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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Reformulation of the No-Free-Lunch Theorem for Entangled Datasets

The No-Free-Lunch (NFL) theorem is a celebrated result in learning theory that limits one’s ability to learn a function with a training data set. With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer’s ability to learn a unitary process with quantum training data. However, in the quantum setting, the training data can possess entanglement, a strong correlation with no classical analog. In this work, we show that entangled data sets lead to an apparent violation of the (classical) NFL theorem. This motivates a reformulation that accounts for the degree of entanglement in the training set. As our main result, we prove a quantum NFL theorem whereby the fundamental limit on the learnability of a unitary is reduced by entanglement. We employ Rigetti's quantum computer to test both the classical and quantum NFL theorems. In conclusion, our work establishes that entanglement is a commodity in quantum machine learning.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Search for a Sub-eV Sterile Neutrino Using Daya Bay’s Full Dataset

This Letter presents results of a search for the mixing of a sub-eV sterile neutrino with three active neutrinos based on the full data sample of the Daya Bay Reactor Neutrino Experiment, collected during 3158 days of detector operation, which contains 5.55 × 106 reactor $\overline{v}$ e candidates identified as inverse beta-decay interactions followed by neutron capture on gadolinium. The analysis benefits from a doubling of the statistics of our previous result and from improvements of several important systematic uncertainties. No significant oscillation due to mixing of a sub-eV sterile neutrino with active neutrinos was found. Exclusion limits are set by both Feldman-Cousins and CLs methods. Light sterile neutrino mixing with sin 2⁡ 2⁢θ 14 ≳ 0.01 can be excluded at 95% confidence level in the region of 0.01 eV 2 ≲ |Δ⁢$m$$^{2}_{41}$| ≲ 0.1 eV 2 . This result represents the world-leading constraints in the region of 2 × 10 –4 eV 2 ≲ |Δ⁢$m$$^{2}_{41}$| ≲ 0.2 eV 2 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
DOE OSTI2025

Constraints on lepton number violation with the 2 tonne · year CUORE dataset

Matter-antimatter asymmetry underlines the incompleteness of the current understanding of particle physics. Neutrinoless double-beta decay (0νββ) may help explain this asymmetry while unveiling the Majorana nature of the neutrino. The CUORE (Cryogenic Underground Observatory for Rare Events) experiment searches for 0νββ of 130 Te using a tonne-scale cryogenic calorimeter operated at milli-kelvin temperatures. We report no evidence of 0νββ and place a lower limit on the half-life of T 1/2 > 3.5 × 10 25 years (90% credibility interval) with over 2 tonne·years of TeO 2 exposure. Finally, the tools and techniques developed for this result and the 5-year stable operation of nearly 1000 detectors demonstrate crucial infrastructure for future-generation experiments capable of searching for 0νββ across multiple isotopes.

Adams, D. Q. [Department of Physics and Astronomy,↗
DOE OSTI2022

Characterization of the correlated background for a sterile neutrino search using the first dataset of the JSNS$^2$ experiment

JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of ν¯ μ → ν¯ e appearance oscillations using muon decay-at-rest neutrinos. Before dedicated data taking in the first-half of 2021, we performed a commissioning run for 10 days in June 2020. Using the data obtained in this commissioning run, in this paper, we present an estimate of the correlated background which imitates the ν¯ e signal in a sterile neutrino search. In addition, in order to demonstrate future prospects of the JSNS 2 experiment, possible pulse shape discrimination improvements towards reducing cosmic ray induced fast neutron background are described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A search for $$\upmu ^+ \rightarrow \textrm{e}^+ \upgamma $$ with the first dataset of the MEG II experiment

Abstract The MEG II experiment, based at the Paul Scherrer Institut in Switzerland, reports the result of a search for the decay$$\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma $$ μ + → e + γ from data taken in the first physics run in 2021. No excess of events over the expected background is observed, yielding an upper limit on the branching ratio of$${\mathcal {B}} (\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma ) < 7.5 \times 10^{-13}$$ B ( μ + → e + γ ) < 7.5 × 10 - 13 (90% CL). The combination of this result and the limit obtained by MEG gives$${\mathcal {B}} (\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma ) < 3.1 \times 10^{-13}$$ B ( μ + → e + γ ) < 3.1 × 10 - 13 (90% CL), which is the most stringent limit to date. A ten-fold larger sample of data is being collected during the years 2022–2023, and data-taking will continue in the coming years.

Physics↗
DOE OSTI2022

Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models

As Additive Layer Manufacturing (ALM) becomes pervasive in industry, its applications in safety critical component manufacturing are being explored and adopted. However, ALM's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of ALM devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is very challenging. The ideal algorithm for this context is simple, computationally efficient, minimizes false positives, and is accurate enough to resolve small deviations. In this paper, we present a probabilistic-model-based approach to address this challenge. To test our approach, we analyze current measurements from a polymer composite 3D printer during emulated tampering attacks. Our results show that our approach can consistently and efficiently locate small changes in the presence of substantial operational noise.

Yoginath, Srikanth↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2025

A Survey on Error-Bounded Lossy Compression for Scientific Datasets

Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular pros and cons. In this article, we provide a comprehensive survey of emerging error-bounded lossy compression techniques. The key contribution is fourfold. (1) We summarize a novel taxonomy of lossy compression into six classic models. (2) We provide a comprehensive survey of 10 commonly used compression components/modules. (3) We summarized pros and cons of 47 state-of-the-art lossy compressors and present how state-of-the-art compressors are designed based on different compression techniques. (4) We discuss how customized compressors are designed for specific scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data.

Error-Bounded Lossy Compression↗