NETL's SmartSearch Deep Learning Tool, Scalable Data Search and Parse for Carbon Storage Data Discovery
NETL's SmartSearch Deep Learning Tool, Scalable Data Search and Parse for Carbon Storage Data Discovery
Baker, David Victor↗
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FAIR Interfaces for Geospatial Scientific Data Searches
Several factors must be considered in designing a highly accurate, reliable, scalable, and user-friendly geospatial data search interfaces. This paper examines four critical questions that ought to be considered during design phase: (1) Is the search interface or API that provides the search capability useable by both humans and machines? (2) Are the results consistent and reliable? (3) Is the output response format free to use, community-defined, and non-propriety? (4) Does the API clearly state the usage clauses? This paper discusses how certain data repositories at the US Department of Energy's Oak Ridge National Laboratory apply FAIR data principles to enable geospatial searches and address the above-mentioned questions.
Devarakonda, Ranjeet↗
Investigating the Future of Scientific Data Search [Slides]
Searching for usable, actionable, data in a trustworthy manner is a challenge across scientific communities. Artificial Intelligence (AI) and Machine Learning (ML) techniques may be leveraged to increase the utility of scientific data by: Demystify unstructured data to aid curation & sharing Surfacing hard to find datasets. User Experience (UX) Research can help uncover scientists needs & challenges finding data and using AI/ML enabled tools.
97 MATHEMATICS AND COMPUTING↗
Evaluation of a scientific data search infrastructure
The ability to search over large scientific datasets has become crucial to next-generation scientific discoveries as data generated from scientific facilities grow dramatically. In previous work, we developed and deployed ScienceSearch, a search infrastructure for scientific data which uses machine learning to automate metadata creation. Our current deployment is deployed atop a container based platform at a HPC center. In this article, we present an evaluation and discuss our experiences with the ScienceSearch infrastructure. Specifically, we present a performance evaluation of ScienceSearch's infrastructure focusing on scalability trends. The obtained results show that ScienceSearch is able to serve up to 130 queries/min with latency under 3 s. We discuss our infrastructure setup and evaluation results to provide our experiences and a perspective on opportunities and challenges of our search infrastructure.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
AD – Elog Data Search Using Natural Language Processing Techniques
The goal of this project is to develop and evaluate a Machine Learning model using natural language processing techniques to categorize AD E-Log entries efficiently. AD E-Logs, which play a vital role in Fermilab's projects as a system to record and manage various data, often suffer from slow search processing and imprecise results. To address this issue, we preprocessed the data and employed the Doc2Vec model for training. The resulting model enables us to identify and retrieve the most relevant entries, thus enhancing user experience in accessing desired information.
Muse, Amiin↗
Threat Sources for Creating Synthetic Urban Search Data
Equivalent point source energy emission distributions were computed for various threat sources for use in simulating the detector responses for urban search scenarios. The sources include standard isotopic sources used in detector testing, medical and industrial sources occasionally encountered in urban searches, and several types of special nuclear materials. Most of the equivalent point source distributions represent small sources inside some amount of shielding, but the special nuclear material sources represent volumetrically distributed sources in spheres of metal. Text-based inputs for emission distributions are available for the Monte Carlo transport codes Monte Carlo N-Particle, SCALE/MAVRIC, and Omnibus/Shift, any of which can easily be converted to other formats. These sources were developed for use in the Radiological Anomaly Detection and Identification (RADAI) project and the follow-on project, the RADAI-Extended project, sponsored by the National Nuclear Security Administration Office of Defense Nuclear Nonproliferation Research and Development.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Fast and flexible analysis of direct dark matter search data with machine learning
We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs such as light and charge signal strengths. Furthermore, this technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Searches for exclusive Higgs and Z boson decays into a vector quarkonium state and a photon using 139 fb$^{-1}$ of ATLAS $\sqrt{s}=13$ TeV proton–proton collision data
Searches for the exclusive decays of Higgs and Z bosons into a vector quarkonium state and a photon are performed in the μ + μ - γ final state with a proton– proton collision data sample corresponding to an integrated luminosity of 139 fb -1 collected at $\sqrt{s}=13$ TeV with the ATLAS detector at the CERN Large Hadron Collider. The observed data are compatible with the expected back grounds. The 95% confidence-level upper limits on the branching fractions of the Higgs boson decays into J/ψ γ , ψ(2S) γ , and $Υ$(1S, 2S, 3S) γ are found to be 2.0 × 10 -4 , 10.5×10 -4 , and (2.5, 4.2, 3.4)×10 -4 , respectively, assuming Standard Model production of the Higgs boson. The corresponding 95% CL upper limits on the branching fractions of the Z boson decays are 1.2 × 10 -6 , 2.4 × 10 -6 , and (1.1, 1.3, 2.4) × 10 -6 . An observed 95% CL interval of (-133, 175) is obtained for the κ c /κ γ ratio of Higgs boson coupling modifiers, and a 95% CL interval of (-37, 40) is obtained for κ b /κ γ .
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Teaching sterile neutrinos in an undergrad laboratory: a sterile-neutrino search using data from the MicroBooNE liquid-argon time projection chamber performed in an undergraduate teaching laboratory
Dataset associated with "A sterile-neutrino search using data from the MicroBooNE liquid-argon time projection chamber performed in an undergraduate teaching laboratory" an educational paper to teach data analisys within particle physics for 3-4 yr physics major undergrads. Data format are csv, h5 and pkl
Gramellini, Elena [Manchester U.] (ORCID:000000031↗
The NANOGrav 15 yr Data Set: Search for Signals from New Physics
The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Quantum computing without quantum computers: Database search and data processing using classical wave superposition
Quantum computers are proven to be more efficient at solving a specific class of problems compared to traditional digital computers. Superposition of states and quantum entanglement are the two key ingredients that make quantum computing so powerful. However, not all quantum algorithms require quantum entanglement (e.g., search through an unsorted database). Is it possible to utilize classical wave superposition to speed up database searching as much as by using quantum computers? There were several attempts to mimic quantum computers using classical waves. It was concluded that the use of classical wave superposition comes with the cost of an exponential increase in resources. In this work, we consider the feasibility of building classical wave-based devices able to provide fundamental speedup over digital counterparts without the exponential overhead. We present experimental data on database searching through a magnetic database using spin wave superposition. The results demonstrate the same speedup as expected for quantum computers. Also, we present examples of numerical modeling demonstrating classical wave interference for period finding. This approach may not compete with quantum computers with efficiency but outperform classical digital computers. We argue that classical wave-based devices can perform some of the quantum algorithms with the same efficiency as quantum computers as long as quantum entanglement is not required.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Using GANs with adaptive training data to search for new molecules
The process of drug discovery involves a search over the space of all possible chemical compounds. Generative Adversarial Networks (GANs) provide a valuable tool towards exploring chemical space and optimizing known compounds for a desired functionality. Standard approaches to training GANs, however, can result in mode collapse, in which the generator primarily produces samples closely related to a small subset of the training data. In contrast, the search for novel compounds necessitates exploration beyond the original data. In this work, we present an approach to training GANs that promotes incremental exploration and limits the impacts of mode collapse using concepts from Genetic Algorithms. In our approach, valid samples from the generator are used to replace samples from the training data. We consider both random and guided selection along with recombination during replacement. By tracking the number of novel compounds produced during training, we show that updates to the training data drastically outperform the traditional approach, increasing potential applications for GANs in drug discovery.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Data-directed search for new physics based on symmetries of the SM
We propose exploiting symmetries (exact or approximate) of the Standard Model (SM) to search for physics Beyond the Standard Model (BSM) using the data-directed paradigm (DDP). Symmetries are very powerful because they provide two samples that can be compared without requiring simulation. Focusing on the data, exclusive selections which exhibit significant asymmetry can be identified efficiently and marked for further study. Using a simple and generic test statistic which compares two matrices already provides good sensitivity, only slightly worse than that of the profile likelihood ratio test statistic which relies on the exact knowledge of the signal shape. This can be exploited for rapidly scanning large portions of the measured data, in an attempt to identify regions of interest. We also demonstrate that weakly supervised Neural Networks could be used for this purpose as well.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Data-driven search for promising intercalating ions and layered materials for metal-ion batteries
The rise in demand for lithium-ion batteries has led to a large-scale search for electrode materials and intercalating ion species to meet the demands of next-generation energy technologies. Recent efforts largely focus on searching for cathodes that can accommodate large amounts of intercalating ions, but similar work on anodes is relatively limited. This study utilizes machine learning methods to find alternative two-dimensional (2D) materials and intercalating ions beyond Li for metal-ion batteries with high-power efficiencies. The approach first uses density functional theory (DFT) calculations to estimate the theoretical capacities and voltages of various metal ions on 2D materials. The DFT-generated data also provide insights into the local structural accommodation upon ion intercalation on various 2D materials. Significant changes to the lattice can result in irreversible changes to the bonding environments in the anode material, resulting in poor cycling stability. Next, this study develops a binding energy and structural accommodation-based classification model to screen anode materials for next-generation batteries. The classification model selects intercalating ions and 2D material pairs suitable for batteries based on the calculated voltage and volumetric changes in the 2D material upon intercalation. Finally, this study builds a regression model to accurately predict the binding energies of the various intercalating ions on 2D materials. The approach highlights the importance of different elemental and structural features for classification and regression tasks. In conclusion, the insights gained from this study on the role of involved features, such as electronegativities of the constituent ions and the presence of unfilled electronic levels, will help to streamline further studies towards the search for future layered battery materials.
36 MATERIALS SCIENCE↗
High-throughput and data-driven search for stable optoelectronic AMSe 3 materials
The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.
36 MATERIALS SCIENCE↗
First machine learning gravitational-wave search mock data challenge
Not Available
79 ASTRONOMY AND ASTROPHYSICS↗
Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report
Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗