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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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ORR Strain Sensor as Potential Detector for HEP
Slides describing the student's work during his internship as well as broader context for how it fits within the broader scientific program of developing optical ring resonator strain sensors for particle detection.
Developing and Distributing HEP Software Stacks with Spack
The Computational Science and AI Directorate at Fermilab is using Spack to support the development efforts of a large number of scientific programmers, in many independent projects and experiments. While independent, these projects share many dependencies. They are typically under continuous and fairly rapid development. They have to support deployment on diverse hardware. This is a different context than is typical for the management of HPC software, where Spack was born. To support our community, we have created a model that enables users to develop code with greater efficiency than is possible with Spack’s current development facilities. In this talk we will present: - a brief introduction to the science we support (particle physics) - how the code we work with is naturally organized into several layers of packages - how we are using Spack to manage those layers - how we leverage the layering to provide efficient support for developers, using our Spack extension “MPD”. - some suggestions for changes or additions to Spack to make such work easier.
The Bias-Variance-Correlation Tradeoff and Its Implications for ML Applications in HEP
The bias-variance tradeoff is a well-recognized phenomenon in statistics and machine learning. In this talk, I will discuss an extension, dubbed the bias-variance-correlation tradeoff. Roughly speaking, as the flexibility of a model decreases, the correlations in the outputs of a trained model for different inputs increases. Such correlations have implications for several applications of machine learning in high energy physics, e.g., the use generative models for event generation. In particular, I will argue that claims in the literature of data amplification by generative models stem from ignoring important correlations between the model's outputs for different inputs.
Optimising HEP Simulations on 3D Architecture: Primitive Gates for BT
In this report, the first attempt for an alternative approach for the simulation of the non-abelian group BT has been done. First, we analysed why SNAP and Displacement decomposition is not feasible for this problem with the current technology, and then we explored a new approach for the optimisation of the pulses. In particular, we studied how the expansion of the pulses in Chebyshev Polynomials could affect the optimisation process. Even if the study is not conclusive, we believe that it might be worth continuing to study this approach in the future, in order to be able to efficiently implement quantum circuits on computers.
Optimising HEP Simulations on 3D Architecture: PrimitiveGates for BT
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An Nginx-based Content Distribution Network for HEP
With the move to HTTP/WebDAV and JSON Web Tokens as a standard protocol for transfers within the WLCG distributed storage network, a large amount of off-the-shelf technologies become viable for meeting the requirements of a Storage Element (SE). In this work, we explore the capabilities and performance of the OpenResty framework, which extends the nginx server with the LuaJIT scripting language, to recreate the feature set of a SE. We demonstrate token-authenticated HTTP read, write, and WebDAV third-party copy features, as well as a storage federation with HTTP redirect, proxy, and caching capabilities. We further explore the performance scaling in terms of throughput and requests per second.
Deep Learning Methods for Symbolic Calculations in HEP
This project develops machine learning methods to accelerate symbolic calculations in high-energy physics. Using sequence-to-sequence transformer models, we construct frameworks to predict squared amplitudes and related quantities for Standard Model processes, including quantum electrodynamics, quantum chromodynamics, and electroweak interactions. The results demonstrate that deep learning can successfully learn complex symbolic relationships and provide a scalable approach to symbolic computation with potential applications in precision calculations and collider phenomenology.
ORNL HEP Self-Appraisal for FY2026
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Novel Ultrafast Lu2O3:Yb Ceramics for Future HEP Applications
Inorganic scintillators activated by charge transfer luminescence Yb3+ are considered promising ultrafast material to break the ps timing barrier for future high energy physics applications. Inorganic scintillators in ceramic form are potentially more cost-effective than crystals because of their lower fabrication temperature and no need for aftergrowth mechanical processing. This paper reports an investigation on Lu2O3:Yb and Lu2xY2(1−x)O3:Yb scintillating ceramic samples fabricated by Radiation Monitoring Devices Inc. All samples show X-ray excited luminescence peaked at 370 nm. Ultrafast decay time of 1.1 ns was observed by using a microchannel plate-photomultiplier tube-based test bench at Caltech. Considering its intrinsic high density (9.4 g/cm3), Lu2O3:Yb ceramics are promising for future time of fight application for high energy physics experiments.
Database computing in HEP
The major SSC experiments are expected to produce up to 1 Petabyte of data per year each. Once the primary reconstruction is completed by farms of inexpensive processors, I/O becomes a major factor in further analysis of the data. We believe that the application of database techniques can significantly reduce the I/O performed in these analyses. We present examples of such I/O reductions in prototypes based on relational and object-oriented databases of CDF data samples.
High Energy Power and Propulsion (HEP and P) Capability Roadmap
Contents include the following: Introduction. Capability Roadmaps. Solar Systems. st) Energy Storage Systems. Radioisotope Systems. Nuclear Fission Systems. Conclusion/Summary.
Learning from the Pandemic: the Future of Meetings in HEP and Beyond
The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. While the increasing deployment of effective vaccines around the world is a very positive development, the timeline and pathway to "normality" is uncertain and the "new normal" we will settle into is anyone's guess. Particle physics, like many other scientific fields, has more than a year of experience in holding virtual meetings, workshops, and conferences. A great deal of experimentation and innovation to explore how to execute these meetings effectively has occurred. Therefore, it is an appropriate time to take stock of what we as a community learned from running virtual meetings and discuss possible strategies for the future. Continuing to develop effective strategies for meetings with a virtual component is likely to be important for reducing the carbon footprint of our research activities, while also enabling greater diversity and inclusion for participation. This report summarizes a virtual two-day workshop on Virtual Meetings held May 5-6, 2021 which brought together experts from both inside and outside of high-energy physics to share their experiences and practices with organizing and executing virtual workshops, and to develop possible strategies for future meetings as we begin to emerge from the COVID-19 pandemic. This report outlines some of the practices and tools that have worked well which we hope will serve as a valuable resource for future virtual meeting organizers in all scientific fields.
Simulations for the Development of Quantum Computational Devices for HEP
In this white paper, we describe characteristics of tools for classical 3 simulations of quantum computational devices appropriate for High Energy Physics applications.
Software and Computing for Small HEP Experiments
This white paper briefly summarized key conclusions of the recent US Community Study on the Future of Particle Physics (Snowmass 2021) workshop on Software and Computing for Small High Energy Physics Experiments.
Quantum Networks for High Energy Physics (HEP)
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R&D Program for HEP High-Power Targets at Fermilab
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A Ceph S3 Object Data Store for HEP
We present a novel data format design that obviates the need for data tiers by storing individual event data products in column objects. The objects are stored and retrieved through Ceph S3 technology, with a layout designed to minimize metadata volume and maximize data processing parallelism. Performance benchmarks of data storage and retrieval are presented.