Results for “false positive analysis” — Page 6

NASA NTRSDate not supplied

Performance Evaluation of a Shaped Sonic Boom Detector and Classifier

NASA will soon fly the X‑59 aircraft over selected communities to evaluate community responses to shaped sonic booms. Community tests will include dozens of deployed acoustic sensors capable of measuring and detecting shaped sonic boom waveforms in situ for rapid onboard analysis. To this end, we present a sonic boom detector and classifier. The sonic boom detector identifies a shaped sonic boom within a measured acoustical waveform by calculating the cross-correlation with a template shaped sonic boom waveform. The sonic boom classifier determines whether the identified event is indeed a shaped sonic boom based on the correlation coefficient and the calculated noise exposure level. We evaluate these algorithms using simulations of on- and off-design X-59 sonic boom waveforms injected into previously measured 30 s ambient noise recordings. Results of this case study indicate that the detector identifies a sonic boom with an accuracy of ±100 ms in 99.98% of the cases. Furthermore, for a given 30 s measurement, the classifier shows true-positive rates of approximately 0.9999 when the false-positive rate is 10e-3. The case study demonstrates that the recommended onboard sonic boom detector and classifier should be highly capable of identifying shaped sonic booms.

sonic boom↗

NASA NTRS2012

Initial Evaluation of a Conflict Detection Tool in the Terminal Area

Despite the recent economic recession and its adverse impact on air travel, the Federal Aviation Administration (FAA) continues to forecast an increase in air traffic demand that may see traffic double or triple by the year 2025. Increases in air traffic will burden the air traffic management system, and higher levels of safety and efficiency will be required. The air traffic controllers primary task is to ensure separation between aircraft in their airspace and keep the skies safe. As air traffic is forecasted to increase in volume and complexity [1], there is an increased likelihood of conflicts between aircraft, which adds risk and inefficiency to air traffic management and increases controller workload. To attenuate these factors, recent ATM research has shown that air and ground-based automation tools could reduce controller workload, especially if the automation is focused on conflict detection and resolution. Conflict Alert is a short time horizon conflict detection tool deployed in the Terminal Radar Approach Control (TRACON), which has limited utility due to the high number of false alerts generated and its use of dead reckoning to predict loss of separation between aircraft. Terminal Tactical Separation Assurance Flight Environment (T-TSAFE) is a short time horizon conflict detection tool that uses both flight intent and dead reckoning to detect conflicts. Results of a fast time simulation experiment indicated that TTSAFE provided a more effective alert lead-time and generated less false alerts than Conflict Alert [2]. TSAFE was previously tested in a Human-In-The-Loop (HITL) simulation study that focused on the en route phase of flight [3]. The current study tested the T-TSAFE tool in an HITL simulation study, focusing on the terminal environment with current day operations. The study identified procedures, roles, responsibilities, information requirements and usability, with the help of TRACON controllers who participated in the experiment. Metrics such as lead alert time, alert response time, workload, situation awareness and other measures were statistically analyzed. These metrics were examined from an overall perspective and comparisons between conditions (altitude resolutions via keyboard entry vs. ADS-B entry) and controller positions (two final approach sectors and two feeder sectors) were also examined. Results of these analyses and controller feedback provided evidence of T-TSAFE s potential promise as a useful air traffic controller tool. Heuristic analysis also provided information on ways in which the T-TSAFE tool can be improved. Details of analyses results will be presented in the full paper.

Verma Savita Arora↗

NASA NTRS2000

Synthetic spectra of simulated terrestrial atmospheres containing possible biomarker gases

NASA's proposed Terrestrial Planet Finder, a space-based interferometer, will eventually allow spectroscopic analyses of the atmospheres of extrasolar planets. Such analyses would provide information about the existence of life on these planets. One strategy in the search for life is to look for evidence of O3 (and hence O2) in a planet's atmosphere; another is to look for gases that might be present in an atmosphere analogous to that of the inhabited early Earth. In order to investigate these possibilities, we have calculated synthetic spectra for several hypothetical terrestrial-type atmospheres. The model atmospheres represent four different scenarios. The first two, representing inhabited terrestrial planets, are an Earth-like atmosphere containing variable amounts of oxygen and an early Earth-type atmosphere containing methane. In addition, two cases representing Mars-like and early Venus-like atmospheres were evaluated, to provide possible "false positive" spectra. The calculated spectra suggest that ozone could be detected by an instrument like Terrestrial Planet Finder if the O2 concentration in the planet's atmosphere is > or = 200 ppm, or 10(-3) times the present atmospheric level. Methane should be observable on an early-Earth type planet if it is present in concentrations of 100 ppm or more. Methane has both biogenic and abiogenic sources, but concentrations exceeding 1000 ppm, or 0.1% by volume, would be difficult to produce from abiogenic sources alone. High methane concentrations in a planet's atmosphere are therefore another potential indicator for extraterrestrial life.

NASA Discipline Exobiology↗

NASA NTRS2003

A New Objective Technique for Verifying Mesoscale Numerical Weather Prediction Models

This report presents a new objective technique to verify predictions of the sea-breeze phenomenon over east-central Florida by the Regional Atmospheric Modeling System (RAMS) mesoscale numerical weather prediction (NWP) model. The Contour Error Map (CEM) technique identifies sea-breeze transition times in objectively-analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean post-sea breeze wind direction and speed to compare the observed and forecast winds behind the sea-breeze front. The CEM technique is superior to traditional objective verification techniques and previously-used subjective verification methodologies because: It is automated, requiring little manual intervention, It accounts for both spatial and temporal scales and variations, It accurately identifies and verifies the sea-breeze transition times, and It provides verification contour maps and simple statistical parameters for easy interpretation. The CEM uses a parallel lowpass boxcar filter and a high-order bandpass filter to identify the sea-breeze transition times in the observed and model grid points. Once the transition times are identified, CEM fits a Gaussian histogram function to the actual histogram of transition time differences between the model and observations. The fitted parameters of the Gaussian function subsequently explain the timing bias and variance of the timing differences across the valid comparison domain. Once the transition times are all identified at each grid point, the CEM computes the mean wind direction and speed during the remainder of the day for all times and grid points after the sea-breeze transition time. The CEM technique performed quite well when compared to independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation. The algorithm correctly identified a forecast or observed sea-breeze occurrence or absence 93% of the time during the two- month evaluation period from July and August 2000. Nearly all failures in CEM were the result of complex precipitation features (observed or forecast) that contaminated the wind field, resulting in a false identification of a sea-breeze transition. A qualitative comparison between the CEM timing errors and the subjectively determined observed and forecast transition times indicate that the algorithm performed very well overall. Most discrepancies between the CEM results and the subjective analysis were again caused by observed or forecast areas of precipitation that led to complex wind patterns. The CEM also failed on a day when the observed sea- breeze transition affected only a very small portion of the verification domain. Based on the results of CEM, the RAMS tended to predict the onset and movement of the sea-breeze transition too early and/or quickly. The domain-wide timing biases provided by CEM indicated an early bias on 30 out of 37 days when both an observed and forecast sea breeze occurred over the same portions of the analysis domain. These results are consistent with previous subjective verifications of the RAMS sea breeze predictions. A comparison of the mean post-sea breeze winds indicate that RAMS has a positive wind-speed bias for .all days, which is also consistent with the early bias in the sea-breeze transition time since the higher wind speeds resulted in a faster inland penetration of the sea breeze compared to reality.

Case, Jonathan L.↗

NASA NTRS1992

Automated anomaly detection for Orbiter High Temperature Reusable Surface Insulation

The description, analysis, and experimental results of a method for identifying possible defects on High Temperature Reusable Surface Insulation (HRSI) of the Orbiter Thermal Protection System (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.

Cooper, Eric G.↗

NASA NTRS2010

Support for Systematic Code Reviews with the SCRUB Tool

SCRUB is a code review tool that supports both large, team-based software development efforts (e.g., for mission software) as well as individual tasks. The tool was developed at JPL to support a new, streamlined code review process that combines human-generated review reports with program-generated review reports from a customizable range of state-of-the-art source code analyzers. The leading commercial tools include Codesonar, Coverity, and Klocwork, each of which can achieve a reasonably low rate of false-positives in the warnings that they generate. The time required to analyze code with these tools can vary greatly. In each case, however, the tools produce results that would be difficult to realize with human code inspections alone. There is little overlap in the results produced by the different analyzers, and each analyzer used generally increases the effectiveness of the overall effort. The SCRUB tool allows all reports to be accessed through a single, uniform interface (see figure) that facilitates brows ing code and reports. Improvements over existing software include significant simplification, and leveraging of a range of commercial, static source code analyzers in a single, uniform framework. The tool runs as a small stand-alone application, avoiding the security problems related to tools based on Web browsers. A developer or reviewer, for instance, must have already obtained access rights to a code base before that code can be browsed and reviewed with the SCRUB tool. The tool cannot open any files or folders to which the user does not already have access. This means that the tool does not need to enforce or administer any additional security policies. The analysis results presented through the SCRUB tool s user interface are always computed off-line, given that, especially for larger projects, this computation can take longer than appropriate for interactive tool use. The recommended code review process that is supported by the SCRUB tool consists of three phases: Code Review, Developer Response, and Closeout Resolution. In the Code Review phase, all tool-based analysis reports are generated, and specific comments from expert code reviewers are entered into the SCRUB tool. In the second phase, Developer Response, the developer is asked to respond to each comment and tool-report that was produced, either agreeing or disagreeing to provide a fix that addresses the issue that was raised. In the third phase, Closeout Resolution, all disagreements are discussed in a meeting of all parties involved, and a resolution is made for all disagreements. The first two phases generally take one week each, and the third phase is concluded in a single closeout meeting.

Holzmann, Gerald J.↗

NASA NTRS1994

Total hydrocarbon analysis by ion mobility spectrometry

Astronauts must be alerted quickly to chemical leaks that compromise their health and the success of their missions. An ideal leak detector would be equally sensitive to all compounds that might constitute a hazard and insensitive to nontoxic compounds. No ideal sensor exists; thus, selection of a methodology is a series of compromises. The commonly used methods are either insensitive at the low exposure levels set by OSHA, NASA, and other organizations or are selectively insensitive to important classes of chemicals such as Freons. After extensive study and experience, the Toxicology Group at JSC has selected ion mobility spectrometry (IMS) for development into a broad range, sensitive detector. In addition to the sensing method, signal processing is important leak detection because a background signal can be expected at all times. The leak-detecting instrument must be programmed to discriminate between authentic leaks and background fluctuations caused by routine operations. The results of an evaluation of the prototype THA is presented in terms related to spacecraft operations. The evaluation included determination of instrumental parameters such as stability and response times. We also included responses to some common components of spacecraft atmospheres in pure form and in binary and ternary mixtures. The output of the four algorithms to the mixtures was found to be noticeably different. These responses are compared on the basis of their utility for signaling a chemical leak. As a means of evaluating its resistance to a falsely positive response, the THA was challenged with carbon dioxide and methane, compounds whose concentrations normally increase in spacecraft air during human habitation. The instrument showed virtually no response to these interferences. Although the prototype THA is designed for space flight, this detector is expected to be useful for field screening at chemical waste dumps and other environmentally sensitive locations.

Cross, John H.↗

NASA NTRS2012

Monitoring of International Space Station Telemetry Using Shewhart Control Charts

Shewhart control charts have been established as an expedient method for analyzing dynamic, trending data in order to identify anomalous subsystem performance as soon as such performance would exceed a statistically established baseline. Additionally, this leading indicator tool integrates a selection methodology that reduces false positive indications, optimizes true leading indicator events, minimizes computer processor unit duty cycles, and addresses human factor concerns (i.e., the potential for flight-controller data overload). This innovation leverages statistical process control, and provides a relatively simple way to allow flight controllers to focus their attention on subtle system changes that could lead to dramatic off-nominal system performance. Finally, this capability improves response time to potential hardware damage and/or crew injury, thereby improving space flight safety. Shewhart control charts require normalized data. However, the telemetry from the ISS Early External Thermal Control System (EETCS) was not normally distributed. A method for normalizing the data was implemented, as was a means of selecting data windows, the number of standard deviations (Sigma Level), the number of consecutive points out of limits (Sequence), and direction (increasing or decreasing trend data). By varying these options, and treating them like dial settings, the number of nuisance alerts and leading indicators were optimized. The goal was to capture all leading indicators while minimizing the number of nuisances. Lean Six Sigma (L6S) design of experiment methodologies were employed. To optimize the results, Perl programming language was used to automate the massive amounts of telemetry data, control chart plots, and the data analysis.

Fitch, Jeffery T.↗

NASA NTRS2024

A High-Performance Computing Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

This report offers analysis and design insights for leveraging High-Performance Computing (HPC) to predict line-of-sight (LOS) Global Navigation Satellite System (GNSS) availability in a city. This work is motivated by the emerging fields of Advanced and Urban Air Mobility (AAM/UAM), where regulatory authorities are seeking city-scale, meter-resolution risk forecasting in order to safely integrate new flight missions with existing urban life and infrastructure. This work addresses the technical challenge of efficiently computing urban GNSS satellite visibility to predict GNSS performance metrics under these requirements. We present a new HPC-optimized shadow casting algorithm variant as a ray-based approach to forecasting satellite visibility. We apply this algorithm variant in a software-defined prognostic service which generates a GNSS navigation risk-correlated map as a path planning-style potential field. We detail dominant computational burdens, viable simplifying assumptions, and different algorithmic implementations, intending to demonstrate a baseline of computation time needed by each stage in such a service. We conclude by analyzing the prototype service’s prediction accuracy compared to receiver data from Corpus Christi, Texas. This informs design trade-offs along the dimensions of hardware, computation time, and tolerable forecasting error (including proportions of false positives and false negatives).

GNSS↗

NASA NTRS2019

Lipid Decontamination Procedures for Life Detection Missions

Molecular biosignatures are key targets for current, proposed, and future life detection missions. With the high accuracy and low limit of detection (LOD) that new and future instruments will require, decontamination of life detection hardware is necessary to prevent false positives. Lipids are a molecular biosignature of interest, as they are ubiquitous to all life as we know it, can survive unaltered in the geologic record for longer than any other biomolecule (i.e. billions of years), and form through both biotic and abiotic processes. Lipids display origin-diagnostic molecular patterns that can reveal biotic or abiotic synthesis, so finding them and ascertaining their molecular features is important for potentially detecting evidence of life elsewhere. Traditional methods of decontamination, or contamination control (CC), primarily clean hardware through fabrication in sterile (cleanroom) environments, killing microbes, and removing/flushing contaminants off instrument and spacecraft components. However, research suggests that some standard cleaning methods are either unlikely to remove lipid contaminants or are incompatible with life detection instrument materials. To solve this problem, I propose to find, test, and verify a decontamination method that thoroughly cleans instruments by destroying lipid molecules, but is simultaneously compatible with major materials used in these instruments. I will study the effects of traditional CC methods (including Dry Heat Microbial Reduction and Vapor phase Hydrogen Peroxide) and experimental CC methods (Electron Beam Irradiation) on lipid molecules for application to life detection instrumentation. I will then develop a CC plan for a novel lipid detector (ExCALiBR, Extractor for Chemical Analysis of Lipid Biomarkers in Regolith) searching for lipids in either soil or icy world scenarios. This plan will uphold planetary protection regulation requirements and validate experimental analyses of in-situ life detection tests.

Decontamination↗

NASA NTRS2017

Kepler Planet Detection Metrics: Per-Target Flux-Level Transit Injection Tests of TPS for Data Release 25

Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.

DR25↗

DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗

NASA NTRS2018

Kepler Data Validation I: Architecture, Diagnostic Tests, and Data Products for Vetting Transiting Planet Candidates

The Kepler Mission was designed to identify and characterize transiting planets in the Kepler Field of View and to determine their occurrence rates. Emphasis was placed on identification of Earth-size planets orbiting in the Habitable Zone of their host stars. Science data were acquired for a period of four years. Long-cadence data with 29.4 min sampling were obtained for approx. 200,000 individual stellar targets in at least one observing quarter in the primary Kepler Mission. Light curves for target stars are extracted in the Kepler Science Data Processing Pipeline, and are searched for transiting planet signatures. A Threshold Crossing Event is generated in the transit search for targets where the transit detection threshold is exceeded and transit consistency checks are satisfied. These targets are subjected to further scrutiny in the Data Validation (DV) component of the Pipeline. Transiting planet candidates are characterized in DV, and light curves are searched for additional planets after transit signatures are modeled and removed. A suite of diagnostic tests is performed on all candidates to aid in discrimination between genuine transiting planets and instrumental or astrophysical false positives. Data products are generated per target and planet candidate to document and display transiting planet model fit and diagnostic test results. These products are exported to the Exoplanet Archive at the NASA Exoplanet Science Institute, and are available to the community. We describe the DV architecture and diagnostic tests, and provide a brief overview of the data products. Transiting planet modeling and the search for multiple planets on individual targets are described in a companion paper. The final revision of the Kepler Pipeline code base is available to the general public through GitHub. The Kepler Pipeline has also been modified to support the Transiting Exoplanet Survey Satellite (TESS) Mission which is expected to commence in 2018.

data analysis↗

DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

The Air Quality Monitor “Benzene” Anomaly: Ground Testing and On-going Effects

The International Space Station (ISS) Air Quality Monitors (AQMs) have provided targeted in-flight analysis of volatile organic compounds (VOCs) in the ISS atmosphere since early 2013. During their initial half decade of use covering multiple sets of units, the AQMs performed well, meeting their validation criteria and showing excellent accuracy compared to archival samples. In addition to routine environmental monitoring, the AQMs have also been used during a number of contingency situations and investigations related to Environmental Control and Life Support Systems (ECLSS). These include a potential ammonia leak, increases in atmospheric ethanol, and efforts to locate potential sources of polydimethylsiloxanes that lead to the production of dimethylsilanediol (DMSD) in the US Water Processor Assembly (WPA). As the fleet of AQMs has aged, several issues have arisen. These have ranged from pervasive problems on electronics boards to loss of sensitivity due to operating in an elevated CO2 environment. The most notable issue encountered during on-orbit operations was incorrect identification of compounds. This initially occurred in mid-2020, when AQM1 reported the presence of benzene. While the AQM team questioned the validity of these results, the concentration of the “benzene” continued to increase and eventually exceeded the 30- and 180-day Spacecraft Maximum Allowable Concentration (SMAC). This led to wide-ranging efforts by a number of groups aimed at understanding the situation and identifying the source of the “benzene.” AQM1 failed after being relocated to the Russian Segment as part of the investigation, and the unit was returned for evaluation. When archive samples collected while the AQM was measuring elevated benzene showed no detectable benzene, the focus of the investigation shifted to determining the cause of the false positive readings. Here, we will discuss the results of this investigation by the AQM team, potential causes of the interference, and subsequent reporting of AQM1 results.

W T Wallace↗

NASA NTRS2024

The Air Quality Monitor “Benzene” Anomaly: Ground Testing and On-going Effects

The International Space Station (ISS) Air Quality Monitors (AQMs) have provided targeted in-flight analysis of volatile organic compounds (VOCs) in the ISS atmosphere since early 2013. During their initial half decade of use covering multiple sets of units, the AQMs performed well, meeting their validation criteria and showing excellent accuracy compared to archival samples. In addition to routine environmental monitoring, the AQMs have also been used during a number of contingency situations and investigations related to Environmental Control and Life Support Systems (ECLSS). These include a potential ammonia leak, increases in atmospheric ethanol, and efforts to locate potential sources of polydimethylsiloxanes that lead to the production of dimethylsilanediol (DMSD) in the US Water Processor Assembly (WPA). As the fleet of AQMs has aged, several issues have arisen. These have ranged from pervasive problems on electronics boards to loss of sensitivity due to operating in an elevated CO2 environment. The most notable issue encountered during on-orbit operations was incorrect identification of compounds. This initially occurred in mid-2020, when AQM1 reported the presence of benzene. While the AQM team questioned the validity of these results, the concentration of the “benzene” continued to increase and eventually exceeded the 30- and 180-day Spacecraft Maximum Allowable Concentration (SMAC). This led to wide-ranging efforts by a number of groups aimed at understanding the situation and identifying the source of the “benzene.” AQM1 failed after being relocated to the Russian Segment as part of the investigation, and the unit was returned for evaluation. When archive samples collected while the AQM was measuring elevated benzene showed no detectable benzene, the focus of the investigation shifted to determining the cause of the false positive readings. Here, we will discuss the results of this investigation by the AQM team, potential causes of the interference, and subsequent reporting of AQM1 results.

W T Wallace↗

NASA NTRSDate not supplied

The Air Quality Monitor “Benzene” Anomaly: Ground Testing and On-going Effects

The International Space Station (ISS) Air Quality Monitors (AQMs) have provided targeted in-flight analysis of volatile organic compounds (VOCs) in the ISS atmosphere since early 2013. During their initial half decade of use covering multiple sets of units, the AQMs performed well, meeting their validation criteria and showing excellent accuracy compared to archival samples. In addition to routine environmental monitoring, the AQMs have also been used during a number of contingency situations and investigations related to Environmental Control and Life Support Systems (ECLSS). These include a potential ammonia leak, increases in atmospheric ethanol, and efforts to locate potential sources of polydimethylsiloxanes that lead to the production of dimethylsilanediol (DMSD) in the US Water Processor Assembly (WPA). As the fleet of AQMs has aged, several issues have arisen. These have ranged from pervasive problems on electronics boards to loss of sensitivity due to operating in an elevated CO2 environment. The most notable issue encountered during on-orbit operations was incorrect identification of compounds. This initially occurred in mid-2020, when AQM1 reported the presence of benzene. While the AQM team questioned the validity of these results, the concentration of the “benzene” continued to increase and eventually exceeded the 30- and 180-day Spacecraft Maximum Allowable Concentration (SMAC). This led to wide-ranging efforts by a number of groups aimed at understanding the situation and identifying the source of the “benzene.” AQM1 failed after being relocated to the Russian Segment as part of the investigation, and the unit was returned for evaluation. When archive samples collected while the AQM was measuring elevated benzene showed no detectable benzene, the focus of the investigation shifted to determining the cause of the false positive readings. Here, we will discuss the results of this investigation by the AQM team, potential causes of the interference, and subsequent reporting of AQM1 results.

William T Wallace↗

NASA NTRS2018

Exoplanet Biosignatures: At the Dawn of a New Era of Planetary Observations

The rapid rate of discoveries of exoplanets has expanded the scope of the science possible for the remote detection of life beyond Earth. The Exoplanet Biosignatures Workshop-Without-Walls (EBWWW) held in 2016 engaged the international scientific community across diverse scientific disciplines, to assess the state of the science and technology in the search for life on exoplanets, and to identify paths for progress. The workshop activities resulted in five (5) major review papers which provide: 1) an encyclopedic review of known and proposed biosignatures and models used to ascertain them; 2) an in-depth review of O2 as a biosignature, rigorously examining the nuances of false positives and negatives for evidence of life; 3) a Bayesian framework to comprehensively organize current understanding to quantify confidence in biosignature assessments; 4) an extension of that Bayesian framework in anticipation of increasing planetary data and novel concepts of biosignatures, and 5) a review of the upcoming telescope capabilities to characterize exoplanets and their environment. Because of the immense content of these review papers, this summary provides a guide to their complementary scope and highlights salient features. Strong themes that emerged from the workshop were that biosignatures must be interpreted in the context of their environment, and that frameworks must be developed to link diverse forms of scientific understanding of that context to quantify the likelihood that a biosignature has been observed. Models are needed to explore the parameter space where measurements will be widespread but sparse in detail. Given the technological prospects for large ground-based telescopes and direct imaging from space-based observatories, the detection of atmospheric signatures of a few potentially habitable planets may come before 2030.

spectral imaging↗