, R. V., Cai, Xiang-Zhou, Caines, Helen Louise, Calderón de la Barca Sánchez, Manuel, Carroll, J., Castillo Castellanos, Javier Ernesto, Castro, M., Cebra, Daniel A., Chaloupka, Petr, Chattopadhyay, S., Chen, H. F., Chen, Y., Chernenko, Sergey P., Cherney, Michael Gerard, Chikanian, Alexei, Choi, B., Christie, William, Coffin, Jean Pierre, Cormier, Thomas Michael, Cramer, John G., Crawford, Henry J., Csanád, Máté, Das, Debasish, Das, Supriya, Derevschikov, Anatoly A., Didenko, Lidia, Dietel, Thomas, Dong, W. J., Dong, X., Draper, James E., Du, F., Dubey, Anand Kumar, Dunin, V. B., Dunlop, James C., Dutta Majumdar, Mihir Ranjan, Eckardt, Volker, Efimov, Leonid G., Emelianov, V., Engelage, Jon M., Eppley, Geary Wareham, Erazmus, Barbara Ewa, Estienne, Magali Danielle, Fachini, Patricia, Faine, V., Faivre, Julien, Fatemi, Renee H., Filimonov, Kirill, Filip, Peter, Finch, Evan, Fisyak, Yuri V., Flierl, Dominik Bernhard, Foley, Kenneth John, Fu, J., Gagliardi, Carl A., Gagunashvili, N., Gans, J., Ganti, Murthy S., Gaudichet, L., Germain, Marie, Geurts, Frank, Ghazikhanian, Vahe, Ghosh, Premomoy, Gonzalez, Johan Enmanuel, Grachov, Oleg A., Grigoriev, Vladislav A., Gronstal, S., Grosnick, David P., Guedon, M., Guertin, Steven M., Gupta, Anik, Gushin, E., Gutierrez, Thomas Dominic, Hallman, Timothy J., Hardtke, David H., Harris, John W., Heinz, Mark Thomas, Henry, Thomas W., Heppelmann, Steven Francis, Herston, T., Hippolyte, Boris, Hirsch, Andrew S., Hjort, Eric, Hoffmann, Gerald W., Horsley, M., Huang, Huan Zhong, Huang, Shengli, Humanic, Thomas J., Igo, George J., Ishihara, A., Jacobs, Peter Martin, Jacobs, William W., Janik, Michał, Jiang, H., Johnson, I., Jones, Peter Graham, Judd, Eleanor G., Kabana, Sonia, Kaneta, Kaneta, Kaplan, Morton, Keane, Declan, Khodyrev, V. Yu., Kiryluk, Joanna, Kisiel, Adam Ryszard, Klay, Jennifer Lynn, Klein, Spencer Robert, Klyachko, A., Koetke, Donald D., Kollegger, Thorsten, Kopytine, M., Kotchenda, L., Kovalenko, A. D., Kramer, M., Kravtsov, Peter, Kravtsov, Vladimir I., Krueger, Keith William, Kuhn, Christian Claude, Kulikov, Anatoly I., Kumar, Ajay, Kunde, Gerd J., Kunz, Christopher Lee, Kutuev, R. Kh., Kuznetsov, A. A., Lamont, Matthew A. C., Landgraf, Jeffery M., Lange, S., Lansdell, Curtis Patrick Leon, Lasiuk, Brian, Laue, Frank, Lauret, Jerome, Lebedev, Alexei, Lednický, Richard, LeVine, Micheal J., Li, C., Li, Q., Lindenbaum, Sam J., Lisa, Michael A., Liu, F., Liu, L., Liu, Z., Liu, Q. J., Ljubicic, Tonko A., Llope, William Joseph, Long, H., Longacre, Ronald S., Lopez-Noriega, M., Love, William A., Ludlam, Thomas, Lynn, David, Ma, Jing-guo, Ma, R., Ma, Yu-Gang, Magestro, Daniel, Mahajan, Sanjay, Mangotra, Lalit K., Mahapatra, D. P., Majka, Richard Daniel, Manweiler, Robert W., Margetis, Spyridon, Markert, Christina, Martin, L., Marx, Jay N., Matis, Howard S., Matulenko, Yuri A., McShane, Tom S., Meissner, F., Melnick, Yu., Meščanin, Aleksei P., Messer, Matthias, Miller, M. L., Milosevich, Z., Minaev, Nikolai G., Mironov, Camelia, Mishra, Debadeepti, Mitchell, J., Mohanty, Bedangadas, Molnar, Levente, Moore, C. F., Mora-Corral, M. J., Morozov, Dmitry A., Morozov, V., de Moura, M. M., Munhoz, Marcelo Gameiro, Nandi, Basanta Kumar, Nayak, S. K., Nayak, Tapan Kumar, Nelson, John M., Nevski, P., Niida, Takafumi, Nikitin, Vladimir A., Nogach, Larisa V., Norman, B., Nurušev, Sandibek B., Odyniec, Grazyna Janina, Ogawa, Akio, Okorokov, Vitaly A., Oldenburg, Markus D., Olson, Douglas L., Paic, Guy, Pandey, S. U., Pal, Susanta Kumar, Panebratsev, Yuri, Panitkin, Sergei Yurievich, Pavlinov, A. I., Pawlak, Tomasz Jan, Perevoztchikov, V., Perkins, Chris, Peryt, Wiktor Stanislaw, Petrov, V. A., Phatak, S. C., Picha, R., Planinic, Mirko, Pluta, Jan Marian, Porile, Norbert T., Porter, Jeffrey Brent, Poskanzer, Arthur M., Potekhin, Maxim, Potrebenikova, E., Potukuchi, B. V. K. S., Prindle, Duncan J., Pruneau, Claude A., Putschke, Jörn H., Rai, Gulshan, Rakness, Gregory L., Raniwala, Rashmi, Raniwala, Sudhir, Ravel, Olivier, Ray, Robert L., Razin, Sergej V., Reichhold, Dennis Michael, Reid, Jeffrey G., Renault, G., Retiere, Fabrice, Ridiger, Alexey, Ritter, Hans Georg, Roberts, Jabus B., Rogachevski, O. V., Romero, Juan L., Rose, A., Roy, Christelle Sophie, Ruan, Lijuan, Sahoo, Raghunath, Sakrejda, Iwona, Salur, Sevil, Sandweiss, Jack H., Savin, Igor A., Schambach, Joachim, Scharenberg, Rolf Paul, Schmitz, Norbert, Schroeder, Lee S., Schweda, Kai, Seger, Janet Elizabeth, Seliverstov, D., Seyboth, Peter, Shahaliev, Ehtiram, Shao, M., Sharma, M., Shestermanov, Konstantin E., Shimanskii, S. S., Singaraju, Rama Narayana, Simon, F., Skoro, Goran P., Smirnov, Nikolai, Snellings, Raimond, Sood, Gopika, Sorensen, Paul Richard, Sowinski, James, Spinka, Harold. M., Srivastava, Brijesh Kumar, Stanislaus, S., Stock, Reinhard, Stolpovsky, Alexander, Strichanov, Michail Nikolaevič, Stringfellow, Blair, Struck, Christof, Suaide, Alexandre Alarcon do Passo, Sugarbaker, Evan R., Suire, Christophe Pierre, Šumbera, Michal, Surrow, Bernd, Symons, Timothy James MacNeil, Toledo, Alejandro Szanto de, Szarwas, Piotr, Tai, An, Takahashi, Jun, Tang, Aihong, Thein, Dylan, Thomas, James H., Tikhomirov, V., Todoroki, Takahito, Tokarev, Mikhail V., Tonjes, Marguerite Belt, Trainor, Thomas A., Trentalange, Stephen, Tribble, Robert E., Trivedi, M. D., Trofimov, V., Tsai, Oleg D., Ullrich, Thomas, Underwood, David G., Van Buren, Gene, VanderMolen, A. M., Vasiliev, Alexander Nikolaevich, Vasiliev, M., Vigdor, Steven E., Viyogi, Yogendra P., Voloshin, Sergei, Waggoner, William T., Wang, F., Wang, G., Wang, X. L., Wang, Z. M., Ward, H., Watson, J. W., Wells, R., Westfall, Gary D., Whitten, Charles A., Wieman, Howard, Willson, Robert, Wissink, Scott W., Witt, Richard, Wood, J., Wu, J., Xu, N., Xu, Z., Xu, Z. Z., Yamamoto, E., Yepes, Pablo P., Yurevich, Vladimir Yurevich, Zanevski, Y. V., Zborovský, Imrich, Zhang, Haitao, Zhang, W. M., Zhang, Z. P., Żołnierczuk, Piotr Adam, Zoulkarneev, R., Zoulkarneeva, J., Zubarev, A. N., Adams, Joseph R., Adler, Clemens, Aggarwal, Madan Mohan, Ahammed, Zubayer, Amonett, John, Anderson, Bryon D., Anderson, Mike, Arkhipkin, Dmitry A., Averichev, Georgy S., Badyal, S. K., Balewski, Jan, Barannikova, Olga, Barnby, Lee Stuart, Baudot, Jérome, Bekele, Selemon, Belaga, Victoria Vladimirovna, Bellwied, Rene, Berger, Jens, Bezverkhny, B. I., Bhardwaj, S., Bhaskar, P., Bhati, Ashok Kumar, Bichsel, Hans, Billmeier, Anja, Bland, Leslie C., Blyth, Charles O., Bonner, Billy E., Botje, Michiel, Boucham, Abdelkrim, Brandin, Andrei V., Bravar, Alessandro, Cadman, R. V., Cai, Xiang-Zhou, Caines, Helen Louise, Calderón de la Barca Sánchez, Manuel, Carroll, J., Castillo Castellanos, Javier Ernesto, Castro, M., Cebra, Daniel A., Chaloupka, Petr, Chattopadhyay, S., Chen, H. F., Chen, Y., Chernenko, Sergey P., Cherney, Michael Gerard, Chikanian, Alexei, Choi, B., Christie, William, Coffin, Jean Pierre, Cormier, Thomas Michael, Cramer, John G., Crawford, Henry J., Csanád, Máté, Das, Debasish, Das, Supriya, Derevschikov, Anatoly A., Didenko, Lidia, Dietel, Thomas, Dong, W. J., Dong, X., Draper, James E., Du, F., Dubey, Anand Kumar, Dunin, V. B., Dunlop, James C., Dutta Majumdar, Mihir Ranjan, Eckardt, Volker, Efimov, Leonid G., Emelianov, V., Engelage, Jon M., Eppley, Geary Wareham, Erazmus, Barbara Ewa, Estienne, Magali Danielle, Fachini, Patricia, Faine, V., Faivre, Julien, Fatemi, Renee H., Filimonov, Kirill, Filip, Peter, Finch, Evan, Fisyak, Yuri V., Flierl, Dominik Bernhard, Foley, Kenneth John, Fu, J., Gagliardi, Carl A., Gagunashvili, N., Gans, J., Ganti, Murthy S., Gaudichet, L., Germain, Marie, Geurts, Frank, Ghazikhanian, Vahe, Ghosh, Premomoy, Gonzalez, Johan Enmanuel, Grachov, Oleg A., Grigoriev, Vladislav A., Gronstal, S., Grosnick, David P., Guedon, M., Guertin, Steven M., Gupta, Anik, Gushin, E., Gutierrez, Thomas Dominic, Hallman, Timothy J., Hardtke, David H., Harris, John W., Heinz, Mark Thomas, Henry, Thomas W., Heppelmann, Steven Francis, Herston, T., Hippolyte, Boris, Hirsch, Andrew S., Hjort, Eric, Hoffmann, Gerald W., Horsley, M., Huang, Huan Zhong, Huang, Shengli, Humanic, Thomas J., Igo, George J., Ishihara, A., Jacobs, Peter Martin, Jacobs, William W., Janik, Michał, Jiang, H., Johnson, I., Jones, Peter Graham, Judd, Eleanor G., Kabana, Sonia, Kaneta, Kaneta, Kaplan, Morton, Keane, Declan, Khodyrev, V. Yu., Kiryluk, Joanna, Kisiel, Adam Ryszard, Klay, Jennifer Lynn, Klein, Spencer Robert, Klyachko, A., Koetke, Donald D., Kollegger, Thorsten, Kopytine, M., Kotchenda, L., Kovalenko, A. D., Kramer, M., Kravtsov, Peter, Kravtsov, Vladimir I., Krueger, Keith William, Kuhn, Christian Claude, Kulikov, Anatoly I., Kumar, Ajay, Kunde, Gerd J., Kunz, Christopher Lee, Kutuev, R. Kh., Kuznetsov, A. A., Lamont, Matthew A. C., Landgraf, Jeffery M., Lange, S., Lansdell, Curtis Patrick Leon, Lasiuk, Brian, Laue, Frank, Lauret, Jerome, Lebedev, Alexei, Lednický, Richard, LeVine, Micheal J., Li, C., Li, Q., Lindenbaum, Sam J., Lisa, Michael A., Liu, F., Liu, L., Liu, Z., Liu, Q. J., Ljubicic, Tonko A., Llope, William Joseph, Long, H., Longacre, Ronald S., Lopez-Noriega, M., Love, William A., Ludlam, Thomas, Lynn, David, Ma, Jing-guo, Ma, R., Ma, Yu-Gang, Magestro, Daniel, Mahajan, Sanjay, Mangotra, Lalit K., Mahapatra, D. P., Majka, Richard Daniel, Manweiler, Robert W., Margetis, Spyridon, Markert, Christina, Martin, L., Marx, Jay N., Matis, Howard S., Matulenko, Yuri A., McShane, Tom S., Meissner, F., Melnick, Yu., Meščanin, Aleksei P., Messer, Matthias, Miller, M. L., Milosevich, Z., Minaev, Nikolai G., Mironov, Camelia, Mishra, Debadeepti, Mitchell, J., Mohanty, Bedangadas, Molnar, Levente, Moore, C. F., Mora-Corral, M. J., Morozov, Dmitry A., Morozov, V., de Moura, M. M., Munhoz, Marcelo Gameiro, Nandi, Basanta Kumar, Nayak, S. K., Nayak, Tapan Kumar, Nelson, John M., Nevski, P., Niida, Takafumi, Nikitin, Vladimir A., Nogach, Larisa V., Norman, B., Nurušev, Sandibek B., Odyniec, Grazyna Janina, Ogawa, Akio, Okorokov, Vitaly A., Oldenburg, Markus D., Olson, Douglas L., Paic, Guy, Pandey, S. U., Pal, Susanta Kumar, Panebratsev, Yuri, Panitkin, Sergei Yurievich, Pavlinov, A. I., Pawlak, Tomasz Jan, Perevoztchikov, V., Perkins, Chris, Peryt, Wiktor Stanislaw, Petrov, V. A., Phatak, S. C., Picha, R., Planinic, Mirko, Pluta, Jan Marian, Porile, Norbert T., Porter, Jeffrey Brent, Poskanzer, Arthur M., Potekhin, Maxim, Potrebenikova, E., Potukuchi, B. V. K. S., Prindle, Duncan J., Pruneau, Claude A., Putschke, Jörn H., Rai, Gulshan, Rakness, Gregory L., Raniwala, Rashmi, Raniwala, Sudhir, Ravel, Olivier, Ray, Robert L., Razin, Sergej V., Reichhold, Dennis Michael, Reid, Jeffrey G., Renault, G., Retiere, Fabrice, Ridiger, Alexey, Ritter, Hans Georg, Roberts, Jabus B., Rogachevski, O. V., Romero, Juan L., Rose, A., Roy, Christelle Sophie, Ruan, Lijuan, Sahoo, Raghunath, Sakrejda, Iwona, Salur, Sevil, Sandweiss, Jack H., Savin, Igor A., Schambach, Joachim, Scharenberg, Rolf Paul, Schmitz, Norbert, Schroeder, Lee S., Schweda, Kai, Seger, Janet Elizabeth, Seliverstov, D., Seyboth, Peter, Shahaliev, Ehtiram, Shao, M., Sharma, M., Shestermanov, Konstantin E., Shimanskii, S. S., Singaraju, Rama Narayana, Simon, F., Skoro, Goran P., Smirnov, Nikolai, Snellings, Raimond, Sood, Gopika, Sorensen, Paul Richard, Sowinski, James, Spinka, Harold. M., Srivastava, Brijesh Kumar, Stanislaus, S., Stock, Reinhard, Stolpovsky, Alexander, Strichanov, Michail Nikolaevič, Stringfellow, Blair, Struck, Christof, Suaide, Alexandre Alarcon do Passo, Sugarbaker, Evan R., Suire, Christophe Pierre, Šumbera, Michal, Surrow, Bernd, Symons, Timothy James MacNeil, Toledo, Alejandro Szanto de, Szarwas, Piotr, Tai, An, Takahashi, Jun, Tang, Aihong, Thein, Dylan, Thomas, James H., Tikhomirov, V., Todoroki, Takahito, Tokarev, Mikhail V., Tonjes, Marguerite Belt, Trainor, Thomas A., Trentalange, Stephen, Tribble, Robert E., Trivedi, M. D., Trofimov, V., Tsai, Oleg D., Ullrich, Thomas, Underwood, David G., Van Buren, Gene, VanderMolen, A. M., Vasiliev, Alexander Nikolaevich, Vasiliev, M., Vigdor, Steven E., Viyogi, Yogendra P., Voloshin, Sergei, Waggoner, William T., Wang, F., Wang, G., Wang, X. L., Wang, Z. M., Ward, H., Watson, J. W., Wells, R., Westfall, Gary D., Whitten, Charles A., Wieman, Howard, Willson, Robert, Wissink, Scott W., Witt, Richard, Wood, J., Wu, J., Xu, N., Xu, Z., Xu, Z. Z., Yamamoto, E., Yepes, Pablo P., Yurevich, Vladimir Yurevich, Zanevski, Y. V., Zborovský, Imrich, Zhang, Haitao, Zhang, W. M., Zhang, Z. P., Żołnierczuk, Piotr Adam, Zoulkarneev, R., Zoulkarneeva, J., and Zubarev, A. N.

Published
2021

36. Identified particle elliptic flow in Au+Au collisions at √sNN = 130 GeV

Author

Adler, Clemens, Ahammed, Zubayer, Allgower, Chris, Amonett, John, Anderson, Bryon D., Anderson, Mike, Averichev, Georgy S., Balewski, Jan, Barannikova, Olga, Barnby, Lee Stuart, Baudot, Jérome, Bekele, Selemon, Belaga, Victoria Vladimirovna, Bellwied, Rene, Berger, Jens, Bichsel, Hans, Bland, Leslie C., Blyth, Charles O., Bonner, Billy E., Bossingham, R., Boucham, Abdelkrim, Brandin, Andrei V., Cadman, R. V., Caines, Helen Louise, Calderón de la Barca Sánchez, Manuel, Cardenas, Andrés, Carroll, J., Castillo Castellanos, Javier Ernesto, Castro, M., Cebra, Daniel A., Chattopadhyay, Subhasis, Chen, M. L., Chen, Y., Chernenko, Sergey P., Cherney, Michael Gerard, Chikanian, Alexei, Choi, B., Christie, William, Coffin, Jean Pierre, Conin, Laurent, Cormier, Thomas Michael, Cramer, John G., Crawford, Henry J., DeMello, M., Deng, W. S., Derevschikov, Anatoly A., Didenko, Lidia, Draper, James E., Dunin, V. B., Dunlop, James C., Eckardt, Volker, Efimov, Leonid G., Emelianov, V., Engelage, Jon M., Eppley, Geary Wareham, Erazmus, Barbara Ewa, Fachini, Patricia, Finch, Evan, Fisyak, Yuri V., Flierl, Dominik Bernhard, Foley, K. J., Fu, J., Gagunashvili, N., Gans, J., Gaudichet, L., Germain, Marie, Geurts, Frank, Ghazikhanian, Vahe, Grabski, J., Grachov, Oleg A., Greiner, D., Grigoriev, Vladislav A., Guedon, M., Gushin, E., Hallman, Timothy J., Hardtke, David H., Harris, John W., Heffner, Michael D., Heppelmann, Steven Francis, Herston, T., Hippolyte, Boris, Hirsch, Andrew S., Hjort, Eric, Hoffmann, Gerald W., Horsley, M., Huang, Huan Zhong, Humanic, Thomas J., Hümmler, Holm Gero, Igo, George J., Ishihara, A., Ivanshin, Yu. I., Jacobs, Peter Martin, Jacobs, William W., Janik, Michał, Johnson, I., Jones, Peter Graham, Judd, Eleanor G., Kaneta, Kaneta, Kaplan, Morton, Keane, Declan, Kisiel, Adam Ryszard, Klay, Jennifer Lynn, Klein, Spencer Robert, Klyachko, A., Konstantinov, A. S., Kotchenda, L., Kovalenko, A. D., Kramer, M., Kravtsov, Peter, Krueger, Keith William, Kuhn, Christian Claude, Kulikov, Anatoly I., Kunde, Gerd J., Kunz, Christopher Lee, Kutuev, R. Kh., Kuznetsov, A. A., Lakehal-Ayat, L., Lamas-Valverde, Jose, Lamont, Matthew A. C., Landgraf, Jeffery M., Lange, S., Lansdell, Curtis Patrick Leon, Lasiuk, Brian, Laue, Frank, Lebedev, Alexei, LeCompte, T., Lednický, Richard, Leontiev, V. M., Leszczynski, P., LeVine, Micheal J., Li, Q., Lindenbaum, Sam J., Lisa, Michael A., Ljubicic, Tonko A., Llope, William Joseph, LoCurto, G., Long, H., Longacre, Ronald S., Lopez-Noriega, M., Love, William A., Lynn, David, Majka, Richard Daniel, Maliszewski, A., Margetis, Spyridon, Martin, L., Marx, Jay N., Matis, Howard S., Matulenko, Yuri A., McShane, Tom S., Meissner, F., Melnick, Yu., Meščanin, Aleksei P., Messer, Matthias, Miller, M. L., Milosevich, Z., Minaev, Nikolai G., Mitchell, J., Moiseenko, V. A., Moltz, D., Moore, C. F., Morozov, V., de Moura, M. M., Munhoz, Marcelo Gameiro, Mutchler, G. S., Nelson, John M., Nevski, P., Nikitin, Vladimir A., Nogach, Larisa V., Norman, B., Nurušev, Sandibek B., Odyniec, Grazyna Janina, Ogawa, Akio, Okorokov, Vitaly A., Oldenburg, Markus D., Olson, Douglas L., Paic, Guy, Pandey, S. U., Panebratsev, Yuri, Panitkin, Sergei Yurievich, Pavlinov, A. I., Pawlak, Tomasz Jan, Perevoztchikov, V., Peryt, Wiktor Stanislaw, Petrov, V. A., Pinganaud, W., Platner, E., Pluta, Jan Marian, Porile, Norbert T., Porter, Jeffrey Brent, Poskanzer, Arthur M., Potrebenikova, E., Prindle, Duncan J., Pruneau, Claude A., Radomski, S., Rai, Gulshan, Ravel, Olivier, Ray, Robert L., Razin, Sergej V., Reichhold, Dennis Michael, Reid, Jeffrey G., Retiere, Fabrice, Ridiger, Alexey, Ritter, Hans Georg, Roberts, Jabus B., Rogachevski, O. V., Romero, Juan L., Roy, Christelle Sophie, Russ, D., Rykov, Vladimir L., Sakrejda, Iwona, Sandweiss, Jack H., Saulys, A. C., Savin, Igor A., Schambach, Joachim, Scharenberg, Rolf Paul, Schweda, Kai, Schmitz, Norbert, Schroeder, Lee S., Schüttauf, Andreas, Seger, Janet Elizabeth, Seliverstov, D., Seyboth, Peter, Shahaliev, Ehtiram, Shestermanov, Konstantin E., Shimanskii, S. S., Shvetcov, V. S., Skoro, Goran P., Smirnov, Nikolai, Snellings, Raimond, Sowinski, James, Spinka, Harold. M., Srivastava, Brijesh Kumar, Stephenson, Edward J., Stock, Reinhard, Stolpovsky, Alexander, Strichanov, Michail Nikolaevič, Stringfellow, Blair, Ströbele, Herbert, Struck, Christof, Suaide, Alexandre Alarcon do Passo, Sugarbaker, Evan R., Suire, Christophe Pierre, Šumbera, Michal, Symons, Timothy James MacNeil, Toledo, Alejandro Szanto de, Szarwas, Piotr, Takahashi, Jun, Tang, Aihong, Thomas, James H., Tikhomirov, V., Trainor, Thomas A., Trentalange, Stephen, Tokarev, Mikhail V., Tonjes, Marguerite Belt, Trofimov, V., Tsai, Oleg D., Turner, K., Ullrich, Thomas, Underwood, David G., Van Buren, Gene, VanderMolen, A. M., Vanyashin, A., Vasilevski, I. M., Vasiliev, Alexander Nikolaevich, Vigdor, Steven E., Voloshin, Sergei, Wang, F., Ward, H., Watson, J. W., Wells, R., Wenaus, T., Westfall, Gary D., Whitten, Charles A., Wieman, Howard, Willson, Robert, Wissink, Scott W., Witt, Richard, Xu, N., Xu, Z., Yakutin, A. E., Yamamoto, E., Yang, J., Yepes, Pablo P., Yokosawa, A., Yurevich, Vladimir Yurevich, Zanevski, Y. V., Zborovský, Imrich, Zhang, W. M., Zoulkarneev, R., Zubarev, A. N., Adler, Clemens, Ahammed, Zubayer, Allgower, Chris, Amonett, John, Anderson, Bryon D., Anderson, Mike, Averichev, Georgy S., Balewski, Jan, Barannikova, Olga, Barnby, Lee Stuart, Baudot, Jérome, Bekele, Selemon, Belaga, Victoria Vladimirovna, Bellwied, Rene, Berger, Jens, Bichsel, Hans, Bland, Leslie C., Blyth, Charles O., Bonner, Billy E., Bossingham, R., Boucham, Abdelkrim, Brandin, Andrei V., Cadman, R. V., Caines, Helen Louise, Calderón de la Barca Sánchez, Manuel, Cardenas, Andrés, Carroll, J., Castillo Castellanos, Javier Ernesto, Castro, M., Cebra, Daniel A., Chattopadhyay, Subhasis, Chen, M. L., Chen, Y., Chernenko, Sergey P., Cherney, Michael Gerard, Chikanian, Alexei, Choi, B., Christie, William, Coffin, Jean Pierre, Conin, Laurent, Cormier, Thomas Michael, Cramer, John G., Crawford, Henry J., DeMello, M., Deng, W. S., Derevschikov, Anatoly A., Didenko, Lidia, Draper, James E., Dunin, V. B., Dunlop, James C., Eckardt, Volker, Efimov, Leonid G., Emelianov, V., Engelage, Jon M., Eppley, Geary Wareham, Erazmus, Barbara Ewa, Fachini, Patricia, Finch, Evan, Fisyak, Yuri V., Flierl, Dominik Bernhard, Foley, K. J., Fu, J., Gagunashvili, N., Gans, J., Gaudichet, L., Germain, Marie, Geurts, Frank, Ghazikhanian, Vahe, Grabski, J., Grachov, Oleg A., Greiner, D., Grigoriev, Vladislav A., Guedon, M., Gushin, E., Hallman, Timothy J., Hardtke, David H., Harris, John W., Heffner, Michael D., Heppelmann, Steven Francis, Herston, T., Hippolyte, Boris, Hirsch, Andrew S., Hjort, Eric, Hoffmann, Gerald W., Horsley, M., Huang, Huan Zhong, Humanic, Thomas J., Hümmler, Holm Gero, Igo, George J., Ishihara, A., Ivanshin, Yu. I., Jacobs, Peter Martin, Jacobs, William W., Janik, Michał, Johnson, I., Jones, Peter Graham, Judd, Eleanor G., Kaneta, Kaneta, Kaplan, Morton, Keane, Declan, Kisiel, Adam Ryszard, Klay, Jennifer Lynn, Klein, Spencer Robert, Klyachko, A., Konstantinov, A. S., Kotchenda, L., Kovalenko, A. D., Kramer, M., Kravtsov, Peter, Krueger, Keith William, Kuhn, Christian Claude, Kulikov, Anatoly I., Kunde, Gerd J., Kunz, Christopher Lee, Kutuev, R. Kh., Kuznetsov, A. A., Lakehal-Ayat, L., Lamas-Valverde, Jose, Lamont, Matthew A. C., Landgraf, Jeffery M., Lange, S., Lansdell, Curtis Patrick Leon, Lasiuk, Brian, Laue, Frank, Lebedev, Alexei, LeCompte, T., Lednický, Richard, Leontiev, V. M., Leszczynski, P., LeVine, Micheal J., Li, Q., Lindenbaum, Sam J., Lisa, Michael A., Ljubicic, Tonko A., Llope, William Joseph, LoCurto, G., Long, H., Longacre, Ronald S., Lopez-Noriega, M., Love, William A., Lynn, David, Majka, Richard Daniel, Maliszewski, A., Margetis, Spyridon, Martin, L., Marx, Jay N., Matis, Howard S., Matulenko, Yuri A., McShane, Tom S., Meissner, F., Melnick, Yu., Meščanin, Aleksei P., Messer, Matthias, Miller, M. L., Milosevich, Z., Minaev, Nikolai G., Mitchell, J., Moiseenko, V. A., Moltz, D., Moore, C. F., Morozov, V., de Moura, M. M., Munhoz, Marcelo Gameiro, Mutchler, G. S., Nelson, John M., Nevski, P., Nikitin, Vladimir A., Nogach, Larisa V., Norman, B., Nurušev, Sandibek B., Odyniec, Grazyna Janina, Ogawa, Akio, Okorokov, Vitaly A., Oldenburg, Markus D., Olson, Douglas L., Paic, Guy, Pandey, S. U., Panebratsev, Yuri, Panitkin, Sergei Yurievich, Pavlinov, A. I., Pawlak, Tomasz Jan, Perevoztchikov, V., Peryt, Wiktor Stanislaw, Petrov, V. A., Pinganaud, W., Platner, E., Pluta, Jan Marian, Porile, Norbert T., Porter, Jeffrey Brent, Poskanzer, Arthur M., Potrebenikova, E., Prindle, Duncan J., Pruneau, Claude A., Radomski, S., Rai, Gulshan, Ravel, Olivier, Ray, Robert L., Razin, Sergej V., Reichhold, Dennis Michael, Reid, Jeffrey G., Retiere, Fabrice, Ridiger, Alexey, Ritter, Hans Georg, Roberts, Jabus B., Rogachevski, O. V., Romero, Juan L., Roy, Christelle Sophie, Russ, D., Rykov, Vladimir L., Sakrejda, Iwona, Sandweiss, Jack H., Saulys, A. C., Savin, Igor A., Schambach, Joachim, Scharenberg, Rolf Paul, Schweda, Kai, Schmitz, Norbert, Schroeder, Lee S., Schüttauf, Andreas, Seger, Janet Elizabeth, Seliverstov, D., Seyboth, Peter, Shahaliev, Ehtiram, Shestermanov, Konstantin E., Shimanskii, S. S., Shvetcov, V. S., Skoro, Goran P., Smirnov, Nikolai, Snellings, Raimond, Sowinski, James, Spinka, Harold. M., Srivastava, Brijesh Kumar, Stephenson, Edward J., Stock, Reinhard, Stolpovsky, Alexander, Strichanov, Michail Nikolaevič, Stringfellow, Blair, Ströbele, Herbert, Struck, Christof, Suaide, Alexandre Alarcon do Passo, Sugarbaker, Evan R., Suire, Christophe Pierre, Šumbera, Michal, Symons, Timothy James MacNeil, Toledo, Alejandro Szanto de, Szarwas, Piotr, Takahashi, Jun, Tang, Aihong, Thomas, James H., Tikhomirov, V., Trainor, Thomas A., Trentalange, Stephen, Tokarev, Mikhail V., Tonjes, Marguerite Belt, Trofimov, V., Tsai, Oleg D., Turner, K., Ullrich, Thomas, Underwood, David G., Van Buren, Gene, VanderMolen, A. M., Vanyashin, A., Vasilevski, I. M., Vasiliev, Alexander Nikolaevich, Vigdor, Steven E., Voloshin, Sergei, Wang, F., Ward, H., Watson, J. W., Wells, R., Wenaus, T., Westfall, Gary D., Whitten, Charles A., Wieman, Howard, Willson, Robert, Wissink, Scott W., Witt, Richard, Xu, N., Xu, Z., Yakutin, A. E., Yamamoto, E., Yang, J., Yepes, Pablo P., Yokosawa, A., Yurevich, Vladimir Yurevich, Zanevski, Y. V., Zborovský, Imrich, Zhang, W. M., Zoulkarneev, R., and Zubarev, A. N.

Abstract

We report first results on elliptic flow of identified particles at midrapidity in Au+Au collisions at sqrt[sNN] = 130 GeV using the STAR TPC at RHIC. The elliptic flow as a function of transverse momentum and centrality differs significantly for particles of different masses. This dependence can be accounted for in hydrodynamic models, indicating that the system created shows a behavior consistent with collective hydrodynamical flow. The fit to the data with a simple model gives information on the temperature and flow velocities at freeze-out.

Published
2021

37. Q[subscript weak]: First Direct Measurement of the Proton’s Weak Charge

Author

Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Guo, Fang, Kowalski, Stanley B, Rajotte, Jean-Francois, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Guo, Fang, Kowalski, Stanley B, and Rajotte, Jean-Francois

Abstract

The Q[subscript weak] experiment, which took data at Jefferson Lab in the period 2010 - 2012, will precisely determine the weak charge of the proton by measuring the parity-violating asymmetry in elastic e-p scattering at 1.1 GeV using a longitudinally polarized electron beam and a liquid hydrogen target at a low momentum transfer of Q² = 0.025 (GeV/c)². The weak charge of the proton is predicted by the Standard Model and any significant deviation would indicate physics beyond the Standard Model. The technical challenges and experimental apparatus for measuring the weak charge of the proton will be discussed, as well as the method of extracting the weak charge of the proton. The results from a small subset of the data, that has been published, will also be presented. Furthermore an update will be given of the current status of the data analysis.

Published
2021

39. Observation of the antimatter helium-4 nucleus

Author

Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross Cameron, Hays-Wehle, James Prewitt, Leight, William Axel, Milner, Richard G, Redwine, Robert P, Seele, Joseph Patrick, Steadman, Stephen G, Surrow, Bernd, van Nieuwenhuizen, Gerrit J, Walker, Meredith, STAR Collaboration, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross Cameron, Hays-Wehle, James Prewitt, Leight, William Axel, Milner, Richard G, Redwine, Robert P, Seele, Joseph Patrick, Steadman, Stephen G, Surrow, Bernd, van Nieuwenhuizen, Gerrit J, Walker, Meredith, and STAR Collaboration

Abstract

High-energy nuclear collisions create an energy density similar to that of the Universe microseconds after the Big Bang; in both cases, matter and antimatter are formed with comparable abundance. However, the relatively short-lived expansion in nuclear collisions allows antimatter to decouple quickly from matter, and avoid annihilation. Thus, a high-energy accelerator of heavy nuclei provides an efficient means of producing and studying antimatter. The antimatter helium-4 nucleus (), also known as the anti-(), consists of two antiprotons and two antineutrons (baryon number B = '4). It has not been observed previously, although the -particle was identified a century ago by Rutherford and is present in cosmic radiation at the ten per cent level. Antimatter nuclei with B'1 have been observed only as rare products of interactions at particle accelerators, where the rate of antinucleus production in high-energy collisions decreases by a factor of about 1,000 with each additional antinucleon. Here we report the observation of, the heaviest observed antinucleus to date. In total, 18 counts were detected at the STAR experiment at the Relativistic Heavy Ion Collider (RHIC; ref. 6) in 10 9 recorded gold-on-gold (Au+Au) collisions at centre-of-mass energies of 200 GeV and 62 GeV per nucleon-nucleon pair. The yield is consistent with expectations from thermodynamic and coalescent nucleosynthesis models, providing an indication of the production rate of even heavier antimatter nuclei and a benchmark for possible future observations of in cosmic radiation.

Published
2019

40. Q[subscript weak]: First Direct Measurement of the Proton’s Weak Charge

Author

Balewski, Jan T., Guo, Fang, Kowalski, Stanley B, Rajotte, Jean-Francois, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Guo, Fang, Kowalski, Stanley B, and Rajotte, Jean-Francois

Subjects
Nuclear Experiment
Abstract

The Q[subscript weak] experiment, which took data at Jefferson Lab in the period 2010 - 2012, will precisely determine the weak charge of the proton by measuring the parity-violating asymmetry in elastic e-p scattering at 1.1 GeV using a longitudinally polarized electron beam and a liquid hydrogen target at a low momentum transfer of Q² = 0.025 (GeV/c)². The weak charge of the proton is predicted by the Standard Model and any significant deviation would indicate physics beyond the Standard Model. The technical challenges and experimental apparatus for measuring the weak charge of the proton will be discussed, as well as the method of extracting the weak charge of the proton. The results from a small subset of the data, that has been published, will also be presented. Furthermore an update will be given of the current status of the data analysis.

Published
2017

43. Q[subscript weak]: First Direct Measurement of the Proton’s Weak Charge

Author

Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Guo, Fang, Kowalski, Stanley B, Rajotte, Jean-Francois, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Guo, Fang, Kowalski, Stanley B, and Rajotte, Jean-Francois

Abstract

The Q[subscript weak] experiment, which took data at Jefferson Lab in the period 2010 - 2012, will precisely determine the weak charge of the proton by measuring the parity-violating asymmetry in elastic e-p scattering at 1.1 GeV using a longitudinally polarized electron beam and a liquid hydrogen target at a low momentum transfer of Q² = 0.025 (GeV/c)². The weak charge of the proton is predicted by the Standard Model and any significant deviation would indicate physics beyond the Standard Model. The technical challenges and experimental apparatus for measuring the weak charge of the proton will be discussed, as well as the method of extracting the weak charge of the proton. The results from a small subset of the data, that has been published, will also be presented. Furthermore an update will be given of the current status of the data analysis.

Published
2018

44. LERF - New Life for the Jefferson Lab FEL

Author

Tennant, Chris, Alarcon, Ricardo, Balewski, Jan, Benson, Stephen, Bernauer, Jan, Bessuille, Jason, Blyth, David, Boyce, James, Cao, Tongtong, Cervantes, Raphael, Coleman, James, Corliss, Ross, Cowan, Ray, Dehmelt, Klaus, Deshpande, Abhay, Dipert, Robert, Dongwi, Bishoy, Douglas, David, Epstein, Charles, Evtushenko, Pavel, Feege, Nils, Fisher, Peter, Frierson, Shawn, Friščić, Ivica, Garçon, Michel, Gubeli, Joseph, GuèYe, Paul, Hasell, Douglas, Hernandez-Garcia, Carlos, Ice, Lauren, Ihloff, Ernie, Jordan, Kevin, Kalantarians, Narbe, Keith, Christopher, Kelsey, James, Kohl, Michael, Lee, Sangbaek, Legg, Robert, Liyanage, Anusha, McCaughan, Michael, Milner, Richard, Moran, Patrick, Nazeer, Jesmin, Palumbo, Daniel, Randall, Glenn, Satogata, Todd, Spata, Michael, Steadman, Stephen, Surrow, Bernd, Thorpe, Brianna, Tiefenback, Michael, Tschalär, Chris, Vidal, Christopher, Wang, Yimin, Zhang, Shukui, Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
solenoid, electron, [PHYS.PHYS.PHYS-ACC-PH]Physics [physics]/Physics [physics]/Accelerator Physics [physics.acc-ph], 01 natural sciences, 030218 nuclear medicine & medical imaging, Accelerator Physics, target, operation, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, WG5: Applications, linac, 010306 general physics
Abstract

In 2012 Jefferson Laboratory's energy recovery linac (ERL) driven Free Electron Laser successful completed a transmission test in which high current CW beam (4.3 mA at 100 MeV) was transported through a 2 mm aperture for 7 hours with beam losses as low as 3 ppm. The purpose of the run was to mimic an internal gas target for DarkLight* - an experiment designed to search for a dark matter particle. The ERL was not run again until late 2015 for a brief re-commissioning in preparation for the next phase of DarkLight. In the intervening years, the FEL was rebranded as the Low Energy Recirculator Facility (LERF), while organizationally the FEL division was absorbed into the Accelerator division. In 2016 several weeks of operation were allocated to configure the machine for Darklight with the purpose of exercising - for the first time - an internal gas target in an ERL. Despite a number of challenges, including the inability to energy recover, beam was delivered to a target of thickness 10¹⁸ cm⁻² which represents a 3 order of magnitude increase in thickness from previous internal target experiments. Details of the machine configuration and operational experience will be discussed., Proceedings of the 59th ICFA Advanced Beam Dynamics Workshop on Energy Recovery Linacs, ERL2017, Geneva, Switzerland

Published
2017

45. [J over ψ] polarization in p+p collisions at √s = 200 TeV in STAR

Author

Walker, M., van Nieuwenhuizen, Gerrit Jan, Stevens, Justin, Balewski, Jan T., Corliss, Ross, Hays-Wehle, James Prewitt, Leight, William Axel, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Walker, M., van Nieuwenhuizen, Gerrit Jan, Stevens, Justin, Balewski, Jan T., Corliss, Ross, Hays-Wehle, James Prewitt, and Leight, William Axel

Subjects
Nuclear Experiment
Abstract

We report on a polarization measurement of inclusive [J over ψ] mesons in the di-electron decay channel at mid-rapidity at 2, United States. Dept. of Energy. Office of Science, National Science Foundation (U.S.)

Published
2014

47. The Q[superscript p][subscript Weak] experiment

Author

Androic, D., Armstrong, D. S, Asaturyan, A., Averett, T., Beaufait, J., Beminiwattha, R. S, Benesch, J., Benmokhtar, F., Birchall, J., Carlini, R. D, Cornejo, J. C, Covrig, S., Dalton, M. M, Davis, C. A, Deconinck, W., Diefenbach, J., Dowd, J. F, Dunne, J. A, Dutta, D., Duvall, W. S, Elaasar, M., Falk, W. R, Finn, J. M, Forest, T., Gaskell, D., Gericke, M. T W, Grames, J., Gray, V. M, Grimm, K., Hoskins, J. R, Johnston, K., Jones, D., Jones, M., Jones, R., Kargiantoulakis, M., King, P. M, Korkmaz, E., Leacock, J., Leckey, J., Lee, A. R, Lee, J. H, Lee, L., MacEwan, S., Mack, D., Magee, J. A, Mahurin, R., Mammei, J., Martin, J., McHugh, M., Meekins, D., Mei, J., Michaels, R., Micherdzinska, A., Myers, K. E, Mkrtchyan, A., Mkrtchyan, H., Narayan, A., Ndukum, L. Z, Nelyubin, V., Nuruzzaman, N., van Oers, W. T H, Opper, A. K, Page, S. A, Pan, J., Paschke, K., Phillips, S. K, Pitt, M. L, Poelker, M., Ramsay, W. D, Roche, J., Sawatzky, B., Seva, T., Shabestari, M. H, Silwal, R., Simicevic, N., Smith, G., Solvignon, P., Spayde, D. T, Subedi, A., Subedi, R., Suleiman, R., Tadevosyan, V., Tobias, W. A, Tvaskis, V., Waidyawansa, B., Wang, P., Wells, S. P, Wood, S. A, Yang, S., Young, R. D, Zhamkochyan, S., Balewski, Jan T., Dow, Karen Ann, Guo, Fang, Kowalski, Stanley B, Rajotte, Jean-Francois, Zou, David, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Dow, Karen Ann, Guo, Fang, Kowalski, Stanley B, Rajotte, Jean-Francois, and Zou, David

Abstract

In May 2012, the Q[superscript p][subscript Weak] collaboration completed a two year measurement program to determine the weak charge of the proton Q[superscript p][subscript Weak] = ( 1 - 4sin [superscript 2] θ[subscript W] at the Thomas Jefferson National Accelerator Facility (TJNAF). The experiment was designed to produce a 4.0 % measurement of the weak charge, via a 2.5 % measurement of the parity violating asymmetry in the number of elastically scattered 1.165 GeV electrons from protons, at forward angles. At the proposed precision, the experiment would produce a 0.3 % measurement of the weak mixing angle at a momentum transfer of Q[superscript 2] = 0.026 GeV[superscript 2], making it the most precise stand alone measurement of the weak mixing angle at low momentum transfer. In combination with other parity measurements, Q[superscript p][subscript Weak] will also provide a high precision determination of the weak charges of the up and down quarks. At the proposed precision, a significant deviation from the Standard Model prediction could be a signal of new physics at mass scales up to ≃ 6 TeV, whereas agreement would place new and significant constraints on possible Standard Model extensions at mass scales up to ≃ 2 TeV. This paper provides an overview of the physics and the experiment, as well as a brief look at some preliminary diagnostic and analysis data.

Published
2013

48. Evolution of the differential transverse momentum correlation function with centrality in Au+Au collisions at √sNN = 200 GeV

Author

Betancourt, Michael Joseph, Balewski, Jan T., Corliss, Ross Cameron, Hays-Wehle, James Prewitt, Leight, William Axel, Milner, Richard G, Redwine, Robert P, Seele, Joseph Patrick, Steadman, Stephen G, Surrow, Bernd, van Nieuwenhuizen, Gerrit J, Walker, Matthew H, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Betancourt, Michael Joseph, Balewski, Jan T., Corliss, Ross Cameron, Hays-Wehle, James Prewitt, Leight, William Axel, Milner, Richard G, Redwine, Robert P, Seele, Joseph Patrick, Steadman, Stephen G, Surrow, Bernd, van Nieuwenhuizen, Gerrit J, and Walker, Matthew H

Subjects
Nuclear Experiment
Abstract

We present first measurements of the evolution of the differential transverse momentum correlation function, C, with collision centrality in Au. +. Au interactions at √sNN=200 GeV. This observable exhibits a strong dependence on collision centrality that is qualitatively similar to that of number correlations previously reported. We use the observed longitudinal broadening of the near-side peak of C with increasing centrality to estimate the ratio of the shear viscosity to entropy density, η/s, of the matter formed in central Au. +. Au interactions. We obtain an upper limit estimate of η/s that suggests that the produced medium has a small viscosity per unit entropy. Keywords: Azimuthal correlations, QGP, Heavy ion collisions, United States. Department of Energy. Office of Nuclear Physics, United States. Department of Energy. High Energy Physics Division, National Science Foundation (U.S.), Alfred P. Sloan Foundation

Published
2011

49. Neutral pion cross section and spin asymmetries at intermediate pseudorapidity in polarized proton collisions at √s = 200 GeV

Author

Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross, Hays-Wehle, James Prewitt, Leight, William Axel, Stevens, Justin, van Nieuwenhuizen, Gerrit Jan, Walker, M., Adamczyk, L., Adkins, J. K., Agakishiev, G., Aggarwal, M. M., Ahammed, Z., Alekseev, I., Alford, J., Anson, C. D., Aparin, A., Arkhipkin, D., Aschenauer, E. C., Averichev, G. S., Banerjee, A., Barber, B., Barnovska, Z., Beavis, D. R., Bellwied, R., Bhasin, A., Bhati, A. K., Bhattarai, P., Bichsel, H., Bielcik, J., Bielcikova, J., Bland, L. C., Bordyuzhin, I. G., Borowski, W., Bouchet, J., Brandin, A. V., Bridgeman, A., Brovko, S. G., Bültmann, S., Bunzarov, I., Burton, T. P., Butterworth, J., Caines, H., Calderon De La Barca Sanchez, M., Cebra, D., Cendejas, R., Cervantes, M. C., Chaloupka, P., Chang, Z., Chattopadhyay, S., Chen, H. F., Chen, J. H., Chen, L., Cheng, J., Cherney, M., Chikanian, A., Christie, W., Chwastowski, J., Codrington, M. J. M., Cramer, J. G., Crawford, H. J., Cui, X., Das, S., Davila Leyva, A., De Silva, L. C., Debbe, R. R., Dedovich, T. G., Deng, J., Derevschikov, A. A., Derradi de Souza, R., Dhamija, S., di Ruzza, B., Didenko, L., Dilks, C., Ding, F., Dion, A., Djawotho, P., Dong, X., Drachenberg, J. L., Draper, J. E., Du, C. M., Dunkelberger, L. E., Dunlop, James C., Efimov, L. G., Engelage, J., Engle, K. S., Eppley, G., Eun, L., Evdokimov, O., Fatemi, Renee, Fazio, S., Fedorisin, J., Fersch, R. G., Filip, P., Finch, E., Fisyak, Y., Flores, C. E., Gagliardi, C. A., Gangadharan, D. R., Garand, D., Geurts, F. J. M., Gibson, A., Girard, M., Gliske, S., Grosnick, D., Guo, Y., Gupta, A., Gupta, S., Guryn, W., Haag, B., Hajkova, O., Hamed, A., Han, L.-X., Haque, R., Harris, J. W., He, W., Heppelmann, S., Hirsch, A., Hoffmann, G. W., Hofman, D. J., Horvat, S., Huang, B., Huang, H. Z., Huck, P., Humanic, T. J., Igo, G., Jacobs, W. W., Jang, H., Jena, C., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kauder, K., Ke, H. W., Keane, D., Kechechyan, A., Kesich, A., Khan, Z. H., Kikola, D. P., Kisel, I., Kisiel, A., Koetke, D. D., Kollegger, T., Konzer, J., Koralt, I., Korsch, W., Kotchenda, L., Kravtsov, P., Krueger, K., Kulakov, I., Kumar, L., Kycia, R. A., Lamont, M. A. C., Landgraf, J. M., Landry, K. D., Lauret, J., Lebedev, A., Lednicky, R., Lee, J. H., LeVine, M. J., Li, C., Li, W., Li, X., Li, Y., Li, Z. M., Lima, L. M., Lisa, M. A., Liu, F., Ljubicic, T., Llope, W. J., Longacre, R. S., Luo, X., Ma, G. L., Ma, Y. G., Madagodagettige Don, D. M. M. D., Mahapatra, D. P., Majka, R., Manweiler, R., Margetis, S., Markert, C., Masui, H., Matis, H. S., McDonald, D., McShane, T. S., Minaev, N. G., Mioduszewski, S., Mohanty, B., Mondal, M. M., Morozov, D. A., Munhoz, M. G., Mustafa, M. K., Naglis, M., Nandi, B. K., Nasim, Md., Nayak, T. K., Nelson, J. M., Nogach, L. V., Noh, S. Y., Nord, P. M., Novak, J., Nurushev, S. B., Odyniec, G., Ogawa, A., Oh, K., Ohlson, A., Okorokov, V., Oldag, E. W., Oliveira, R. A. N., Olson, D., Pachr, M., Page, B. S., Pal, S. K., Pan, Y. X., Pandit, Y., Panebratsev, Y., Pawlak, T., Pawlik, B., Pei, H., Perkins, C., Peryt, W., Peterson, A., Pile, P., Planinic, M., Pluta, J., Plyku, D., Pochron, W., Poljak, N., Porter, J., Poskanzer, A. M., Powell, C. B., Pruneau, C., Pruthi, N. K., Przybycien, M., Pujahari, P. R., Putschke, J., Qiu, H., Ramachandran, S., Raniwala, R., Raniwala, S., Ray, R. L., Riley, C. K., Ritter, H. G., Roberts, J. B., Rogachevskiy, O. V., Romero, J. L., Ross, J. F., Roy, A., Ruan, L., Rusnak, J., Sahoo, N. R., Sahu, P. K., Sakrejda, I., Salur, S., Sandacz, A., Sandweiss, J., Sangaline, E., Sarkar, A., Schambach, J., Scharenberg, R. P., Schaub, J., Schmah, A. M., Schmidke, W. B., Schmitz, N., Seger, J., Selyuzhenkov, I., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, M., Sharma, B., Shen, W. Q., Shi, S. S., Shou, Q. Y., Sichtermann, E. P., Singaraju, R. N., Skoby, M. J., Smirnov, D., Smirnov, N., Solanki, D., Sorensen, P., deSouza, U. G., Spinka, H. M., Srivastava, B., Stanislaus, T. D. S., Stock, R., Strikhanov, M., Stringfellow, B., Suaide, A. A. P., Sumbera, M., Sun, X., Sun, X. M., Sun, Y., Sun, Z., Surrow, B., Svirida, D. N., Symons, T. J. M., Szanto de Toledo, A., Takahashi, J., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Tokarev, M., Trentalange, S., Tribble, R. E., Tribedy, P., Trzeciak, B. A., Tsai, O. D., Turnau, J., Ullrich, T., Underwood, D. G., Van Buren, G., Vanfossen, J. A., Varma, R., Vasconcelos, G. M. S., Vasiliev, A. N., Vertesi, R., Videbæk, F., Viyogi, Y. P., Vokal, S., Voloshin, S. A., Vossen, A., Wada, M., Wang, F., Wang, G., Wang, H., Wang, J. S., Wang, Q., Wang, X. L., Wang, Y., Webb, G., Webb, J. C., Westfall, G. D., Wieman, H., Wissink, S. W., Witt, R., Wu, Y. F., Xiao, Z., Xie, W., Xin, K., Xu, H., Xu, N., Xu, Q. H., Xu, W., Xu, Y., Xu, Z., Yan, W., Yang, C., Yang, Y., Ye, Z., Yepes, P., Yi, L., Yip, K., Yoo, I-K., Zawisza, Y., Zbroszczyk, H., Zha, W., Zhang, J. B., Zhang, S., Zhang, X. P., Zhang, Y., Zhang, Z. P., Zhao, F., Zhao, J., Zhong, C., Zhu, X., Zhu, Y. H., Zoulkarneeva, Y., Zyzak, M., Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross, Hays-Wehle, James Prewitt, Leight, William Axel, Stevens, Justin, van Nieuwenhuizen, Gerrit Jan, Walker, M., Adamczyk, L., Adkins, J. K., Agakishiev, G., Aggarwal, M. M., Ahammed, Z., Alekseev, I., Alford, J., Anson, C. D., Aparin, A., Arkhipkin, D., Aschenauer, E. C., Averichev, G. S., Banerjee, A., Barber, B., Barnovska, Z., Beavis, D. R., Bellwied, R., Bhasin, A., Bhati, A. K., Bhattarai, P., Bichsel, H., Bielcik, J., Bielcikova, J., Bland, L. C., Bordyuzhin, I. G., Borowski, W., Bouchet, J., Brandin, A. V., Bridgeman, A., Brovko, S. G., Bültmann, S., Bunzarov, I., Burton, T. P., Butterworth, J., Caines, H., Calderon De La Barca Sanchez, M., Cebra, D., Cendejas, R., Cervantes, M. C., Chaloupka, P., Chang, Z., Chattopadhyay, S., Chen, H. F., Chen, J. H., Chen, L., Cheng, J., Cherney, M., Chikanian, A., Christie, W., Chwastowski, J., Codrington, M. J. M., Cramer, J. G., Crawford, H. J., Cui, X., Das, S., Davila Leyva, A., De Silva, L. C., Debbe, R. R., Dedovich, T. G., Deng, J., Derevschikov, A. A., Derradi de Souza, R., Dhamija, S., di Ruzza, B., Didenko, L., Dilks, C., Ding, F., Dion, A., Djawotho, P., Dong, X., Drachenberg, J. L., Draper, J. E., Du, C. M., Dunkelberger, L. E., Dunlop, James C., Efimov, L. G., Engelage, J., Engle, K. S., Eppley, G., Eun, L., Evdokimov, O., Fatemi, Renee, Fazio, S., Fedorisin, J., Fersch, R. G., Filip, P., Finch, E., Fisyak, Y., Flores, C. E., Gagliardi, C. A., Gangadharan, D. R., Garand, D., Geurts, F. J. M., Gibson, A., Girard, M., Gliske, S., Grosnick, D., Guo, Y., Gupta, A., Gupta, S., Guryn, W., Haag, B., Hajkova, O., Hamed, A., Han, L.-X., Haque, R., Harris, J. W., He, W., Heppelmann, S., Hirsch, A., Hoffmann, G. W., Hofman, D. J., Horvat, S., Huang, B., Huang, H. Z., Huck, P., Humanic, T. J., Igo, G., Jacobs, W. W., Jang, H., Jena, C., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kauder, K., Ke, H. W., Keane, D., Kechechyan, A., Kesich, A., Khan, Z. H., Kikola, D. P., Kisel, I., Kisiel, A., Koetke, D. D., Kollegger, T., Konzer, J., Koralt, I., Korsch, W., Kotchenda, L., Kravtsov, P., Krueger, K., Kulakov, I., Kumar, L., Kycia, R. A., Lamont, M. A. C., Landgraf, J. M., Landry, K. D., Lauret, J., Lebedev, A., Lednicky, R., Lee, J. H., LeVine, M. J., Li, C., Li, W., Li, X., Li, Y., Li, Z. M., Lima, L. M., Lisa, M. A., Liu, F., Ljubicic, T., Llope, W. J., Longacre, R. S., Luo, X., Ma, G. L., Ma, Y. G., Madagodagettige Don, D. M. M. D., Mahapatra, D. P., Majka, R., Manweiler, R., Margetis, S., Markert, C., Masui, H., Matis, H. S., McDonald, D., McShane, T. S., Minaev, N. G., Mioduszewski, S., Mohanty, B., Mondal, M. M., Morozov, D. A., Munhoz, M. G., Mustafa, M. K., Naglis, M., Nandi, B. K., Nasim, Md., Nayak, T. K., Nelson, J. M., Nogach, L. V., Noh, S. Y., Nord, P. M., Novak, J., Nurushev, S. B., Odyniec, G., Ogawa, A., Oh, K., Ohlson, A., Okorokov, V., Oldag, E. W., Oliveira, R. A. N., Olson, D., Pachr, M., Page, B. S., Pal, S. K., Pan, Y. X., Pandit, Y., Panebratsev, Y., Pawlak, T., Pawlik, B., Pei, H., Perkins, C., Peryt, W., Peterson, A., Pile, P., Planinic, M., Pluta, J., Plyku, D., Pochron, W., Poljak, N., Porter, J., Poskanzer, A. M., Powell, C. B., Pruneau, C., Pruthi, N. K., Przybycien, M., Pujahari, P. R., Putschke, J., Qiu, H., Ramachandran, S., Raniwala, R., Raniwala, S., Ray, R. L., Riley, C. K., Ritter, H. G., Roberts, J. B., Rogachevskiy, O. V., Romero, J. L., Ross, J. F., Roy, A., Ruan, L., Rusnak, J., Sahoo, N. R., Sahu, P. K., Sakrejda, I., Salur, S., Sandacz, A., Sandweiss, J., Sangaline, E., Sarkar, A., Schambach, J., Scharenberg, R. P., Schaub, J., Schmah, A. M., Schmidke, W. B., Schmitz, N., Seger, J., Selyuzhenkov, I., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, M., Sharma, B., Shen, W. Q., Shi, S. S., Shou, Q. Y., Sichtermann, E. P., Singaraju, R. N., Skoby, M. J., Smirnov, D., Smirnov, N., Solanki, D., Sorensen, P., deSouza, U. G., Spinka, H. M., Srivastava, B., Stanislaus, T. D. S., Stock, R., Strikhanov, M., Stringfellow, B., Suaide, A. A. P., Sumbera, M., Sun, X., Sun, X. M., Sun, Y., Sun, Z., Surrow, B., Svirida, D. N., Symons, T. J. M., Szanto de Toledo, A., Takahashi, J., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Tokarev, M., Trentalange, S., Tribble, R. E., Tribedy, P., Trzeciak, B. A., Tsai, O. D., Turnau, J., Ullrich, T., Underwood, D. G., Van Buren, G., Vanfossen, J. A., Varma, R., Vasconcelos, G. M. S., Vasiliev, A. N., Vertesi, R., Videbæk, F., Viyogi, Y. P., Vokal, S., Voloshin, S. A., Vossen, A., Wada, M., Wang, F., Wang, G., Wang, H., Wang, J. S., Wang, Q., Wang, X. L., Wang, Y., Webb, G., Webb, J. C., Westfall, G. D., Wieman, H., Wissink, S. W., Witt, R., Wu, Y. F., Xiao, Z., Xie, W., Xin, K., Xu, H., Xu, N., Xu, Q. H., Xu, W., Xu, Y., Xu, Z., Yan, W., Yang, C., Yang, Y., Ye, Z., Yepes, P., Yi, L., Yip, K., Yoo, I-K., Zawisza, Y., Zbroszczyk, H., Zha, W., Zhang, J. B., Zhang, S., Zhang, X. P., Zhang, Y., Zhang, Z. P., Zhao, F., Zhao, J., Zhong, C., Zhu, X., Zhu, Y. H., Zoulkarneeva, Y., and Zyzak, M.

Abstract

The differential cross section and spin asymmetries for neutral pions produced within the intermediate pseudorapidity range 0.8 < η < 2.0 in polarized proton-proton collisions at √s = 200 GeV are presented. Neutral pions were detected using the end cap electromagnetic calorimeter in the STAR detector at RHIC. The cross section was measured over a transverse momentum range of 5 < p[subscript T] < 16 GeV/c and is found to agree with a next-to-leading order perturbative QCD calculation. The longitudinal double-spin asymmetry A[subscript LL] is measured in the same pseudorapidity range and spans a range of Bjorken-x down to x ≈ 0.01. The measured A[subscript LL] is consistent with model predictions for varying degrees of gluon polarization. The parity-violating asymmetry A[subscript L] is also measured and found to be consistent with zero. The transverse single-spin asymmetry A[subscript N] is measured over a previously unexplored kinematic range in Feynman-x and p[subscript T]. Such measurements may aid our understanding of the onset and kinematic dependence of the large asymmetries observed at more forward pseudorapidity (η ≈ 3) and their underlying mechanisms. The A[subscript N] results presented are consistent with a twist-3 model prediction of a small asymmetry over the present kinematic range., United States. Dept. of Energy. Office of Nuclear Physics, United States. Dept. of Energy. Office of High Energy Physics, National Science Foundation (U.S.)

Published
2014

50. Fluctuations of charge separation perpendicular to the event plane and local parity violation in √S[subscript NN] = 200 GeV Au + Au collisions at the BNL Relativistic Heavy Ion Collider

Author

Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross, Hays-Wehle, James Prewitt, Leight, William Axel, Stevens, Justin, van Nieuwenhuizen, Gerrit Jan, Walker, M., Massachusetts Institute of Technology. Laboratory for Nuclear Science, Balewski, Jan T., Betancourt, Michael Joseph, Corliss, Ross, Hays-Wehle, James Prewitt, Leight, William Axel, Stevens, Justin, van Nieuwenhuizen, Gerrit Jan, and Walker, M.

Abstract

Previous experimental results based on data (∼15 × 10[superscript 6] events) collected by the STAR detector at the BNL Relativistic Heavy Ion Collider suggest event-by-event charge-separation fluctuations perpendicular to the event plane in noncentral heavy-ion collisions. Here we present the correlator previously used split into its two component parts to reveal correlations parallel and perpendicular to the event plane. The results are from a high-statistics 200-GeV Au + Au collisions data set (57 × 10[superscript 6] events) collected by the STAR experiment. We explicitly count units of charge separation from which we find clear evidence for more charge-separation fluctuations perpendicular than parallel to the event plane. We also employ a modified correlator to study the possible P-even background in same- and opposite-charge correlations, and find that the P-even background may largely be explained by momentum conservation and collective motion., Brookhaven National Laboratory, Lawrence Berkeley National Laboratory, Open Science Grid, United States. Dept. of Energy. Office of Science, National Science Foundation (U.S.), Alfred P. Sloan Foundation

Published
2014

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