Results for “High efficiency acceleration” — Page 40

DOE OSTI2024

Mechanisms of electromagnetic field control on mineral scaling in brackish water reverse osmosis: Combined homogenous and heterogeneous nucleation

Electromagnetic field (EMF) treatment has emerged as a promising approach for scaling control due to its cost-effectiveness, simplicity, and low energy consumption. However, there is a limited understanding of the mechanisms by which applied EMF impacts mineral scaling in reverse osmosis (RO) systems. This has led to inconclusive and varied results and uncertainties regarding its effectiveness. This study elucidates the impacts of EMF on homogenous and heterogeneous nucleation and membrane performance during RO desalination of different feedwaters. Our results reveal that EMF exhibits greater efficacy in treating near-saturated water (SI∼0), especially when coupled with extended hydraulic flushing (HF). For saturated brackish water desalination, heterogeneous scaling predominantly occurs on membrane surfaces, with the effectiveness of EMF in inhibiting scaling primarily attributed to the hydration effect. In supersaturated solutions, EMF promotes bulk precipitation due to the magnetohydrodynamic effect, quickly blocking membrane pores. Thus, when the saturation reaches a certain high level during RO desalination, magnetohydrodynamic EMF effects can accelerate flux decline caused by homogeneous scaling. In conclusion, this work provides an efficient method for predicting EMF efficiency, emphasizing the importance of saturation conditions and HF cleaning duration in determining membrane performance, suggesting these show promise for improving undersaturated or near-saturated feedwater desalination via RO.

42 ENGINEERING↗

DOE OSTI2025

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗

DOE OSTI2025

Advancing X-ray quantum imaging through Monte-Carlo simulations

Imaging with X-rays poses fundamental limits due to radiation damage of the highly energetic photons. This becomes problematic for sensitive biological systems such as subcellular structures. Lowering the radiation dose, without sacrificing the signal-to-noise ratio, would be desirable for any kind of imaging modalities involving X-rays. To achieve this goal, quantum imaging with entangled X-ray photons constitutes a promising route. Production of biphotons have been demonstrated in the X-ray regime by the process of Spontaneous Parametric Down-Conversion (SPDC). However, compared to SPDC in the regime of visible light, the production rate for X-ray biphotons is extremely low. With the introduction of new high average brightness X-ray sources, such as 4th generation synchrotrons and high repetition rate Free-Electron X-ray Lasers (XFEL), quantum imaging may become practical. We introduce a ray tracing approach using Monte-Carlo sampling, specifically designed for quantum imaging with entangled X-ray photons generated by SPDC. By simulation, the superior image quality of quantum over classical imaging methods is demonstrated using realistic experimental conditions available at high repetition rate XFELs. With these simulations, we can efficiently assist the design of future experiments at beam lines, which can substantially accelerate the advancement of X-ray quantum imaging and reduce costs.

Entangled Photons↗

DOE OSTI2014

GAMMA-RAY EMISSION FROM SUPERNOVA REMNANT INTERACTIONS WITH MOLECULAR CLUMPS

Observations of the middle-aged supernova remnants IC 443, W28, and W51C indicate that the brightnesses at GeV and TeV energies are correlated with each other and with regions of molecular clump interaction, but not with the radio synchrotron brightness. We suggest that the radio emission is primarily associated with a radiative shell in the interclump medium of a molecular cloud, while the γ-ray emission is primarily associated with the interaction of the radiative shell with molecular clumps. The shell interaction produces a high pressure region, so that the γ-ray luminosity can be approximately reproduced even if shock acceleration of particles is not efficient, provided that energetic particles are trapped in the cooling region. In this model, the spectral shape ≳ 2 GeV is determined by the spectrum of cosmic ray protons. Models in which diffusive shock acceleration determines the spectrum tend to underproduce TeV emission because of the limiting particle energy that is attained.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Real-Time GPU-Accelerated OFDR With an Integrated Auxiliary Interferometer

A GPU-accelerated optical frequency domain reflectometry (OFDR) system with an improved integrated auxiliary interferometer is proposed. Unlike conventional approaches that require separate auxiliary interferometers and multiple detection channels, the proposed OFDR system embeds this functionality directly into the signal via an intentional beat component. This enables self-calibration of laser nonlinearity while maintaining a cost-effective hardware configuration. Building on this simplified configuration, the system leverages GPU acceleration with an NVIDIA RTX 4070 Ti to achieve real-time performance, delivering high-throughput signal processing for continuous OFDR interrogation. The signal processing pipeline comprises signal capture, resampling for nonlinearity compensation, and frequency shift computation, all optimized for parallel execution. Hardware benchmarking demonstrates substantial acceleration over CPU implementations, achieving up to a 45× speedup for resampling and frequency shift computations and enabling processing latencies below 30 ms. Thermal response validation is conducted under two complementary scenarios: localized heating using a water bath and cryogenic-temperature conditions using liquid nitrogen. Under localized heating, the system achieves an accuracy of 0.249 °C with a thermal sensitivity of 5.971 GHz/°C, while cryogenic-temperature validation demonstrates a frequency shift response with a sensitivity of 2.383 GHz/°C and an accuracy of 2.04 °C. The high acceleration of the proposed GPU-accelerated OFDR system and its accuracy are achieved by exploiting CUDA-based stride indexing, enabling efficient parallel segmentation and processing of large datasets without additional memory copies. The benchmarking results confirm the robustness, accuracy, and deployability of the proposed OFDR system across a wide temperature range, establishing it as a practical platform for real-time distributed fiber sensing in structurally dynamic environments.

Harb, Salah [Lawrence Berkeley National Laboratory↗

DOE OSTI2022

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it remains notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes the first GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs' inference efficiency. Specifically, on the algorithm level, GCoD integrates a divide and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently outperforms state-of-the-art designs in terms of accelerator efficiency while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices to better understand its advantages. All codes and pre-trained models will be released upon acceptance.

You, Haoran↗

DOE OSTI2022

Nek5000/RS Performance on Advanced GPU Architectures

We demonstrate NekRS performance results on various advanced GPU architectures. NekRS is a GPU-accelerated version of Nek5000 that is targeting high performance on forthcoming exascale platforms. It is being developed in DOE’s Center of Efficient Exascale Discretizations (CEED), which is one of the co-design centers under the Exascale Computing Project (ECP). In this report, we consider Frontier, Crusher, Spock, Polaris, Perlmutter, ThetaGPU and Summit. Simulations are performed using ExaSMR’s 17x17 rod-bundle geometries with different problem sizes. The report focuses on strong scaling performance and analysis. Many of the results shown in this report are the outcome from participation in the ALCF GPU Hackathon on Polaris, which was held on 7/19/22, and 7/26–7/28/22. Members of the Nek5000/RS team included Misun Min, Yu-Hsiang Lan, Paul Fischer and Thilina Rathnayake. Mentors were Kris Rowe (ALCF) and Peng Wang (NVIDIA). Also presented in this report are results on Frontier, which were obtained in collaboration with John Holmen at OLCF.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗

NASA NTRS1974

Measurements of the spectrum of galactic electrons at very high energies

A counter telescope has been exposed in three balloon flights in 1970 to measure the flux and energy spectrum of cosmic ray electrons between 10 and 1000 GeV. This instrument has been modified by incorporating a large area CsI crystal as well as highly efficient time of flight circuitry, and was flown again twice during 1972. The methods of data analysis are based on extensive accelerator calibrations at SLAC. The resulting electron spectrum fits well to a single power law with an index (gamma) equal to 2.66 over the whole energy region. No obvious steepening can be observed, although statistical uncertainties prohibit definite claims beyond 250 GeV.

Mueller, D.↗

NASA NTRSDate not supplied

Space Nuclear Propulsion for Deep Space Science Missions

The use of nuclear thermal propulsion (NTP) 1 and nuclear electric propulsion (NEP) 2 systems on deep space science missions to the outer planets and into the interstellar medium 3 can yield significant spacecraft system and mission performance benefits and improvements relative to the use of conventional chemical propulsion systems. Several recent and ongoing programs are developing the technologies and systems required to realize a near-term deep space nuclear propulsion capability. NTP provides improved propulsion efficiencies compared to chemical propulsion, while also providing substantial thrust. This combination of high thrust and increased specific impulse (I_sp) provides high acceleration and extended thrusting periods, enabling greatly reduced trip-times on certain types of missions compared to various propulsive alternatives. For examples, compared to a baseline mission using chemical propulsion, NTP-powered missions to Jupiter or Uranus could deliver approximately 2.4-3.6 times more payload (in the case of Jupiter, the payload delivery is significantly larger than the Juno spacecraft). In this comparison, the higher end of the payload advantage is obtained when the trip time is held equal for the NTP-powered vehicle and a vehicle using a chemical propulsion departure stage. NTP systems are presently under development by multiple government agencies. NASA’s Space Nuclear Propulsion (SNP) project aims to demonstrate a hydrogen-fed NTP engine at 900 s specific impulse (I_sp) and approximately 10-15 klb_f of thrust. DARPA’s Demonstration Rocket for Agile Cislunar Operations (DRACO) program is targeting a demonstration of an NTP system in the cislunar space between the Earth and the Moon. An appropriately phased development plan that applies the development of the reactor technology for an NTP engine in this performance class and leverages mature, existing liquid rocket component hardware provides a path to a lower cost propulsion system that can be realized on a shorter development schedule. NEP, with high Isp in the 2,000-8,000 s range, can also provide advantages over chemical propulsion, including a much greater payload delivery mass and the flexibility for planners to trade between delivered mass and a wider window of mission trajectory options. Electric propulsion (EP) systems have demonstrated great utility, performing notably on the Dawn mission to enable rendezvous and orbital insertion at two separate bodies, Vesta and Ceres. An NEP-powered vehicle would have a similar capability to visit multiple bodies, loitering at each before moving to the next. A 10 kW_e NEP system provides a power- rich environment on the spacecraft that is simply not possible using present radioisotope power systems, giving mission planners more scientific instrument and communication hardware options. Several programs and projects are presently developing NEP systems and subsystems in the 10 kW_e power range, leveraging past reactor work and recent nuclear power generation risk-reduction demonstration activities such as the Demonstration Using Flattop Fission (DUFF) and the Kilopower Reactor Using Stirling TechnologY (KRUSTY). The goal of the Air Force Research Laboratory’s Joint Energy Technology Supplying On-Orbit Nuclear Power (JETSON) program is an in-space demonstration vehicle that has a 10 kW_e -class fission power source. These past and present efforts can be combined with the ongoing development of 10 kW_e -class electric propulsion systems (notably the NEXT-C ion thruster or the Hall-effect thrusters for Power and Propulsion Element of the Lunar Gateway) to provide a pathway to a low-cost, reliable NEP system for deep space science application.

Kurt A. Polzin↗

NASA NTRSDate not supplied

Space Nuclear Propulsion for Deep Space Science Missions

The use of nuclear thermal propulsion (NTP) 1 and nuclear electric propulsion (NEP) 2 systems on deep space science missions to the outer planets and into the interstellar medium 3 can yield significant spacecraft system and mission performance benefits and improvements relative to the use of conventional chemical propulsion systems. Several recent and ongoing programs are developing the technologies and systems required to realize a near-term deep space nuclear propulsion capability. NTP provides improved propulsion efficiencies compared to chemical propulsion, while also providing substantial thrust. This combination of high thrust and increased specific impulse (I_sp) provides high acceleration and extended thrusting periods, enabling greatly reduced trip-times on certain types of missions compared to various propulsive alternatives. For examples, compared to a baseline mission using chemical propulsion, NTP-powered missions to Jupiter or Uranus could deliver approximately 2.4-3.6 times more payload (in the case of Jupiter, the payload delivery is significantly larger than the Juno spacecraft). In this comparison, the higher end of the payload advantage is obtained when the trip time is held equal for the NTP-powered vehicle and a vehicle using a chemical propulsion departure stage. NTP systems are presently under development by multiple government agencies. NASA’s Space Nuclear Propulsion (SNP) project aims to demonstrate a hydrogen-fed NTP engine at 900 s specific impulse (I_sp) and approximately 10-15 klb_f of thrust. DARPA’s Demonstration Rocket for Agile Cislunar Operations (DRACO) program is targeting a demonstration of an NTP system in the cislunar space between the Earth and the Moon. An appropriately phased development plan that applies the development of the reactor technology for an NTP engine in this performance class and leverages mature, existing liquid rocket component hardware provides a path to a lower cost propulsion system that can be realized on a shorter development schedule. NEP, with high Isp in the 2,000-8,000 s range, can also provide advantages over chemical propulsion, including a much greater payload delivery mass and the flexibility for planners to trade between delivered mass and a wider window of mission trajectory options. Electric propulsion (EP) systems have demonstrated great utility, performing notably on the Dawn mission to enable rendezvous and orbital insertion at two separate bodies, Vesta and Ceres. An NEP-powered vehicle would have a similar capability to visit multiple bodies, loitering at each before moving to the next. A 10 kW_e NEP system provides a power- rich environment on the spacecraft that is simply not possible using present radioisotope power systems, giving mission planners more scientific instrument and communication hardware options. Several programs and projects are presently developing NEP systems and subsystems in the 10 kW_e power range, leveraging past reactor work and recent nuclear power generation risk-reduction demonstration activities such as the Demonstration Using Flattop Fission (DUFF) and the Kilopower Reactor Using Stirling TechnologY (KRUSTY). The goal of the Air Force Research Laboratory’s Joint Energy Technology Supplying On-Orbit Nuclear Power (JETSON) program is an in-space demonstration vehicle that has a 10 kW_e -class fission power source. These past and present efforts can be combined with the ongoing development of 10 kW_e -class electric propulsion systems (notably the NEXT-C ion thruster or the Hall-effect thrusters for Power and Propulsion Element of the Lunar Gateway) to provide a pathway to a low-cost, reliable NEP system for deep space science application.

Kurt A. Polzin↗

DOE OSTI2023

First Detection of 120 GeV Protons with Superconducting Nanowire Detectors

We report the test results for a 120 GeV proton beam incident on superconducting nanowire particle detectors of various wire sizes. NbN devices with the same sensitive area were fabricated with different wire widths and tested at a temperature of 3 K. The relative detection efficiency was extracted from bias current scans for each device. The results demonstrate efficient detection of high energy protons for wire widths less than 400 nm. These results are particularly relevant for novel applications at accelerator facilities, such as the Electron-Ion Collider, where cryogenic cooling is readily available.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Multi-messenger Light Curves from Gamma-Ray Bursts in the Internal Shock Model

Gamma-ray bursts (GRBs) are promising as sources of neutrinos and cosmic rays. In the internal shock scenario, blobs of plasma emitted from a central engine collide within a relativistic jet and form shocks, leading to particle acceleration and emission. Motivated by present experimental constraints and sensitivities, we improve the predictions of particle emission by investigating time-dependent effects from multiple shocks. We produce synthetic light curves with different variability timescales that stem from properties of the central engine. For individual GRBs, qualitative conclusions about model parameters, neutrino production efficiency, and delays in high-energy gamma-rays can be deduced from inspection of the gamma-ray light curves. GRBs with fast time variability without additional prominent pulse structure tend to be efficient neutrino emitters, whereas GRBs with fast variability modulated by a broad pulse structure can be inefficient neutrino emitters and produce delayed high-energy gamma-ray signals. Our results can be applied to quantitative tests of the GRB origin of ultra-high-energy cosmic rays, and have the potential to impact current and future multi-messenger searches.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Collaborating CPU and GPU for large-scale high-order CFD simulations with complex grids on the TianHe-1A supercomputer

Programming and optimizing complex, real-world CFD codes on current many-core accelerated HPC systems is very challenging, especially when collaborating CPUs and accelerators to fully tap the potential of heterogeneous systems. In this paper, with a tri-level hybrid and heterogeneous programming model using MPI + OpenMP + CUDA, we port and optimize our high-order multi-block structured CFD software HOSTA on the GPU-accelerated TianHe-1A supercomputer. HOSTA adopts two self-developed high-order compact definite difference schemes WCNS and HDCS that can simulate flows with complex geometries. We present a dual-level parallelization scheme for efficient multi-block computation on GPUs and perform particular kernel optimizations for high-order CFD schemes. The GPU-only approach achieves a speedup of about 1.3 when comparing one Tesla M2050 GPU with two Xeon X5670 CPUs. To achieve a greater speedup, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present a novel scheme to balance the loads between the store-poor GPU and the store-rich CPU. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per TianHe-1A node for HOSTA by 2.3×, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Further, to scale HOSTA on TianHe-1A, we propose a gather/scatter optimization to minimize PCI-e data transfer times for ghost and singularity data of 3D grid blocks, and overlap the collaborative computation and communication as far as possible using some advanced CUDA and MPI features. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 TianHe-1A nodes. With our method, we have successfully simulated an EET high-lift airfoil configuration containing 800M cells and China's large civil airplane configuration containing 150M cells. To our best knowledge, those are the largest-scale CPU–GPU collaborative simulations that solve realistic CFD problems with both complex configurations and high-order schemes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Understanding and design of interstitial oxygen conductors

Highly efficient oxygen-active materials that react with, absorb, and transport oxygen is essential for fuel cells, electrolyzers and related applications. While vacancy-mediated oxygen-ion conductors have long been the focus of research, they are limited by high migration barriers at intermediate temperatures (400–600 °C), which hinder their practical applications. In contrast, interstitial oxygen conductors exhibit significantly lower migration barriers enabling higher ionic conductivity at lower temperatures. This review systematically examines both well-established and recently identified families of interstitial oxygen-ion conductors, focusing on how their unique structural motifs such as corner-sharing polyhedral frameworks, isolated polyhedral, and cage-like architectures, facilitate low migration barriers through interstitial and/or interstitialcy diffusion mechanisms. A central discussion of this review focuses on the evolution of design strategies, from targeted donor doping, element screening, to physical-intuition descriptor material screening and machine learning approach, which leverage computational tools to explore vast chemical spaces in search for new interstitial conductors. The success of these strategies demonstrates that a significant, largely unexplored space remains for discovering high-performing interstitial oxygen conductors. Crucial features enabling high-performance interstitial oxygen diffusion include the availability of electrons for oxygen reduction and sufficient structural flexibility with accessible volume for interstitial accommodation and migration. This review concludes with a forward-looking perspective, proposing a knowledge-driven methodology that integrates current understanding with data-centric approaches to identify promising interstitial oxygen conductors outside traditional search paradigms. These approaches are expected to significantly accelerate the development of high-performance interstitial oxygen conductors for a variety of oxygen-active applications, ultimately paving the way for more efficient and sustainable energy technologies.

Interstitial oxygen conductors↗

DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗

DOE OSTI2012

CONSTRAINTS ON COSMIC RAYS, MAGNETIC FIELDS, AND DARK MATTER FROM GAMMA-RAY OBSERVATIONS OF THE COMA CLUSTER OF GALAXIES WITH VERITAS AND FERMI

Observations of radio halos and relics in galaxy clusters indicate efficient electron acceleration. Protons should likewise be accelerated and, on account of weak energy losses, can accumulate, suggesting that clusters may also be sources of very high energy (VHE; E > 100 GeV) gamma-ray emission. We report here on VHE gamma-ray observations of the Coma galaxy cluster with the VERITAS array of imaging Cerenkov telescopes, with complementing Fermi Large Area Telescope observations at GeV energies. No significant gamma-ray emission from the Coma Cluster was detected. Integral flux upper limits at the 99% confidence level were measured to be on the order of (2-5) Multiplication-Sign 10{sup -8} photons m {sup -2} s {sup -1} (VERITAS, >220 GeV) and {approx}2 Multiplication-Sign 10{sup -6} photons m {sup -2} s {sup -1} (Fermi, 1-3 GeV), respectively. We use the gamma-ray upper limits to constrain cosmic rays (CRs) and magnetic fields in Coma. Using an analytical approach, the CR-to-thermal pressure ratio is constrained to be <16% from VERITAS data and <1.7% from Fermi data (averaged within the virial radius). These upper limits are starting to constrain the CR physics in self-consistent cosmological cluster simulations and cap the maximum CR acceleration efficiency at structure formation shocks to be <50%. Alternatively, this may argue for non-negligible CR transport processes such as CR streaming and diffusion into the outer cluster regions. Assuming that the radio-emitting electrons of the Coma halo result from hadronic CR interactions, the observations imply a lower limit on the central magnetic field in Coma of {approx}(2-5.5) {mu}G, depending on the radial magnetic field profile and on the gamma-ray spectral index. Since these values are below those inferred by Faraday rotation measurements in Coma (for most of the parameter space), this renders the hadronic model a very plausible explanation of the Coma radio halo. Finally, since galaxy clusters are dark matter (DM) dominated, the VERITAS upper limits have been used to place constraints on the thermally averaged product of the total self-annihilation cross section and the relative velocity of the DM particles, ({sigma}v).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2012

Constraints on Cosmic Rays, Magnetic Fields, and Dark Matter from Gamma-ray Observations of the Coma Cluster of Galaxies with VERITAS and FERMI

Observations of radio halos and relics in galaxy clusters indicate efficient electron acceleration. Protons should likewise be accelerated and, on account of weak energy losses, can accumulate, suggesting that clusters may also be sources of very high energy (VHE; E greater than100 GeV) gamma-ray emission. We report here on VHE gamma-ray observations of the Coma galaxy cluster with the VERITAS array of imaging Cerenkov telescopes, with complementing Fermi Large Area Telescope observations at GeV energies. No significant gamma-ray emission from the Coma Cluster was detected. Integral flux upper limits at the 99 confidence level were measured to be on the order of (2-5) x 10(sup -8) photons m(sup -2) s(sup -1) (VERITAS,greater than 220 GeV) and approximately 2 x 10(sup -6) photons m(sup -2) s(sup -1) (Fermi, 1-3 GeV), respectively. We use the gamma-ray upper limits to constrain cosmic rays (CRs) and magnetic fields in Coma. Using an analytical approach, the CR-to-thermal pressure ratio is constrained to be less than 16% from VERITAS data and less than 1.7% from Fermi data (averaged within the virial radius). These upper limits are starting to constrain the CR physics in self-consistent cosmological cluster simulations and cap the maximum CR acceleration efficiency at structure formation shocks to be 50. Alternatively, this may argue for non-negligible CR transport processes such as CR streaming and diffusion into the outer cluster regions. Assuming that the radio-emitting electrons of the Coma halo result from hadronic CR interactions, the observations imply a lower limit on the central magnetic field in Coma of approximately (2-5.5)microG, depending on the radial magnetic field profile and on the gamma-ray spectral index. Since these values are below those inferred by Faraday rotation measurements in Coma (for most of the parameter space), this renders the hadronic model a very plausible explanation of the Coma radio halo. Finally, since galaxy clusters are dark matter (DM) dominated, the VERITAS upper limits have been used to place constraints on the thermally averaged product of the total self-annihilation cross section and the relative velocity of the DM particles, (sigma upsilon)

cosmic↗