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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Fairfax Water Resources: Estimating Urban Flood Susceptibility, Historical Flooding Extent, and Land Cover Change in Fairfax County, Virginia to Aid in Flood Mitigation Planning

Between 2000 and 2020, Fairfax County, Virginia experienced extreme weather events that caused severe flooding and degradation of roads, businesses, and other public property. A single flood event on July 8th, 2019 resulted in $14.8 million in damages. These flood events routinely impact the community, often resulting in power outages, school closures, and downed trees. The Fairfax County Department of Public Works and Environmental Services partnered with DEVELOP to explore how remotely sensed data could be integrated to support its current flood mitigation efforts. This project used environmental factors such as elevation, slope, and topographic wetness index from Earth observation derived data to map flood susceptibility. We utilized Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to map historic flooding events in Fairfax County. These maps will support flood management practices for the Fairfax County Department of Public Works and Environmental Services through the integration of remotely sensed data. Our results show that developed areas in the county are more susceptible to flooding, coinciding with analysis of flood factors, which indicated that imperviousness and tree canopy were the most influential drivers in flood susceptibility. Other results show that using Earth observations to map historical flooding is limited in urban areas due to false positives from SAR imagery between water and shadows. Further research is necessary to evolve the historical flood mapping technique if Earth observations are to be incorporated in future historical flood analysis.

Kaitlynn Hietpas↗
NASA NTRS2023

CST: A Tool for Optimizing the Efficiency and Effectiveness of Static-Code Analysis Tools

Static Code Analysis (SCA) is a vital component of NASA IV&V’s mission assurance for safety-critical software as it reduces the likelihood of software-induced hazards impacting mission success. Static Code Analysis achieves this by identifying hazards that may not have been otherwise detectable by typical code reviews or other testing. Using SCA tools, however, can be intimidating due to steep learning curves, especially considering tool performance and defect coverage varies greatly. Because of this variation amongst SCA tools, understanding which tools support certain defects and which do not, as well as understanding how to run an analysis based on steps that are unique to each tool, can be difficult to both new and experienced analysts alike. To mitigate this, the SCAWG or the IV&V Static Code Analysis Working Group, created the SCA Checker Taxonomy and Starting Point Profiles. The Checker Selection Tool (CST) incorporates these two SCAWG products into an interactive tool which allows the user to: select organized categories of defects they would like the SCA tools to discover, select default checkers depending on their mission type (e.g. flight), and configure multiple SCA tools at once. C/C++, Java, and Python defect checkers from four common SCA tools were utilized in this iteration of the CST. This iteration also includes the addition of training, SCA tool specific help, and taxonomy guide links, into its design to help users new to Static Code Analysis learn how to perform SCA more efficiently. The CST has been subject to beta testing by experienced static code analysts from the SCAWG to ensure a usable and accurate final product. The implications of the CST in the mission assurance of NASA safety-critical software are profound, as the CST can help identify and reduce false positives and false negatives, fundamentally improving overall SCA efficiency and accuracy.

static code analysis↗
NASA NTRS2014

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

The theory of System Health Management (SHM) and of its operational subset Fault Management (FM) states that FM is implemented as a "meta" control loop, known as an FM Control Loop (FMCL). The FMCL detects that all or part of a system is now failed, or in the future will fail (that is, cannot be controlled within acceptable limits to achieve its objectives), and takes a control action (a response) to return the system to a controllable state. In terms of control theory, the effectiveness of each FMCL is estimated based on its ability to correctly estimate the system state, and on the speed of its response to the current or impending failure effects. This paper describes how this theory has been successfully applied on the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program to quantitatively estimate the effectiveness of proposed abort triggers so as to select the most effective suite to protect the astronauts from catastrophic failure of the SLS. The premise behind this process is to be able to quantitatively provide the value versus risk trade‐off for any given abort trigger, allowing decision makers to make more informed decisions. All current and planned crewed launch vehicles have some form of vehicle health management system integrated with an emergency launch abort system to ensure crew safety. While the design can vary, the underlying principle is the same: detect imminent catastrophic vehicle failure, initiate launch abort, and extract the crew to safety. Abort triggers are the detection mechanisms that identify that a catastrophic launch vehicle failure is occurring or is imminent and cause the initiation of a notification to the crew vehicle that the escape system must be activated. While ensuring that the abort triggers provide this function, designers must also ensure that the abort triggers do not signal that a catastrophic failure is imminent when in fact the launch vehicle can successfully achieve orbit. That is, the abort triggers must have low false negative rates to be sure that real crew‐threatening failures are detected, and also low false positive rates to ensure that the crew does not abort from non‐crew‐threatening launch vehicle behaviors. The analysis process described in this paper is a compilation of over six years of lessons learned and refinements from experiences developing abort triggers for NASA's Constellation Program (Ares I Project) and the SLS Program, as well as the simultaneous development of SHM/FM theory. The paper will describe the abort analysis concepts and process, developed in conjunction with SLS Safety and Mission Assurance (S&MA) to define a common set of mission phase, failure scenario, and Loss of Mission Environment (LOME) combinations upon which the SLS Loss of Mission (LOM) Probabilistic Risk Assessment (PRA) models are built. This abort analysis also requires strong coordination with the Multi‐Purpose Crew Vehicle (MPCV) and SLS Structures and Environments (STE) to formulate a series of abortability tables that encapsulate explosion dynamics over the ascent mission phase. The design and assessment of abort conditions and triggers to estimate their Loss of Crew (LOC) Benefits also requires in‐depth integration with other groups, including Avionics, Guidance, Navigation and Control(GN&C), the Crew Office, Mission Operations, and Ground Systems. The outputs of this analysis are a critical input to SLS S&MA's LOC PRA models. The process described here may well be the first full quantitative application of SHM/FM theory to the selection of a sensor suite for any aerospace system.

Lo, Yunnhon↗
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗
DOE OSTI2026

Automating Detection and Diagnosis of Faults, Failures, and Underperformance in PV Plants

The project developed hybrid physics-based and machine-learning methods for near-real-time detection of balance-of-system faults (e.g., string, combiner, and tracker outages) in utility-scale Photovoltaic plants, achieving over 50% true positive rates with under 10% false positives and significantly reducing engineering setup time. In the extended phase, the scope expanded to plant-level underperformance analysis and industry benchmarking through the SUPER.epri.com platform. SUPER standardizes data processing and performance metrics across more than 9 GWac and 120+ plants, enabling robust comparisons and insights into loss rates, inverter downtime, and capacity degradation.

14 SOLAR ENERGY↗
NASA NTRS2021

Data-driven landslide nowcasting at the global scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

XGBoos↗
NASA NTRS2018

Follow-up Imaging of Disk Candidates from the Disk Detective Citizen Science Project: New Discoveries and False Positives in WISE Circumstellar Disk Surveys

The Disk Detective citizen science project aims to find new stars with excess 22 m emission from circumstellar dust in the All WISE data release from the Wide-field Infrared Survey Explorer. We evaluated 261 Disk Detective objects of interest with imaging with the Robo-AO adaptive optics instrument on the 1.5 m telescope at Palomar Observatory and with RetroCam on the 2.5 m du Pont Telescope at Las Campanas Observatory to search for background objects at 0 15–12 separations from each target. Our analysis of these data leads us to reject 7% of targets. Combining this result with statistics from our online image classification efforts implies that at most7.9%±0.2% of All WISE-selected infrared excesses are good disk candidates. Applying our false-positive rates to other surveys, we find that the infrared excess searches of McDonald et al. and Marton et al. all have false-positiverates >70%. Moreover, we find that all 13 disk candidates in Theissen & West with W4 signal-to-noise ratio >3are false positives. We present 244 disk candidates that have survived vetting by follow-up imaging. Of these,213 are newly identified disk systems. Twelve of these are candidate members of comoving pairs based on Gaia astrometry, supporting the hypothesis that warm dust is associated with binary systems. We also note the discovery of 22 m excess around two known members of the Scorpius–Centaurus association, and we identifyknown disk host WISEA J164540.79-310226.6 as a likely Sco-Cen member. Thirty of these disk candidates arecloser than 125 pc (including 26 debris disks), making them good targets for both direct-imaging exoplanetsearches.

Wide-field Infrared Survey Explorer↗
NASA NTRS2012

Validation of Atmosphere/Ionosphere Signals Associated with Major Earthquakes by Multi-Instrument Space-Borne and Ground Observations

The latest catastrophic earthquake in Japan (March 2011) has renewed interest in the important question of the existence of pre-earthquake anomalous signals related to strong earthquakes. Recent studies have shown that there were precursory atmospheric/ionospheric signals observed in space associated with major earthquakes. The critical question, still widely debated in the scientific community, is whether such ionospheric/atmospheric signals systematically precede large earthquakes. To address this problem we have started to investigate anomalous ionospheric / atmospheric signals occurring prior to large earthquakes. We are studying the Earth's atmospheric electromagnetic environment by developing a multisensor model for monitoring the signals related to active tectonic faulting and earthquake processes. The integrated satellite and terrestrial framework (ISTF) is our method for validation and is based on a joint analysis of several physical and environmental parameters (thermal infrared radiation, electron concentration in the ionosphere, lineament analysis, radon/ion activities, air temperature and seismicity) that were found to be associated with earthquakes. A physical link between these parameters and earthquake processes has been provided by the recent version of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model. Our experimental measurements have supported the new theoretical estimates of LAIC hypothesis for an increase in the surface latent heat flux, integrated variability of outgoing long wave radiation (OLR) and anomalous variations of the total electron content (TEC) registered over the epicenters. Some of the major earthquakes are accompanied by an intensification of gas migration to the surface, thermodynamic and hydrodynamic processes of transformation of latent heat into thermal energy and with vertical transport of charged aerosols in the lower atmosphere. These processes lead to the generation of external electric currents in specific regions of the atmosphere and the modifications, by dc electric fields, in the ionosphere-atmosphere electric circuit. We retrospectively analyzed temporal and spatial variations of four different physical parameters (gas/radon counting rate, lineaments change, long-wave radiation transitions and ionospheric electron density/plasma variations) characterizing the state of the lithosphere/atmosphere coupling several days before the onset of the earthquakes. Validation processes consist in two phases: A. Case studies for seven recent major earthquakes: Japan (M9.0, 2011), China (M7.9, 2008), Italy (M6.3, 2009), Samoa (M7, 2009), Haiti (M7.0, 2010) and, Chile (M8.8, 2010) and B. A continuous retrospective analysis was preformed over two different regions with high seismicity- Taiwan and Japan for 2003-2009. Satellite, ground surface, and troposphere data were obtained from Terra/ASTER, Aqua/AIRS, POES and ionospheric variations from DEMETER and COSMIC-I data. Radon and GPS/TEC were obtaining from monitoring sites in Taiwan, Japan and Italy and from global ionosphere maps (GIM) respectively. Our analysis of ground and satellite data during the occurrence of 7 global earthquakes has shown the presence of anomalies in the atmosphere. Our results for Tohoku M9.0 earthquake show that on March 7th, 2011 (4 days before the main shock and 1 day before the M7.2 foreshock of March 8, 2011) a rapid increase of emitted infrared radiation was observed by the satellite data and an anomaly was developed near the epicenter. The GPS/TEC data indicate an increase and variation in electron density reaching a maximum value on March 8. From March 3 to 11 a large increase in electron concentration was recorded at all four Japanese ground-based ionosondes, which returned to normal after the main earthquake. Similar approach for analyzing atmospheric and ionospheric parameters has been applied for China (M7.9, 2008), Italy (M6.3, 2009), Samoa (M7, 2009), Haiti (M7.0, 2010) and Chile (M8.8, 2010) eahquakes. Results have revealed the presence of related variations of these parameters implying their connection with the earthquake process. The second phase (B) of this validation included 102 major earthquakes (M>5.9) in Taiwan and Japan. We have found anomalous behavior before all of these events with no false negatives. False alarm ratio for false positives is less then 10% and has been calculated for the same month of the earthquake occurrence for the entire period of analysis (2003-2009). The commonalities for detecting atmospheric/ionospheric anomalies are: i.) Regularly appearance over regions of maximum stress (i.e., along plate boundaries); ii.) Anomaly existence over land and sea; and iii) association with M>5.9 earthquakes not deeper than 100km. Due to their long duration over the same region these anomalies are not consistent with a meteorological origin. Our initial results from the ISTF validation of multi-instrument space-borne and ground observations show a systematic appearance of atmospheric anomalies near the epicentral area, one to seven (average) days prior to the largest earthquakes, and suggest that it could be explained by a coupling process between the observed physical parameters and the pre-earthquake preparation processes.

Ouzounov, Dimitar↗
NASA NTRS2003

Predicting protein functions from redundancies in large-scale protein interaction networks

Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

Proteins/chemistry/metabolism↗
NASA NTRS2020

Visible - Near Infrared Spectral Indices for Mapping Mineralogy and Chemistry with OSIRIS-REx

The primary objective of the Origins, Spectral Interpretation, Resource Identification, SecurityRegolith Explorer (OSIRIS-REx) mission is to return to Earth a pristine sample of carbonaceous material from the primitive asteroid (101955) Bennu. To support compositional mapping as part of sample site selection and characterization, we tested 95 spectral parameters on visible to near infrared laboratory reflectance data of minerals and carbonaceous meteorites to find which of these parameters reliably identified spectral features of interest. Most spectral parameters had high positive detection rates when used on spectra of pure, single component materials. The meteorite spectra have fewer and weaker absorption features and, as a result, fewer detections with the spectral parameters. Parameters targeting absorptions at 0.7 and 2.7 – 3 µm, which arise due to hydrated minerals, were most successful for the meteorites. Based on these results, we have identified a set of 17 parameters that are most likely to be useful at Bennu and spectral analogs. These parameters detect olivines, pyroxenes, carbonates, water/OH-bearing minerals, serpentines, ferric minerals, and organics. Particle size and albedo are known to affect band depth but had a negligible impact on parameter accuracy. The effect of instrument-like noise added to the laboratory spectra can result in more false positive detections, but these exhibit large errors. Our study has determined the prioritization of spectral parameters used for OSIRISREx spectral analysis and mapping and informs the reliability of all parameter-derived data products.

Hannah H Kaplan↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

RNAseq-based transcriptome assembly of Clostridium acetobutylicum for functional genome annotation and discovery

Accurate genome annotations are essential in modern biology and biotechnology, yet they are still largely based on genome sequencing and comparative analyses. We show that the Clostridium acetobutylicum genome annotation can be markedly improved by integrating bioinformatic predictions with RNA sequencing (RNAseq) data. Samples were acquired under butanol, butyrate, and unstressed treatments across various growth conditions. Analysis of an initial assembly revealed errors due to background signals and limitations of assembly algorithms. Hurdles for RNAseq transcriptome mapping include optimizing library complexity and sequencing depth, yet most studies report low sequencing depth and ignore the effect of ribosomal RNA abundance. An integrative analysis was developed to combine motif predictions, single-nucleotide resolution sequencing depth, and library complexity to resolve difficulties in assembly curation. This minimized false positive error and determined gene boundaries, in some cases, to the exact base-pair of prior studies. This will be the first strand-specific transcriptome assembly in a Clostridium organism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Systematic and scalable genome-wide essentiality mapping to identify nonessential genes in phages

Phages are one of the key ecological drivers of microbial community dynamics, function, and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor intensive and are generally phage specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage–host combinations that facilitate the identification of nonessential genes. As a proof of concept, we use an arrayed genome-wide CRISPR interference (CRISPRi) assay to map gene essentiality landscape in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification, and tracking of phages in diverse applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis

In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates Occam’s razor via the multiplicity or trials factor in the look-elsewhere effect. However, if the prior is not known completely, the frequentist hypothesis test via the false-positive rate is a better approach, as it is less sensitive to the prior choice. We argue that when only partial prior information is available, it is best to combine the two methodologies by using the Bayes factor as a test statistic in the frequentist analysis. We show that the standard frequentist maximum likelihood-ratio test statistic corresponds to the Bayes factor with a non-informative Jeffrey’s prior. We also show that mixed priors increase the statistical power in frequentist analyses over the maximum likelihood test statistic. We develop an analytic formalism that does not require expensive simulations and generalize Wilks’ theorem beyond its usual regime of validity. In specific limits, the formalism reproduces existing expressions, such as the p-value of linear models and periodograms. We apply the formalism to an example of exoplanet transits, where multiplicity can be more than 10 7 . We show that our analytic expressions reproduce the $p$-values derived from numerical simulations. We offer an interpretation of our formalism based on the statistical mechanics. We introduce the counting of states in a continuous parameter space using the uncertainty volume as the quantum of the state. We show that both the $p$-value and Bayes factor can be expressed as an energy versus entropy competition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Improvements of pre-emptive identification of particle accelerator failures using binary classifiers and dimensionality reduction

In this paper we look at the properties of the Spallation Neutron Source (SNS) Differential Beam Current Monitor (DCM) data and various methods of data transformation to improve pre-emptive detection of machine trips. In this study, the foundation of the approach is the analysis of new underlying data and understanding various properties with the goal of faster classification, higher precision and higher recall with the aim to reduce false positives as low as required. The result of the research presented in this paper are a binary classifier capable of predicting accelerator failures with millisecond classification time, 96% precision, 58% true positive and 0% false positive rate and optimization techniques enabling real-time implementations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

A Catalog of Kepler Habitable Zone Exoplanet Candidates

The NASA Kepler mission ha s discovered thousands of new planetary candidates, many of which have been confirmed through follow-up observations. A primary goal of the mission is to determine the occurrence rate of terrestrial-size planets within the Habitable Zone (HZ) of their host stars. Here we provide a list of HZ exoplanet candidates from the Kepler Q1–Q17 Data Release 24 data-vetting process. This work was undertaken as part of the Kepler HZ Working Group. We use a variety of criteria regarding HZ boundaries and planetary sizes to produce complete lists of HZ candidates, including a catalog of 104 candidates within the optimistic HZ and 20 candidates with radii less than two Earth radii within the conservative HZ. We cross-match our HZ candidates with the stellar properties and confirmed planet properties from Data Release 25 to provide robust stellar parameters and candidate dispositions. We also include false-positive probabilities recently calculated by Morton et al. for each of the candidates within our catalogs to aid in their validation. Finally, we performed dynamical analysis simulations for multi-planet systems that contain candidates with radii less than two Earth radii as a step toward validation of those systems.

Astrobiology↗
NASA NTRS2012

Obtaining Accurate Change Detection Results from High-Resolution Satellite Sensors

Multi-date acquisitions of high-resolution imaging satellites (e.g. GeoEye and WorldView), can display local changes of current economic interest. However, their large data volume precludes effective manual analysis, requiring image co-registration followed by image-to-image change detection, preferably with minimal analyst attention. We have recently developed an automatic change detection procedure that minimizes false-positives. The processing steps include: (a) Conversion of both the pre- and post- images to reflectance values (this step is of critical importance when different sensors are involved); reflectance values can be either top-of-atmosphere units or have full aerosol optical depth calibration applied using bi-directional reflectance knowledge. (b) Panchromatic band image-to-image co-registration, using an orthorectified base reference image (e.g. Digital Orthophoto Quadrangle) and a digital elevation model; this step can be improved if a stereo-pair of images have been acquired on one of the image dates. (c) Pan-sharpening of the multispectral data to assure recognition of change objects at the highest resolution. (d) Characterization of multispectral data in the post-image ( i.e. the background) using unsupervised cluster analysis. (e) Band ratio selection in the post-image to separate surface materials of interest from the background. (f) Preparing a pre-to-post change image. (g) Identifying locations where change has occurred involving materials of interest.

multispectral↗