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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Search for charmed baryons in the Λ c + η system and measurement of the branching fractions of Λ c ( 2880 ) + and Λ c ( 2940 ) + decaying to Λ c + η and p D 0 relative to Σ c ( 2455 ) π

We search for excited charmed baryons in the Λ c + η system using a data sample corresponding to an integrated luminosity of 980 fb − 1 . The data were collected by the Belle detector at the KEKB e + e − asymmetric-energy collider. No significant signals are found in the Λ c + η mass spectrum, including the known Λ c ( 2880 ) + and Λ c ( 2940 ) + . Clear Λ c ( 2880 ) + and Λ c ( 2940 ) + signals are observed in the p D 0 mass spectrum. We set upper limits at 90% credibility level on ratios of branching fractions of Λ c ( 2880 ) + and Λ c ( 2940 ) + decaying to Λ c + η relative to Σ c ( 2455 ) π of < 0.13 for the Λ c ( 2880 ) + and < 1.11 for the Λ c ( 2940 ) + . We measure ratios of branching fractions of Λ c ( 2880 ) + and Λ c ( 2940 ) + decaying to p D 0 relative to Σ c ( 2455 ) π of 0.75 ± 0.03 ( stat ) ± 0.07 ( syst ) for the Λ c ( 2880 ) + and 3.59 ± 0.21 ( stat ) ± 0.56 ( syst ) for the Λ c ( 2940 ) + . Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Angular analysis of B → K * e + e − in the low- q 2 region with new electron identification at Belle

We perform an angular analysis of the B → K * e + e − decay for the dielectron mass squared, q 2 , range of 0.0008 – 1.1200 GeV 2 / c 4 using the full Belle dataset in the K * 0 → K + π − and K * + → K S 0 π + channels, incorporating new methods of electron identification to improve the statistical power of the dataset. This analysis is sensitive to contributions from right-handed currents from physics beyond the Standard Model by constraining the Wilson coefficients C 7 ( ′ ) . We perform a fit to the B → K * e + e − differential decay rate and measure the imaginary component of the transversality amplitude to be A T Im = − 1.27 ± 0.52 ± 0.12 , and the K * transverse asymmetry to be A T ( 2 ) = 0.52 ± 0.53 ± 0.11 , with F L and A T Re fixed to the Standard Model values. The resulting constraints on the value of C 7 ′ are consistent with the Standard Model within a 2 σ confidence interval. Published by the American Physical Society 2024

Ferlewicz, D. (ORCID:0000000243741234)↗
DOE OSTI2024

Search for two-body B meson decays to Λ 0 and Ω c ( * ) 0

We report the results of the first search for Standard Model and baryon-number-violating two-body decays of the neutral B mesons to Λ 0 and Ω c ( * ) 0 using 711 fb − 1 of data collected at the ϒ ( 4 S ) resonance with the Belle detector at the KEKB asymmetric-energy e + e − collider. We observe no evidence of signal from any such decays and set 95% confidence-level upper limits on the products of B 0 and B ¯ 0 branching fractions for these two-body decays with B ( Ω c 0 → π + Ω − ) in the range between 9.5 × 10 − 8 and 31.2 × 10 − 8 . Published by the American Physical Society 2024

Savinov, V. (ORCID:0000000291842830)↗
DOE OSTI2024

Measurement of Angular Coefficients of B ¯ → D * ℓ ν ¯ ℓ : Implications for | V c b | and Tests of Lepton Flavor Universality

We measure the complete set of angular coefficients J i for exclusive B ¯ → D * ℓ ν ¯ ℓ decays ( ℓ = e , μ ). Our analysis uses the full 711 fb − 1 Belle dataset with hadronic tag-side reconstruction. The results allow us to extract the form factors describing the B ¯ → D * transition and the Cabibbo-Kobayashi-Maskawa matrix element | V cb | . Using recent lattice QCD calculations for the hadronic form factors, we find | V cb | = ( 40.7 ± 0.7 ) × 10 − 3 using the Boyd-Grinstein-Lebed parametrization, compatible with determinations from inclusive semileptonic decays. We search for lepton flavor universality violation as a function of the hadronic recoil parameter w and investigate the differences of the electron and muon angular distributions. We find no deviation from standard model expectations. Published by the American Physical Society 2024

Physics↗
DOE OSTI2024

Evidence of h b ( 2 P ) → ϒ ( 1 S ) η Decay and Search for h b ( 1 P , 2 P ) → ϒ ( 1 S ) π 0 with the Belle Detector

We report the first evidence for the h b ( 2 P ) → ϒ ( 1 S ) η transition with a significance of 3.5 standard deviations. The decay branching fraction is measured to be B [ h b ( 2 P ) → ϒ ( 1 S ) η ] = ( 7.1 − 3.2 + 3.7 ± 0.8 ) × 10 − 3 , which is noticeably smaller than expected. We also set upper limits on π 0 transitions of B [ h b ( 2 P ) → ϒ ( 1 S ) π 0 ] < 1.8 × 10 − 3 , and B [ h b ( 1 P ) → ϒ ( 1 S ) π 0 ] < 1.8 × 10 − 3 , at the 90% confidence level. These results are obtained with a 131.4 fb − 1 data sample collected near the ϒ ( 5 S ) resonance with the Belle detector at the KEKB asymmetric-energy e + e − collider. Published by the American Physical Society 2024

Kovalenko, E. (ORCID:0000000180841931)↗
DOE OSTI2024

Observation of the Electromagnetic Field Effect via Charge-Dependent Directed Flow in Heavy-Ion Collisions at the Relativistic Heavy Ion Collider

The deconfined quark-gluon plasma (QGP) created in relativistic heavy-ion collisions enables the exploration of the fundamental properties of matter under extreme conditions. Noncentral collisions can produce strong magnetic fields on the order of 10 18 G , which offers a probe into the electrical conductivity of the QGP. In particular, quarks and antiquarks carry opposite charges and receive contrary electromagnetic forces that alter their momenta. This phenomenon can be manifested in the collective motion of final-state particles, specifically in the rapidity-odd directed flow, denoted as v 1 ( y ) . Here, we present the charge-dependent measurements of d v 1 / d y near midrapidities for π ± , K ± , and p ( p ¯ ) in Au + Au and isobar ( Ru 44 96 + Ru 44 96 and Zr 40 96 + Zr 40 96 ) collisions at s NN = 200 GeV , and in Au + Au collisions at 27 GeV, recorded by the STAR detector at the Relativistic Heavy Ion Collider. The combined dependence of the v 1 signal on collision system, particle species, and collision centrality can be qualitatively and semiquantitatively understood as several effects on constituent quarks. While the results in central events can be explained by the u and d quarks transported from initial-state nuclei, those in peripheral events reveal the impacts of the electromagnetic field on the QGP. Our data put valuable constraints on the electrical conductivity of the QGP in theoretical calculations. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification

The peculiar nature of whole slide imaging (WSI), digitizing conventional glass slides to obtain multiple high resolution images which capture microscopic details of a patient’s histopathological features, has garnered increased interest from the computer vision research community over the last two decades. Given the unique computational space and time complexity inherent to gigapixel-size whole slide image data, researchers have proposed novel machine learning algorithms to aid in the performance of diagnostic tasks in clinical pathology. One effective algorithm represents a Whole slide image as a bag of smaller image patches, which can be represented as low-dimension image patch embeddings. Weakly supervised deep-learning methods, such as cluster-constrained-attention multiple instance learning (CLAM), have shown promising results when combined with image patch embeddings. While traditional ensemble classifiers yield improved task performance, such methods come with a steep cost in model complexity. Through knowledge distillation, it is possible to retain some performance improvements from an ensemble, while minimizing costs to model complexity. In this work, we implement a weakly supervised ensemble using clustering-constrained-attention multiple-instance learners (CLAM), which uses attention and instance-level clustering to identify task salient regions and feature extraction in whole slides. By applying logit-based and attention-based knowledge distillation, we show it is possible to retain some performance improvements resulting from the ensemble at zero cost to model complexity.

Alamudun, Folami↗
DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗
DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗
DOE OSTI2025

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing

Energy efficiency of training and inferencing with large neural network models is a critical challenge facing the future of sustainable large-scale machine learning workloads. This paper introduces an alternative strategy, called phantom parallelism, to minimize the net energy consumption of traditional tensor (model) parallelism, the most energy-inefficient component of large neural network training. The approach is presented in the context of feed-forward network architectures as a preliminary, but comprehensive, proof-of-principle study of the proposed methodology. We derive new forward and backward propagation operators for phantom parallelism, implement them as custom autograd operations within an end-to-end phantom parallel training pipeline and compare its parallel performance and energy-efficiency against those of conventional tensor parallel training pipelines. Formal analyses that predict lower bandwidth and FLOP counts are presented with supporting empirical results on up to 256 GPUs that corroborate these gains. Experiments are shown to deliver ∼50% reduction in the energy consumed to train FFNs using the proposed phantom parallel approach when compared with conventional tensor parallel methods. Additionally, the proposed approach is shown to train smaller phantom models to the same model loss on smaller GPU counts as larger tensor parallel models on larger GPU counts offering the possibility for even greater energy savings.

Seal, Sudip [ORNL] (ORCID:0000000332330656)↗