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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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53 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine learning and LHC event generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

MadNIS - Neural multi-channel importance sampling

Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Generative unfolding with distribution mapping

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping (DM) to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z+2 -jets.

Butter, Anja↗
DOE OSTI2026

Iterative HOMER with uncertainties

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

Butter, Anja [Heidelberg Univ. (Germany); Sorbonne↗
DOE OSTI2022

TF07 Snowmass Report: Theory of Collider Phenomena

Theoretical research has long played an essential role in interpreting data from high-energy particle colliders and motivating new accelerators to advance the energy and precision frontiers. Collider phenomenology is an essential interface between theoretical models and experimental observations, since theoretical studies inspire experimental analyses while experimental results sharpen theoretical ideas. This report -- from the Snowmass 2021 Theory Frontier topical group for Collider Phenomenology (TF07) -- showcases the dynamism, engagement, and motivations of collider phenomenologists by exposing selected exciting new directions and establishing key connections between cutting-edge theoretical advances and current and future experimental opportunities. By investing in collider phenomenology, the high-energy physics community can help ensure that theoretical advances are translated into concrete tools that enable and enhance current and future experiments, and in turn, experimental results feed into a more complete theoretical understanding and motivate new questions and explorations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1979

A continuous silicon-coating facility

Automatic continuous silicon-coating facility is used to process 100 by 10 cm graphite-coated ceramic substrates for silicon solar cells. Process reduces contamination associated with conventional dip-coating processes, improving material service life.

Butter, C.↗
NASA NTRS1980

Continuous coating of silicon-on-ceramic

Growth of sheet silicon on low-cost substrates has been demonstrated by the silicon coating with inverted meniscus (SCIM) technique. A mullite-based ceramic substrate is coated with carbon and then passed over a trough of molten silicon with a raised meniscus. Solidification occurs at the trailing edge of the downstream meniscus, producing a silicon-on-ceramic (SOC) layer. Meniscus shape and stability are controlled by varying the level of molten silicon in a reservoir connected to the trough. The thermal conditions for growth and the crystallographic texture of the SOC layers are similar to those produced by dip-coating, the original technique of meniscus-controlled growth. The thermal conditions for growth have been analyzed in some detail. The analysis correctly predicts the velocity-thickness relationship and the liquid-solid interface shape for dip-coating, and appears to be equally applicable to SCIM-coating. Solar cells made from dip-coated SOC material have demonstrated efficiencies of 10% on 4-sq cm cells and 9.9% on 10-sq cm cells.

Heaps, J. D.↗
NASA NTRS1947

Application of the Analogy Between Water Flow with a Free Surface and Two-Dimensional Compressible Gas Flow

The theory of the hydraulic analogy -- that is, the analogy between water flow with a free surface and two-dimensional compressible gas flow -- and the limitations and conditions of the analogy are discussed. A test was run using the hydraulic analogy as applied to the flow about circular cylinders of various diameters at subsonic velocities extending into the supercritical range. The apparatus and techniques used in this application are described and criticized. Reasonably satisfactory agreement of pressure distributions and flow fields existed between water and air flow about corresponding bodies. This agreement indicated the possibility of extending experimental compressibility research by new methods.

Orlin, W James↗
NASA NTRS1998

Mouse osteoblastic cell line (MC3T3-E1) expresses extracellular calcium (Ca2+o)-sensing receptor and its agonists stimulate chemotaxis and proliferation of MC3T3-E1 cells

The calcium-sensing receptor (CaR) is a G protein-coupled receptor that plays key roles in extracellular calcium ion (Ca2+o) homeostasis in parathyroid gland and kidney. Osteoblasts appear at sites of osteoclastic bone resorption during bone remodeling in the "reversal" phase following osteoclastic resorption and preceding bone formation. Bone resorption produces substantial local increases in Ca2+o that could provide a signal for osteoblasts in the vicinity, leading us to determine whether such osteoblasts express the CaR. In this study, we used the mouse osteoblastic, clonal cell line MC3T3-E1. Both immunocytochemistry and Western blot analysis, using an antiserum specific for the CaR, detected CaR protein in MC3T3-E1 cells. We also identified CaR transcripts in MC3T3-E1 cells by Northern analysis using a CaR-specific riboprobe and by reverse transcription-polymerase chain reaction with CaR-specific primers, followed by nucleotide sequencing of the amplified products. Exposure of MC3T3-E1 cells to high Ca2+o (up to 4.8 mM) or the polycationic CaR agonists, neomycin and gadolinium (Gd3+), stimulated both chemotaxis and DNA synthesis in MC3T3-E1 cells. Therefore, taken together, our data strongly suggest that the osteoblastic cell line MC3T3-E1 possesses both CaR protein and mRNA very similar, if not identical, to those in parathyroid and kidney. Furthermore, the CaR in these osteoblasts could play a key role in regulating bone turnover by stimulating the proliferation and migration of such cells to sites of bone resorption as a result of local release of Ca2+o.

Non-NASA Center↗
NASA NTRS1998

Expression of extracellular calcium (Ca2+o)-sensing receptor in human peripheral blood monocytes

The calcium-sensing receptor (CaR) is a G protein-coupled receptor playing key roles in extracellular calcium ion (Ca2+o) homeostasis in parathyroid gland and kidney. Macrophage-like mononuclear cells appear at sites of osteoclastic bone resorption during bone turnover and may play a role in the "reversal" phase of skeletal remodeling that follows osteoclastic resorption and precedes osteoblastic bone formation. Bone resorption produces substantial local increases in Ca2+o that could provide a signal for such mononuclear cells present locally within the bone marrow microenvironment. Indeed, previous studies by other investigators have shown that raising Ca2+o either in vivo or in vitro stimulated the release of interleukin-6 (IL-6) from human peripheral blood monocytes, suggesting that these cells express a Ca2+o-sensing mechanism. In these earlier studies, however, the use of reverse transcription-polymerase chain reaction (RT-PCR) failed to detect transcripts for the CaR previously cloned from parathyroid and kidney in peripheral blood monocytes. Since we recently found that non-specific esterase-positive, putative monocytes isolated from murine bone marrow express the CaR, we reevaluated the expression of this receptor in human peripheral blood monocytes. Immunocytochemistry, flow cytometry, and Western blot analysis, performed using a polyclonal antiserum specific for the CaR, detected CaR protein in human monocytes. In addition, the use of RT-PCR with CaR-specific primers, followed by nucleotide sequencing of the amplified products, identified CaR transcripts in the cells. Therefore, taken together, our data show that human peripheral blood monocytes possess both CaR protein and mRNA very similar if not identical to those expressed in parathyroid and kidney that could mediate the previously described, direct effects of Ca2+o on these cells. Furthermore, since mononuclear cells isolated from bone marrow also express the CaR, the latter might play some role in the "reversal" phase of bone remodeling, sensing local changes in Ca2+o resulting from osteoclastic bone resorption and secreting osteotropic cytokines or performing other Ca2+o-regulated functions that contribute to the control of bone turnover.

NASA Discipline Musculoskeletal↗
NASA NTRS2003

Long baseline interferometric observations of HD 195019: no K dwarf companion detected

Radial velocity measurements of the G3V/IV star HD 195019 revealed the presence of an orbiting companion with m sin(i) = 3.5 Jupiter masses and a period of 18 days. Here we present new visability measurements obtained at the Palomar Testbed Interferometer which rule out any companion in an orbit consistent with the spectroscopic data and having more than 1% of the flux of the primary star in the near-infrared K band.

extrasolar planets interferometry radial velocity↗
DOE OSTI2022

Green AI: Insights Into Deep Learning's Looming Energy Efficiency Crisis

As demands grow to integrate artificial intelligence into every aspect of industry, commerce, and life, deep learning's exploding energy cost has become a looming crisis, making AI systems a salient energy-efficiency challenge. One might expect that doubling a neural network's size would halve its error rate, or at least allow it to achieve greater performance given the same amount of time and energy. I will present clear and substantial scientific evidence which indicates that not only is this intuition wildly wrong, but that neural networks scale so poorly that to increase deep learning performance by only a small fraction can easily require an order of magnitude or more increase in computational resources and energy. Further, the marginal trade-off price of to increase model performance rapidly explodes as performance targets are increased. To address this challenge, I will provide a toolkit of techniques that can be applied today to mitigate the inefficiency of modern deep learning. And, I will conclude by illuminating a practical path forward towards efficient, Green AI.

artificial intelligence↗