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NASA NTRS2002

Information Theoretic Approaches to Rapid Discovery of Relationships in Large Climate Data Sets

Mutual information as the asymptotic Bayesian measure of independence is an excellent starting point for investigating the existence of possible relationships among climate-relevant variables in large data sets, As mutual information is a nonlinear function of of its arguments, it is not beholden to the assumption of a linear relationship between the variables in question and can reveal features missed in linear correlation analyses. However, as mutual information is symmetric in its arguments, it only has the ability to reveal the probability that two variables are related. it provides no information as to how they are related; specifically, causal interactions or a relation based on a common cause cannot be detected. For this reason we also investigate the utility of a related quantity called the transfer entropy. The transfer entropy can be written as a difference between mutual informations and has the capability to reveal whether and how the variables are causally related. The application of these information theoretic measures is rested on some familiar examples using data from the International Satellite Cloud Climatology Project (ISCCP) to identify relation between global cloud cover and other variables, including equatorial pacific sea surface temperature (SST), over seasonal and El Nino Southern Oscillation (ENSO) cycles.

Knuth, Kevin H.↗

NASA NTRS2015

Managing Large Datasets for Atmospheric Research

Since the mid-1980s, airborne and ground measurements have been widely used to provide comprehensive characterization of atmospheric composition and processes. Field campaigns have generated a wealth of insitu data and have grown considerably over the years in terms of both the number of measured parameters and the data volume. This can largely be attributed to the rapid advances in instrument development and computing power. The users of field data may face a number of challenges spanning data access, understanding, and proper use in scientific analysis. This tutorial is designed to provide an introduction to using data sets, with a focus on airborne measurements, for atmospheric research. The first part of the tutorial provides an overview of airborne measurements and data discovery. This will be followed by a discussion on the understanding of airborne data files. An actual data file will be used to illustrate how data are reported, including the use of data flags to indicate missing data and limits of detection. Retrieving information from the file header will be discussed, which is essential to properly interpreting the data. Field measurements are typically reported as a function of sampling time, but different instruments often have different sampling intervals. To create a combined data set, the data merge process (interpolation of all data to a common time base) will be discussed in terms of the algorithm, data merge products available from airborne studies, and their application in research. Statistical treatment of missing data and data flagged for limit of detection will also be covered in this section. These basic data processing techniques are applicable to both airborne and ground-based observational data sets. Finally, the recently developed Toolsets for Airborne Data (TAD) will be introduced. TAD (tad.larc.nasa.gov) is an airborne data portal offering tools to create user defined merged data products with the capability to provide descriptive statistics and the option to treat measurement uncertainty.

Chen, Gao↗

DOE OSTI2015

Deconstructed transverse mass variables

Traditional searches for R-parity conserving natural supersymmetry (SUSY) require large transverse mass and missing energy cuts to separate the signal from large backgrounds. SUSY models with compressed spectra inherently produce signal events with small amounts of missing energy that are hard to explore. We use this difficulty to motivate the construction of "deconstructed" transverse mass variables which are designed preserve information on both the norm and direction of the missing momentum. Here, we demonstrate the effectiveness of these variables in searches for the pair production of supersymmetric top-quark partners which subsequently decay into a final state with an isolated lepton, jets and missing energy. We show that the use of deconstructed transverse mass variables extends the accessible compressed spectra parameter space beyond the region probed by traditional methods. The parameter space can further be expanded to neutralino masses that are larger than the difference between the stop and top masses. In addition, we also discuss how these variables allow for novel searches of single stop production, in order to directly probe unconstrained stealth stops in the small stop-and neutralino-mass regime. We also demonstrate the utility of these variables for generic gluino and stop searches in all-hadronic final states. Overall, we demonstrate that deconstructed transverse variables are essential to any search wanting to maximize signal separation from the background when the signal has undetected particles in the final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1996

Managing Approach Plate Information Study (MAPLIST): An Information Requirements Analysis of Approach Chart Use

Adequately presenting all necessary information on an approach chart represents a challenge for cartographers. Since many tasks associated with using approach charts are cognitive (e.g., planning the approach and monitoring its progress), and since the characteristic of a successful interface is one that conforms to the users' mental models, understanding pilots' underlying models of approach chart information would greatly assist cartographers. To provide such information, a new methodology was developed for this study that enhances traditional information requirements analyses by combining psychometric scaling techniques with a simulation task to provide quantifiable links between pilots' cognitive representations of approach information and their use of approach information. Results of this study should augment previous information requirements analyses by identifying what information is acquired, when it is acquired, and what presentation concepts might facilitate its efficient use by better matching the pilots' cognitive model of the information. The primary finding in this study indicated that pilots mentally organize approach chart information into ten primary categories: communications, geography, validation, obstructions, navigation, missed approach, final items, other runways, visibility requirement, and navigation aids. These similarity categories were found to underlie the pilots' information acquisitions, other mental models, and higher level cognitive processes that are used to accomplish their approach and landing tasks.

Ricks, Wendell R.↗

DOE OSTI2014

Exploiting the full information carried by jets for reconstructing the mass of the hadronically decaying Z in WZ/ZZ events with a lepton, missing transverse energy and 3 jets at CDF

Observing WZ/ZZ production at the Tevatron in the final state with a lepton, missing transverse energy and two jets is extremely difficult because of the low signal rate and the very large background. In the attempt to increase the acceptance in the analysis of the data collected by the CDF experiment, we study the sample with 3 high-energy jets, where according to simulations about 1/3 of the diboson events are expected to be. Rather than choosing always the two jets of largest transverse energy ( E T ) to reconstruct the Z mass, we make use of the information carried by all jets. We describe in detail how to better combine the jet information, and introduce a method of interest in every experiment searching for hadronic resonances in the W/Z + jets channel, including measurements of Higgs boson production associated with a W or Z.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

A COMPARISON OF SPECTROSCOPIC VERSUS IMAGING TECHNIQUES FOR DETECTING CLOSE COMPANIONS TO KEPLER OBJECTS OF INTEREST

Kepler planet candidates require both spectroscopic and imaging follow-up observations to rule out false positives and detect blended stars. Traditionally, spectroscopy and high-resolution imaging have probed different host star companion parameter spaces, the former detecting tight binaries and the latter detecting wider bound companions as well as chance background stars. In this paper, we examine a sample of 11 Kepler host stars with companions detected by two techniques—near-infrared adaptive optics and/or optical speckle interferometry imaging, and a new spectroscopic deblending method. We compare the companion effective temperatures (T{sub eff}) and flux ratios (F{sub B}/F{sub A}, where A is the primary and B is the companion) derived from each technique and find no cases where both companion parameters agree within 1σ errors. In 3/11 cases the companion T{sub eff} values agree within 1σ errors, and in 2/11 cases the companion F{sub B}/F{sub A} values agree within 1σ errors. Examining each Kepler system individually considering multiple avenues (isochrone mapping, contrast curves, probability of being bound), we suggest two cases for which the techniques most likely agree in their companion detections (detect the same companion star). Overall, our results support the advantage that the spectroscopic deblending technique has for finding very close-in companions (θ ≲ 0.″02–0.″05) that are not easily detectable with imaging. However, we also specifically show how high-contrast AO and speckle imaging observations detect companions at larger separations (θ ≥ 0.″02–0.″05) that are missed by the spectroscopic technique, provide additional information for characterizing the companion and its potential contamination (e.g., position angle, separation, magnitude differences), and cover a wider range of primary star effective temperatures. The investigation presented here illustrates the utility of combining the two techniques to reveal higher-order multiples in known planet-hosting systems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Circular polarisation: a new probe of dark matter and neutrinos in the sky

The study of anomalous electromagnetic emission in the sky is the basis of indirect searches for dark matter. It is also a powerful tool to constrain the radiative decay of active neutrinos. Until now, quantitative analyses have focused on the flux and energy spectrum of such an emission; polarisation has never been considered. Here we show that we could be missing out on an essential piece of information. The radiative decay of neutrinos, as well as the interactions of dark matter and neutrinos with Standard Model particles can generate a circular polarisation signal in X-rays or γ-rays. If observed, this could reveal important information about their spatial distribution and particle-antiparticle ratio, and could even reveal the nature of the high-energy particle physics processes taking place in astrophysical sites. The question of the observability of these polarised signatures and their separation from background astrophysical sources is left for future work.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Constraining neutrinophilic mediators at FASER ν , FLArE, and FASER ν 2

High energy collider neutrinos have been observed for the first time by the FASER ν experiment. The detected spectrum of collider neutrinos scattering off nucleons can be used to probe neutrinophilic mediators with GeV-scale masses. We find that constraints on the pseudoscalar (axial vector) neutrinophilic mediator are close to the scalar (vector) case since they have similar cross section in the neutrino massless limit. We perform an analysis on the measured muon spectra at FASER ν , and find that the bounds on the vector mediator from the current FASER ν data are comparable to the existing bounds at m Z ′ ≈ 0.2 GeV . We also study the sensitivities to a neutrinophilic mediator at future Forward Physics Facilities including FLArE and FASER ν 2 by using both the missing transverse momentum and the charge identification information. We find that FLArE and FASER ν 2 can impose stronger bounds on both the scalar and vector neutrinophilic mediators than the existing bounds. The constraints on the scalar mediator can reach 0.08 (0.1) for m ϕ ≲ 1 GeV with (without) muon charge identification at FASER ν 2 . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data

Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. Here, the proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Analysis of Radiocarbon, Stable Isotopes and DNA in Teeth to Facilitate Identification of Unknown Decedents

The characterization of unidentified bodies or suspected human remains is a frequent and important task for forensic investigators. However, any identification method requires clues to the person’s identity to allow for comparisons with missing persons. If such clues are lacking, information about the year of birth, sex and geographic origin of the victim, is particularly helpful to aid in the identification casework and limit the search for possible matches. We present here results of stable isotope analysis of 13 C and 18 O, and bomb-pulse 14 C analyses that can help in the casework. The 14 C analysis of enamel provided information of the year of birth with an average absolute error of 1.8±1.3 years. We also found that analysis of enamel and root from the same tooth can be used to determine if the 14 C values match the rising or falling part of the bomb-curve. Enamel laydown times can be used to estimate the date of birth of individuals, but here we show that this detour is unnecessary when using a large set of crude 14 C data of tooth enamel as a reference. The levels of 13 C in tooth enamel were higher in North America than in teeth from Europe and Asia, and Mexican teeth showed even higher levels than those from USA. DNA analysis was performed on 28 teeth, and provided individual-specific profiles in most cases and sex determination in all cases. In conclusion, these analyses can dramatically limit the number of possible matches and hence facilitate person identification work.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1988

Infrared low-level wind shear work

Results of field experiments for the detection of clear air disturbance and low level wind shear utilizing an infrared airborne system are given in vugraph form. The hits, misses and nuisance alarms scores are given. Information is given on the infrared spatial resolution technique. The popular index of aircraft hazard (F= WX over g - VN over AS) is developed for a remote temperature sensor.

Adamson, Pat↗

NASA NTRS1991

Monitoring and analysis of data from complex systems

Some of the methods, systems, and prototypes that have been tested for monitoring and analyzing the data from several spacecraft and vehicles at the Marshall Space Flight Center are introduced. For the Huntsville Operations Support Center (HOSC) infrastructure, the Marshall Integrated Support System (MISS) provides a migration path to the state-of-the-art workstation environment. Its modular design makes it possible to implement the system in stages on multiple platforms without the need for all components to be in place at once. The MISS provides a flexible, user-friendly environment for monitoring and controlling orbital payloads. In addition, new capabilities and technology may be incorporated into MISS with greater ease. The use of information systems technology in advanced prototype phases, as adjuncts to mainline activities, is used to evaluate new computational techniques for monitoring and analysis of complex systems. Much of the software described (specially, HSTORESIS (Hubble Space Telescope Operational Readiness Expert Safemode Investigation System), DRS (Device Reasoning Shell), DART (Design Alternatives Rational Tool), elements of the DRA (Document Retrieval Assistant), and software for the PPS (Peripheral Processing System) and the HSPP (High-Speed Peripheral Processor)) is available with supporting documentation, and may be applicable to other system monitoring and analysis applications.

Dollman, Thomas↗

NASA NTRS2002

NASA Hybrid Reflectometer Project

Time-domain and frequency-domain reflectometry have been used for about forty years to locate opens and shorts in cables. Interpretation of reflectometry data is as much art as science. Is there information in the data that is being missed? Can the reflectometers be improved to allow us to detect and locate defects in cables that are not outright shorts or opens? The Hybrid Reflectometer Project was begun this year at NASA Ames Research Center, initially to model wire physics, simulating time-domain reflectometry (TDR) signals in those models and validating the models against actual TDR data taken on testbed cables. Theoretical models of reflectometry in wires will give us an understanding of the merits and limits of these techniques and will guide the application of a proposed hybrid reflectometer with the aim of enhancing reflectometer sensitivity to the point that wire defects can be detected. We will point out efforts by some other researchers to apply wire physics models to the problem of defect detection in wires and we will describe our own initial efforts to create wire physics models and report on testbed validation of the TDR simulations.

Lynch, Dana↗

NASA NTRS2018

Flood Analysis of the Thomas Flood in California Using NASA Data in a GIS

In December 2017, the Thomas Fire burned areas in Ventura and Santa Barbara Counties in California, consuming 281,893 acres (440 square miles), and becoming the largest wildfire in California history. On January 8, 2018, a winter storm made landfall in Santa Barbara County, releasing more than 5 inches of rain onto the same scarred area, causing San Ysidro and Montecito Creeks to overflow and leading to devastating flash flooding and debris flows by January 9, 2018. Several areas that were not typically prone to flooding were at risk due to the higher than usual flows in the nearby streams and creeks. The unprecedented flooding killed at least 20 people and left several others missing. NASA's Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. This poster presents the use of satellite data from NASA's inventory that have the potential for use in identification and analysis of forest fire risk and subsequent after-effects within a geographic information system (GIS).

science↗

NASA NTRSDate not supplied

Investigation of the 30P30N Slat Flow Field with Passive Control Devices Using Particle Image Velocimetry

The leading-edge slat of a high-lift wing is one of the main noise contributors during approach and landing. This paper describes an experimental investigation of the velocity field associated with multiple passive noise treatments, including slat-cusp extensions, a cove filler, and a gap filler, on a two-dimensional multi-element high-lift 30P30N airfoil. Previous work documented comparisons of both surface and far field pressure fluctuations in the presence of these devices with those for a baseline case at different flow conditions. However, important information related to the velocity fields was missing from the previous measurements, hindering our ability to elucidate the changes in the flow physics associated with the noise treatments. Therefore, two-component Particle Image Velocity has been used to investigate the influence of passive noise treatments on the flow fields. All measurements are taken at an effective, free-air angle of attack of 5.5 degrees and a stowed-chord-based Reynolds number of 1.71 × 10e6. The measurements show that the slat extensions shorten the slat-cove shear layer trajectory, resulting in reduced growth of disturbances within the slat-cove shear layer. This leads to a shift of tonal peaks to higher frequencies and a reduction in the tonal amplitudes. The gap filler blocks the flow path through the gap, causing the reattachment location to shift to the main wing leading edge lower surface. Consequently, the feedback loop associated with the flow-acoustic interaction in the baseline case is eliminated, and the turbulent kinetic energy in the slat-cove shear layer is significantly reduced. However, extensive flow separation is observed on the suction side of the gap filler, which does not eliminate the noise reduction benefit due to the gap filler, but does degrade the aerodynamic performance of the high-lift configuration. Finally, the overall flow field in the presence of the cove filler is similar to that in the baseline case at the design angle of attack, but the slat-cove shear layer is eliminated leading to a suppression of the cavity tones associated with the shear layer. This change accounts for the reduction in slat noise as measured in previous work.

Aeroacoustics↗

DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗

DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Development and Validation of the Near-Miss Safety Score (NMSS) Framework for Heavy-Duty Vehicle Safety Assessment

Heavy-duty commercial vehicles present unique safety challenges due to their size, articulation dynamics, and operational complexity. As Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) become more common in Class 8 tractor-trailers, traditional crash-based metrics are no longer sufficient to evaluate safety performance. This study introduces the Near-Miss Safety Score (NMSS)—a quantitative, physics-informed framework developed as a leading indicator of safety for advanced commercial vehicle technologies. NMSS quantifies how close a vehicle or operator comes to a collision or safety-critical event by integrating vehicle kinematics (relative distance, velocity, and acceleration) with driver or system response latency and time-to-collision. A modifier function adjusts the base score for vehicle-specific and environmental factors such as trailer articulation, load distribution, braking condition, and roadway environment. The framework enables systematic evaluation of ADAS/ADS performance under a range of operational and degraded conditions. By capturing near-miss dynamics rather than relying on crash data, NMSS provides a proactive approach to risk assessment, accelerates technology validation, and enhances interpretability for regulators and fleet operators. The proposed NMSS was validated using data collected from a motorcoach platform, demonstrating the framework’s applicability to heavy-duty safety evaluation and performance benchmarking. Results and key insights are presented in this paper.

Siekmann, Adam [ORNL] (ORCID:0000000284653935)↗