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On the Hunt for Detectable Biosignatures in Jezero Crater: What to Look for and Where

Introduction: The Perseverance rover, which is currently exploring Jezero crater on Mars, is equipped with seven instruments that allow for observation of textures, minerals, color, structure, and chemistry of rocks and sediments in order to search for signs of ancient life, understand the geologic history of the crater, and identify candidates for sample return (Farley et al. 2020). The first of these aims includes the direct detection of potential biosignatures, including textures, organic molecules, minerals, and elemental chemistries that are of biogenic origin (Mustard et al. 2013). The presence of these biosignatures will be constrained by the habitability of the local region, the preservation potential of the host rocks, and the sensitivity of the instrument suite, and their biogenicity will be investigated after return to Earth as part of the Mars Sample Return campaign. Here, we examine key targets on the three planned campaigns, potential biosignatures that may be present, and the capabilities of key rover instruments. High Potential Biosignature Sites in Jezero: To date, measurements have been made on multiple sites that both contain minerals known in terrestrial settings to preserve biosignatures and likely were habitable settings (Williford et al. 2021). Two examples include the fine-grained rocks at the base of the delta fan and the NW inner margin of the crater. Fine-grained Rocks at Base of Delta Fan. Fine-grained, clay-bearing rocks may have been deposited as muddy lake sediments that could have hosted life and preserved biosignatures settling out of the water column. The report of organic molecules by the Curiosity rover in the Sheepbed mudstone and Murray formation has highlighted this site in particular. Potential biosignatures in mudstones, especially those rich in silica (McMahon et al. 2018), microbialites and complex organics. NW Inner Margin of Crater. This unit, located along the inner margin of the crater, contains strong carbonate signatures and may have been the littoral zone of a lake (Horgan et al. 2020). The potential biosignatures here include microfossils, microbialites, biominerals, and complex organics. Detectability of Biosignatures by the Mars 2020 Instrument Suite: The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument comprises a Deep UV spectrometer, context imager, and color camera to generate spatially resolved chemical maps (Bhartia et al. 2020). It is sensitive to trace organics as well as a range of minerals and can detect native fluorescence from aromatic organics; the WATSON and ACI cameras can be used to observe morphologies such as stromatolitic laminations or filaments ranging from the tens of micron to millimeter scale. SHERLOC can detect organics that may be present in either of the high potential biosignature targets, as well as detect carbonates in the latter. However, the presence of high amounts of iron, such as in iron-rich clays, would cause attenuation of spectral response through UV absorption. The Planetary Instrument for X-ray Lithochemistry (PIXL) comprises an X-ray fluorescence spectrometer and camera that can scan rock surfaces to generate elemental maps (Allwood et al. 2020). PIXL can detect chemical biosignatures such as spatial variations of elemental abundances that may have resulted from biological activity. PIXL would be particularly useful in detecting fine textures and elemental chemistries in either high potential biosignature target, but cannot directly detect minerals such as carbonate. The SuperCam instrument performs three types of spectroscopy, color imaging, and acoustic recording to remotely examine elemental composition, minerals, organics, and textures (Maurice et al. 2021). Using laser induced breakdown spectroscopy and time resolved luminescence spectroscopy, SuperCam can detect major elemental building blocks of organics (i.e., C, H, N, O, P, S) and conjugated organic structures, respectively, which may be found in either site. While in other contexts, luminescence is a useful tool for biosignature identification, luminescence generated by the 532 nm laser may obscure the Raman signal. Conclusion: The three instruments discussed can be used collaboratively to establish the presence of potential biosignatures in samples. These high-priority samples may then be returned to Earth for detailed laboratory analysis.

S. Sharma↗
DOE OSTI2014

Laboratory and Field Testing of Commercially Available Detectors for the Identification of Chemicals of Interest in the Nuclear Fuel Cycle for the Detection of Undeclared Activities

Traditionally, IAEA inspectors have focused on the detection of nuclear indicators as part of infield inspection activities. The ability to rapidly detect and identify chemical as well as nuclear signatures can increase the ability of IAEA inspectors to detect undeclared activities at a site. Identification of chemical indicators have been limited to use in the analysis of environmental samples. Although IAEA analytical laboratories are highly effective, environmental sample processing does not allow for immediate or real-time results to an IAEA inspector at a facility. During a complementary access inspection, under the Additional Protocol, the use of fieldable technologies that can quickly provide accurate information on chemicals that may be indicative of undeclared activities can increase the ability of IAEA to effectively and efficiently complete their mission. The Complementary Access Working Group (CAWG) is a multi-laboratory team with members from Brookhaven National Laboratory, Idaho National Laboratory, Los Alamos National Laboratory, and Sandia National Laboratory. The team identified chemicals at each stage of the nuclear fuel cycle that may provide IAEA inspectors with indications that proliferation activities may be occurring. The group eliminated all indicators related to equipment, technology and training, developing a list of by-products/effluents, non-nuclear materials, nuclear materials, and other observables. These proliferation indicators were prioritized based on detectability from a conduct of operations (CONOPS) perspective of a CA inspection (for example, whether an inspector actually can access the S&O or whether it is in process with no physical access), and the IAEA’s interest in the detection technology in conjunction with radiation detectors. The list was consolidated to general categories (nuclear materials from a chemical detection technique, inorganic chemicals, organic chemicals, halogens, and miscellaneous materials). The team then identified commercial off the shelf (COTS) chemical detectors that may detect the chemicals of interest. Three chemical detectors were selected and tested both in laboratory settings and in field operations settings at Idaho National Laboratory. The instruments selected are: Thermo Scientific TruDefender FT (FTIR), Thermo Scientific FirstDefender RM (Raman), and Bruker Tracer III SD (XRF). Functional specifications, operability, and chemical detectability, selectivity, and limits of detection were determined. Results from the laboratory and field tests will be presented. This work is supported by the Next Generation Safeguards Initiative, Office of Nonproliferation and International Security, National Nuclear Security Administration.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

The Impact of Below Detection Limit Samples in (NPP) Decommissioning Residual Risk Assessment - 17033

There are some technical problems with finalizing regulations and standards that address nuclear power plant (NPP) decommissioning site soil analyses for license termination. It is difficult to know how to best include detection limit observations from site soil analysis, because typical soil collection data is reported above, at, and below the detection limit. In addition, multiple detection limits are required to adequately estimate the soil radionuclide concentrations. In the literature, researchers analyzing environmental data refer to below the detection limit observations as 'left-censored data'. Left-censored data are less than the detection limit of the instrument(s) that made the measurements. Conventional approaches to managing left-censored data either ignore or simply replace the detection limits with zero, a fraction of the detection limit, or the detection limit itself. However, these approaches are statistically biased and limited in their usefulness. The Kaplan-Meier method, robust regression on order statistics method, and maximum likelihood estimation method are proposed to estimate the left-censored data more precisely. The proposed methods are applied to data from a Monazite powder manufacturing plant and the Colorado School of Mines Research Institute. Summary statistics such as the sample mean, sample standard deviation, sample percentiles are calculated from each data set and are used in risk and volume estimation calculations. Previous environmental analyses, such as the exposure assessment, recognize the uncertainty issue that is addressed in this research using the Latin hypercube sampling approach. Next, probabilistic distributions of the input parameters are developed for the uncertainty analyses. Finally, a framework methodology depicting the decision-making process in the proposed method is presented. The framework defines the critical steps, the amount of radioactivity, types of distributions, censoring percentage, sample size, and number of detection limits; it also allows the user to select the appropriate approach based on their site-specific data and analyses. The goal of this research is to develop a more precise risk assessment, estimate of the volume of soil removal, examine potential cost savings. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Detection of Historical and Future Precipitation Variations and Extremes Over the Continental United States

Problem: The overall goal of this proposal is to detect observed seasonal-mean precipitation variations and extreme event occurrences over the United States. Detection, e.g. the process of demonstrating that an observed change in climate is unusual, first requires some means of estimating the range of internal variability absent any external drivers. Ideally, the internal variability would be derived from the observations themselves, however generally the observed variability is a confluence of both internal variability and variability in response to external drivers. Further, numerical climate models—the standard tool for detection studies—have their own estimates of intrinsic variability, which may differ substantially from that found in the observed system as well as other model systems. These problems are further compounded for weather and climate extremes, which as singular events are particularly ill-suited for detection studies because of their infrequent occurrence, limited spatial range, and underestimation within global and even regional numerical models. Rationale: As a basis for this research we will show how stochastic daily-precipitation models—models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period—can be used to address many of these issues simultaneously. Through the novel application of these well-established models, we can first estimate the changes/trends in various means and extremes that can occur even with fixed daily-precipitation characteristics, e.g. that can occur simply as a result of the stochastic evolution of daily weather events within a given climate. Detection of a change in the observed climate—either naturally or anthropogenically forced—can then be defined as any change relative to this stochastic variability, e.g. as changes/trends in the means and extremes that could only have occurred through a change in the underlying climate. As such, this method is capable of detecting “hot spot” regions—as well as “flare ups” within the hot spot regions—that have experienced interannual to multi-decadal scale variations and trends in seasonal-mean precipitation and extreme events. Further by applying the same methods to numerical climate models we can discern the fidelity of the current-generation climate models in representing detectability within the observed climate system. In this way, we can objectively determine the utility of these model systems for performing detection studies of historical and future climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Acoustic vs. AI-Based Satellite Leak Detection in Aging US Water Infrastructure: A Cost and Energy Savings Analysis

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $\$4$–$\$4.8$ billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes.

acoustic leak detection↗
DOE OSTI2017

Simulation of Laser Ultrasonics for Detection of Surface-Connected Rail Defects

Laser ultrasonic produces frequencies in the MHz range, enabling high accuracy and a strong ability to detect rail surface defects. This paper mainly studied on the simulation of detecting surface-connected rail defects on 60 kg rails with laser ultrasonic, established the finite element model of laser-excited ultrasonic Rayleigh wave, carried out the simulation, and verified the effectiveness of the technology through experiments. To solve the problem that laser ultrasonic is insensitive to the width of defects in actual detection, and unable to make quantitative detection of defects, this paper established a new model on the basis of improving the original model that has been verified, exciting ultrasonic at the two sides at the same time of a rail with two staggered beams of laser separately to detect irregular scratch defects on rail surface, and two groups of signal data were received through two probes. Each group of data can present the half-profile information of defects, and further form two detection images of the defect. At last, the two detection images were combined into a complete image through image processing. The results of the experiment indicate that the technology studied offers a new method for the effective quantitative detection of surface-connected defects on rail.

42 ENGINEERING↗
DOE OSTI2020

Dielectrophoresis assisted rapid, selective and single cell detection of antibiotic resistant bacteria with G-FETs

Time-consuming, expensive and low sensitivity diagnostic methods used for monitoring bacterial infections lead to unnecessary or delays in prescription of the right antibiotic treatment. Determining an optimal clinical treatment requires rapid detection and identification of pathogenic bacteria and their sensitivity to specific antimicrobials. However, diagnostic devices that meet all of these criteria have proven elusive thus far. Graphene field effect transistors (G-FET) are a promising solution, since they are highly sensitive to chemical/biological modification, can have fast detection times and can be placed on different substrates. Here, by integrating specific peptide probes over G-FETs, we present a proof-of-concept study for species and strain specific label-free detection of clinical strains of pathogenic bacteria with high specificity and sensitivity. We found that pyrene-conjugated peptides immobilized on G-FETs were capable of detecting pathogenic Staphylococcus aureus at the single-cell level and discriminate against other gram-positive and gram-negative bacterial pathogens. A similar device was able to discriminate between antibiotic resistant and sensitive strains of Acinetobacter baumannii , suggesting that these devices can also be used for detecting antibiotic resistive pathogens. Furthermore, a new means of enhancing attachment, electric-field assisted binding, reduced the detection limit to 104 cells/ml and the detection time to below 5 minutes. Here, the combination of single step attachment, inexpensive production, rapid, selective and sensitive detection suggest G-FETs plus pyrene-conjugated peptides are a new platform for solving major challenges faced in point of care diagnostics to fight infectious diseases and antimicrobial resistance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

A data-driven CO 2 leakage detection using seismic data and spatial–temporal densely connected convolutional neural networks

In carbon capture and sequestration (also known as carbon capture and storage, or CCS), developing effective monitoring methods is needed to detect and respond to CO 2 leakage. CO 2 leakage detection methods rely on geophysical observations and monitoring sensor network. However, traditional methods usually require the development of site-specific physical models and expert interpretation, and the effectiveness of these methods can be limited to different application locations, operational scenarios, and conditions. Here, we developed a novel data-driven leakage detection method based on densely connected convolutional neural networks. Our method is an end-to-end detection approach, that differs from conventional leakage monitoring methods by directly learning a mapping relationship between seismic data and the CO 2 leakage mass. To account for the spatial and temporal characteristics of seismic data, our novel networks architecture combines 1D and 2D convolutional neural networks. To overcome the computational expense of solving optimization problems, we apply a densely-connecting strategy in our network architecture that reduces the number of network parameters. Based on the features generated by our convolutional neural networks, we further incorporate a long short-term memory network to utilize time-sequential information, which further improves the detection accuracy. Lastly, we employ our detection method to synthetic seismic datasets generated based on flow simulations of a hypothetical CO 2 storage scenario with injection into a partially compartmentalized sandstone storage reservoir. To evaluate method performance, we conducted multiple experiments including a random leakage test, a sequential test, and a robustness test. Numerical results show that our CO 2 leakage detection method successfully detects the leakage and accurately predicts the leakage mass, suggesting that it has the potential for application in monitoring of real CO 2 storage sites.

58 GEOSCIENCES↗
DOE OSTI2023

Driving in the dark: Deciphering nighttime driver detection of free-ranging roadside wildlife

Wildlife-vehicle collisions are dangerous for motorists; however, few studies have addressed driver detection of roadside animals, and none have evaluated detection of free-ranging wildlife. Here, we used 24 volunteer drivers, infrared videography, a 75-km route, and free-ranging wildlife to quantify factors influencing (1) probability of wildlife detection, (2) detection distance, and (3) probability of dangerous encounters (i.e., detection distance < distance required for braking) for multiple species at night in South Carolina, USA. Detection probability of white-tailed deer (Odocoileus virginianus) was impacted by multiple driver, animal, and roadside factors. Deer detection distances increased by 20.99 m when drivers used high-beam headlights and 23.36 m when deer were moving but decreased by 0.71 m for every minute into a drive. Every encounter with wild pigs (Sus scrofa) and most encounters with small mammals were considered dangerous. Our findings suggest most drivers cannot safely detect deer, wild pigs, and small mammals at night.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SPIDARman: System-Level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors

In nuclear power plants (NPPs), anomalies arising from sensors or human errors (HEs) can undermine the performance and reliability of plant operations. Anomaly detection models can be employed to detect sensor errors and HEs. Additionally, physics-informed machine learning models can utilize the known physics of the system, as described by mathematical equations, to ensure that sensor values are consistent with physical laws. Hence, we propose SPIDARman: System-level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors, a holistic physics-informed anomaly detection approach based on generative adversarial networks (GANs) to detect anomalies in both automatically collected sensor data and manually collected surveillance data. Here we test our approach on data collected from a flow loop testbed, showcasing its potential to detect anomalies. Results demonstrate that the proposed model performs better than the baseline GAN-based models in detecting sensor and surveillance anomalies, suggesting the potential of physics-informed anomaly detection GAN models in NPPs.

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 OSTI2014

Detection of a faint fast-moving near-Earth asteroid using the synthetic tracking technique

We report a detection of a faint near-Earth asteroid (NEA) using our synthetic tracking technique and the CHIMERA instrument on the Palomar 200 inch telescope. With an apparent magnitude of 23 (H = 29, assuming detection at 20 lunar distances), the asteroid was moving at 6.°32 day{sup –1} and was detected at a signal-to-noise ratio (S/N) of 15 using 30 s of data taken at a 16.7 Hz frame rate. The detection was confirmed by a second observation 77 minutes later at the same S/N. Because of its high proper motion, the NEA moved 7 arcsec over the 30 s of observation. Synthetic tracking avoided image degradation due to trailing loss that affects conventional techniques relying on 30 s exposures; the trailing loss would have degraded the surface brightness of the NEA image on the CCD down to an approximate magnitude of 25 making the object undetectable. This detection was a result of our 12 hr blind search conducted on the Palomar 200 inch telescope over two nights, scanning twice over six (5.°3 × 0.°046) fields. Detecting only one asteroid is consistent with Harris's estimates for the distribution of the asteroid population, which was used to predict a detection of 1.2 NEAs in the H-magnitude range 28-31 for the two nights. The experimental design, data analysis methods, and algorithms are presented. We also demonstrate milliarcsecond-level astrometry using observations of two known bright asteroids on the same system with synthetic tracking. We conclude by discussing strategies for scheduling observations to detect and characterize small and fast-moving NEAs using the new technique.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data

Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/ communications. Intrusion Detection Systems (IDS) are widely deployed for anomaly detection in such critical infrastructures. This paper presents an Interpretable Anomaly Detection System (RX-ADS) for intrusion detection in CAN protocol communication in EVs. Contributions include: 1) window based feature extraction method; 2) deep Autoencoder based anomaly detection method; and 3) adversarial machine learning based explanation generation methodology. The presented approach was tested on two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection performance of RX-ADS was compared against the state-of-the-art approaches on these datasets: HIDS and GIDS. The RX-ADS approach presented performance comparable to the HIDS approach (OTIDS dataset) and has outperformed HIDS and GIDS approaches (Car Hacking dataset). Further, the proposed approach was able to generate explanations for detected abnormal behaviors arising from various intrusions. Furthermore, these explanations were later validated by information used by domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the method can be trained on unlabeled data; 2) explanations help experts in understanding anomalies and root course analysis, and also help with AI model debugging and diagnostics, ultimately improving user trust in AI systems.

42 ENGINEERING↗
DOE OSTI2014

Review of current neutron detection systems for emergency response

Neutron detectors are utilized in a myriad of applications—from safeguarding special nuclear materials (SNM) to determining lattice spacing in soft materials. The transformational changes taking place in neutron detection and imaging techniques in the last few years are largely being driven by the global shortage of helium-3 ( 3 He). This article reviews the status of neutron sensors used specifically for SNM detection in radiological emergency response. These neutron detectors must be highly efficient, be rugged, have fast electronics to measure neutron multiplicity, and be capable of measuring direction of the neutron sources and possibly image them with high spatial resolution. Neutron detection is an indirect physical process: neutrons react with nuclei in materials to initiate the release of one or more charged particles that produce electric signals that can be processed by the detection system. Therefore, neutron detection requires conversion materials as active elements of the detection system; these materials may include boron-10 ( 10 B), lithium-6 ( 6 Li), and gadollinium-157 ( 157 Gd), to name a few, but the number of materials available for neutron detection is limited. However, in recent years, pulse-shape-discriminating plastic scintillators, scintillators made of helium-4 ( 4 He) under high pressure, pillar and trench semiconductor diodes, and exotic semiconductor neutron detectors made from uranium oxide and other materials have widely expanded the parameter space in neutron detection methodology. In this article we will pay special attention to semiconductor-based neutron sensors. Finally, modern microfabricated nanotubes covered inside with neutron converter materials and with very high aspect ratios for better charge transport will be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

SU-E-T-310: Targeting Safety Improvements Through Analysis of Near-Miss Error Detection Points in An Incident Learning Database

Purpose: Radiation treatment planning involves a complex workflow that can make safety improvement efforts challenging. This study utilizes an incident reporting system to identify detection points of near-miss errors, in order to guide our departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or their patterns. Methods: 1377 incidents were analyzed from a departmental nearmiss error reporting system from 3/2012–10/2013. All incidents were prospectively reviewed weekly by a multi-disciplinary team, and assigned a near-miss severity score ranging from 0–4 reflecting potential harm (no harm to critical). A 98-step consensus workflow was used to determine origination and detection points of near-miss errors, categorized into 7 major steps (patient assessment/orders, simulation, contouring/treatment planning, pre-treatment plan checks, therapist/on-treatment review, post-treatment checks, and equipment issues). Categories were compared using ANOVA. Results: In the 7-step workflow, 23% of near-miss errors were detected within the same step in the workflow, while an additional 37% were detected by the next step in the workflow, and 23% were detected two steps downstream. Errors detected further from origination were more severe (p<.001; Figure 1). The most common source of near-miss errors was treatment planning/contouring, with 476 near misses (35%). Of those 476, only 72(15%) were found before leaving treatment planning, 213(45%) were found at physics plan checks, and 191(40%) were caught at the therapist pre-treatment chart review or on portal imaging. Errors that passed through physics plan checks and were detected by therapists were more severe than other errors originating in contouring/treatment planning (1.81 vs 1.33, p<0.001). Conclusion: Errors caught by radiation treatment therapists tend to be more severe than errors caught earlier in the workflow, highlighting the importance of safety checks in dosimetry and physics. We are utilizing our findings to improve manual and automated checklists for dosimetry and physics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Radio counterparts of compact binary mergers detectable in gravitational waves: a simulation for an optimized survey

Mergers of binary neutron stars and black hole–neutron star binaries produce gravitational-wave (GW) emission and outflows with significant kinetic energies. These outflows result in radio emissions through synchrotron radiation. We explore the detectability of these synchrotron-generated radio signals by follow-up observations of GW merger events lacking a detection of electromagnetic counterparts in other wavelengths. We model radio light curves arising from (i) sub-relativistic merger ejecta and (ii) ultra-relativistic jets. The former produce radio remnants on timescales of a few years and the latter produce γ-ray bursts in the direction of the jet and orphan-radio afterglows extending over wider angles on timescales of weeks. Based on the derived light curves, we suggest an optimized survey at 1.4 GHz with five epochs separated by a logarithmic time interval. We estimate the detectability of the radio counterparts of simulated GW-merger events to be detected by advanced LIGO and Virgo by current and future radio facilities. The detectable distances for these GW merger events could be as high as 1 Gpc. Around 20%–60% of the long-lasting radio remnants will be detectable in the case of the moderate kinetic energy of 3⋅10{sup 50} erg and a circum-merger density of 0.1 cm{sup −3} or larger, while 5%–20% of the orphan-radio afterglows with kinetic energy of 10{sup 48} erg will be detectable. The detection likelihood increases if one focuses on the well-localizable GW events. We discuss the background noise due to radio fluxes of host galaxies and false positives arising from extragalactic radio transients and variable active galactic nuclei, and we show that the quiet radio transient sky is of great advantage when searching for the radio counterparts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

On the Ubiquity and Stellar Luminosity Dependence of Exocometary CO Gas: Detection around M Dwarf TWA 7

Millimeter observations of CO gas in planetesimal belts show a high detection rate around A stars, but few detections for later type stars. We present the first CO detection in a planetesimal belt around an M star, TWA 7. The optically thin CO (J = 3–2) emission is colocated with previously identified dust emission from the belt, and the emission velocity structure is consistent with Keplerian rotation around the central star. The detected CO is not well shielded against photodissociation, and must thus be continuously replenished by gas release from exocomets within the belt. We analyze in detail the process of exocometary gas release and destruction around young M dwarfs and how this process compares to earlier type stars. Taking these differences into account, we find that CO generation through exocometary gas release naturally explains the increasing CO detection rates with stellar luminosity, mostly because the CO production rate from the collisional cascade is directly proportional to stellar luminosity. More luminous stars will therefore on average host more massive (and hence more easily detectable) exocometary CO disks, leading to the higher detection rates observed. The current CO detection rates are consistent with a ubiquitous release of exocometary gas in planetesimal belts, independent of spectral type.

79 ASTRONOMY AND ASTROPHYSICS↗