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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Supernova relic neutrinos and the supernova rate problem: Analysis of uncertainties and detectability of ONeMg and failed supernovae

Direct measurements of the core collapse supernova rate (R{sub SN}) in the redshift range 0 ≤ z ≤ 1 appear to be about a factor of two smaller than the rate inferred from the measured cosmic massive star formation rate (SFR). This discrepancy would imply that about one-half of the massive stars that have been born in the local observed comoving volume did not explode as luminous supernovae. In this work, we explore the possibility that one could clarify the source of this 'supernova rate problem' by detecting the energy spectrum of supernova relic neutrinos with a next generation 10{sup 6} ton water Čerenkov detector like Hyper-Kamiokande. First, we re-examine the supernova rate problem. We make a conservative alternative compilation of the measured SFR data over the redshift range 0 ≤z ≤ 7. We show that by only including published SFR data for which the dust obscuration has been directly determined, the ratio of the observed massive SFR to the observed supernova rate R{sub SN} has large uncertainties ∼1.8{sub −0.6}{sup +1.6} and is statistically consistent with no supernova rate problem. If we further consider that a significant fraction of massive stars will end their lives as faint ONeMg SNe or as failed SNe leading to a black hole remnant, then the ratio reduces to ∼1.1{sub −0.4}{sup +1.0} and the rate problem is essentially solved. We next examine the prospects for detecting this solution to the supernova rate problem. We first study the sources of uncertainty involved in the theoretical estimates of the neutrino detection rate and analyze whether the spectrum of relic neutrinos can be used to independently identify the existence of a supernova rate problem and its source. We consider an ensemble of published and unpublished core collapse supernova simulation models to estimate the uncertainties in the anticipated neutrino luminosities and temperatures. We illustrate how the spectrum of detector events might be used to establish the average neutrino temperature and constrain SN models. We also consider supernova ν-process nucleosynthesis to deduce constraints on the temperature of the various neutrino flavors. We study the effects of neutrino oscillations on the detected neutrino energy spectrum and also show that one might distinguish the equation of state (EoS) as well as the cause of the possible missing luminous supernovae from the detection of supernova relic neutrinos. We also analyze a possible enhanced contribution from failed supernovae leading to a black hole remnant as a solution to the supernova rate problem. We conclude that indeed it might be possible (though difficult) to measure the neutrino temperature, neutrino oscillations, and the EoS and confirm this source of missing luminous supernovae by the detection of the spectrum of relic neutrinos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Extragalactic synchrotron transients in the era of wide-field radio surveys. I. Detection rates and light curve characteristics

The impending era of wide-field radio surveys has the potential to revolutionize our understanding of astrophysical transients. Here we evaluate the prospects of a wide range of planned and hypothetical radio surveys using the properties and volumetric rates of known and hypothetical classes of extragalactic synchrotron radio transients (e.g., on-axis and off-axis gamma-ray bursts (GRBs), supernovae, tidal disruption events, compact object mergers). Utilizing these sources and physically motivated considerations we assess the allowed phase space of radio luminosity and peak timescale for extragalactic transients. We also include for the first time effects such as redshift evolution of the rates, K-corrections, and non-Euclidean luminosity distance, which affect the detection rates of the most sensitive surveys. The number of detected events is calculated by means of a Monte Carlo method, using the various survey properties (depth, cadence, area) and realistic detection criteria that include a cut on the minimum variability of the transients during the survey and an assessment of host galaxy contamination. We find that near-term GHz frequency surveys (ASKAP/VAST, Very Large Array Sky Survey) will detect few events: ≲30−50 on- and off-axis long GRBs (LGRBs) and off-axis tidal disruption events, and ∼50−100 neutron star binary mergers if ∼0.5% of the mergers result in a stable millisecond magnetar. Low-frequency surveys (e.g., LOFAR) are unlikely to detect any transients, while a hypothetical large-scale mm survey may detect ∼40 on-axis LGRBs. On the other hand, we find that SKA1 surveys at ∼0.1−1 GHz have the potential to uncover thousands of transients, mainly on-axis and off-axis LGRBs, on-axis short GRBs, off-axis TDEs, and neutron star binary mergers with magnetar remnants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A COMPREHENSIVE STUDY OF DETECTABILITY AND CONTAMINATION IN DEEP RAPID OPTICAL SEARCHES FOR GRAVITATIONAL WAVE COUNTERPARTS

The first direct detection of gravitational waves (GWs) by the ground-based Advanced LIGO/Virgo interferometers is expected to occur within the next few years. These interferometers are designed to detect the mergers of compact object binaries composed of neutron stars and/or black holes to a fiducial distance of ∼200 Mpc and a localization region of ∼100 deg{sup 2}. To maximize the science gains from such GW detections it is essential to identify electromagnetic counterparts. Among the wide range of proposed counterparts, the most promising is optical/IR emission powered by the radioactive decay of r-process elements synthesized in the neutron-rich merger ejecta—a “kilonova.” Here we present detailed simulated observations that encompass a range of strategies for kilonova searches during GW follow-up. We utilize these simulations to assess both the detectability of kilonovae and our ability to distinguish them from a wide range of contaminating transients in the large GW localization regions. We find that if pre-existing deep template images for the GW localization region are available, then nightly observations to a depth of i ≈ 24 mag and z ≈ 23 mag are required to achieve a 95% detection rate; observations that commence within ∼12 hr of trigger will also capture the kilonova peak and provide stronger constraints on the ejecta properties. We also find that kilonovae can be robustly separated from other known and hypothetical types of transients utilizing cuts on color (i − z ≳ 0.3 mag) and rise time (t{sub rise} ≲ 4 days). In the absence of a pre-existing template the observations must reach ∼1 mag deeper to achieve the same kilonova detection rate, but robust rejection of contaminants can still be achieved. Motivated by the results of our simulations we discuss the expected performance of current and future wide-field telescopes in achieving these observational goals, and find that prior to LSST the Dark Energy Camera on the Blanco 4 m telescope and Hyper Suprime-Cam on the Subaru 8 m telescope offer the best potential for kilonova discovery.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

(In)direct detection of boosted dark matter

We initiate the study of novel thermal dark matter (DM) scenarios where present-day annihilation of DM in the galactic center produces boosted stable particles in the dark sector. These stable particles are typically a subdominant DM component, but because they are produced with a large Lorentz boost in this process, they can be detected in large volume terrestrial experiments via neutral-current-like interactions with electrons or nuclei. This novel DM signal thus combines the production mechanism associated with indirect detection experiments (i.e. galactic DM annihilation) with the detection mechanism associated with direct detection experiments (i.e. DM scattering off terrestrial targets). Such processes are generically present in multi-component DM scenarios or those with non-minimal DM stabilization symmetries. As a proof of concept, we present a model of two-component thermal relic DM, where the dominant heavy DM species has no tree-level interactions with the standard model and thus largely evades direct and indirect DM bounds. Instead, its thermal relic abundance is set by annihilation into a subdominant lighter DM species, and the latter can be detected in the boosted channel via the same annihilation process occurring today. Especially for dark sector masses in the 10 MeV–10 GeV range, the most promising signals are electron scattering events pointing toward the galactic center. These can be detected in experiments designed for neutrino physics or proton decay, in particular Super-K and its upgrade Hyper-K, as well as the PINGU/MICA extensions of IceCube. This boosted DM phenomenon highlights the distinctive signatures possible from non-minimal dark sectors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

PROSPECTS FOR THE DETECTION OF FAST RADIO BURSTS WITH THE MURCHISON WIDEFIELD ARRAY

Fast radio bursts (FRBs) are short timescale (<<1 s) astrophysical radio signals, presumed to be a signature of cataclysmic events of extragalactic origin. The discovery of six high-redshift events at ∼1400 MHz from the Parkes radio telescope suggests that FRBs may occur at a high rate across the sky. The Murchison Widefield Array (MWA) operates at low radio frequencies (80-300 MHz) and is expected to detect FRBs due to its large collecting area (∼2500 m{sup 2}) and wide field-of-view (FOV, ∼ 1000 deg{sup 2} at ν = 200 MHz). We compute the expected number of FRB detections for the MWA assuming a source population consistent with the reported detections. Our formalism properly accounts for the frequency-dependence of the antenna primary beam, the MWA system temperature, and unknown spectral index of the source population, for three modes of FRB detection: coherent; incoherent; and fast imaging. We find that the MWA's sensitivity and large FOV combine to provide the expectation of multiple detectable events per week in all modes, potentially making it an excellent high time resolution science instrument. Deviations of the expected number of detections from actual results will provide a strong constraint on the assumptions made for the underlying source population and intervening plasma distribution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

PROSPECTS FOR DETECTING ASTEROSEISMIC BINARIES IN KEPLER DATA

Asteroseismology may in principle be used to detect unresolved stellar binary systems comprised of solar-type stars and/or red giants. This novel method relies on the detection of the presence of two solar-like oscillation spectra in the frequency spectrum of a single light curve. Here, we make predictions of the numbers of systems that may be detectable in data already collected by the NASA Kepler Mission. Our predictions, which are based upon TRILEGAL and BiSEPS simulations of the Kepler field of view, indicate that as many as 200 or more ''asteroseismic binaries'' may be detectable in this manner. Most of these binaries should be comprised of two He-core-burning red giants. Owing largely to the limited numbers of targets with the requisite short-cadence Kepler data, we expect only a small number of detected binaries containing solar-type stars. The predicted yield of detections is sensitive to the assumed initial mass ratio distribution (IMRD) of the binary components and therefore represents a sensitive calibration of the much debated IMRD near mass ratio unity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.

42 ENGINEERING↗
DOE OSTI2018

Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies

Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have realworld significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have high real-world utility, while ignoring the low-utility statistically anomalous instances. To this end, we propose a novel method called Latent Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution. This method uses the EM algorithm to simultaneously incorporate the Geometric Entropy Minimization principle for identifying statistical anomalies, and the Maximum Entropy Discrimination principle to incorporate utility labels, in order to detect highutility anomalies. Here, we apply our method in both simulated and real datasets to demonstrate that it has superior performance over existing alternatives that independently pre-process with unsupervised anomaly detection algorithms before classifying.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Initial study of Schroedinger eigenmaps for spectral target detection

Spectral target detection refers to the process of searching for a specific material with a known spectrum over a large area containing materials with different spectral signatures. Traditional target detection methods in hyperspectral imagery (HSI) require assuming the data fit some statistical or geometric models and based on the model, to estimate parameters for defining a hypothesis test, where one class (i.e., target class) is chosen over the other classes (i.e., background class). Nonlinear manifold learning methods such as Laplacian eigenmaps (LE) have extensively shown their potential use in HSI processing, specifically in classification or segmentation. Recently, Schroedinger eigenmaps (SE), which is built upon LE, has been introduced as a semisupervised classification method. In SE, the former Laplacian operator is replaced by the Schroedinger operator. The Schroedinger operator includes by definition, a potential term V that steers the transformation in certain directions improving the separability between classes. In this regard, we propose a methodology for target detection that is not based on the traditional schemes and that does not need the estimation of statistical or geometric parameters. This method is based on SE, where the potential term V is taken into consideration to include the prior knowledge about the target class and use it to steer the transformation in directions where the target location in the new space is known and the separability between target and background is augmented. An initial study of how SE can be used in a target detection scheme for HSI is shown here. Inscene pixel and spectral signature detection approaches are presented. Finally, the HSI data used comprise various target panels for testing simultaneous detection of multiple objects with different complexities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

TH-C-BRD-01: Analytical Computation of Prompt Gamma Ray Emission and Detection for Proton Range Verification

Purpose: A prompt gamma (PG) slit camera prototype demonstrated that on-line range monitoring within 1–2 mm could be performed by comparing expected and measured PG detection profiles. Monte Carlo (MC) can simulate the expected PG profile but this would result in prohibitive computation time for a complete pencil beam treatment plan. We implemented a much faster method that is based on analytical processing of pre-computed MC data. Methods: The formation of the PG detection signal can be separated into: 1) production of PGs and 2) detection by the camera detectors after PG transport in geometry. For proton energies from 40 to 230 MeV, PG productions in depth were pre-computed by MC (PENH) for 12C, 14N, 16O, 31P and 40Ca. The PG production was then modeled analytically by adding the PG production for each element according to local proton energy and tissue composition.PG transport in the patient/camera geometries and the detector response were modeled by convolving the PG production profile with a transfer function. The latter is interpolated from a database of transfer functions fitted to pre-computed MC data (PENELOPE). The database was generated for a photon source in a cylindrical phantom with various radiuses and a camera placed at various positions.As a benchmark, the analytical model was compared to PENH for a water phantom, a phantom with different slabs (adipose, muscle, lung) and a thoracic CT. Results: Good agreement (within 5%) was observed between the analytical model and PENH for the PG production. Similar accuracy for detecting range shifts was also observed. Speed of around 250 ms per profile was achieved (single CPU) using a non-optimized MatLab implementation. Conclusion: We devised a fast analytical model for generating PG detection profiles. In the test cases considered in this study, similar accuracy than MC was achieved for detecting range shifts. This research is supported by IBA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Computer aided detection of surgical retained foreign object for prevention

Purpose: Surgical retained foreign objects (RFOs) have significant morbidity and mortality. They are associated with approximately $1.5 × 10{sup 9} annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. The authors address the RFO problem with two complementary technologies: a three-dimensional (3D) gossypiboma micro tag, the μTag that improves the visibility of RFOs on radiographs, and a computer aided detection (CAD) system that detects the μTag. It is desirable for the CAD system to operate in a high specificity mode in the operating room (OR) and function as a first reader for the surgeon. This allows for fast point of care results and seamless workflow integration. The CAD system can also operate in a high sensitivity mode as a second reader for the radiologist to ensure the highest possible detection accuracy. Methods: The 3D geometry of the μTag produces a similar two dimensional (2D) depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. The authors created a data set of 1800 cadaver images with the 3D μTag and other common man-made surgical objects positioned randomly. A total of 1061 cadaver images contained a single μTag and the remaining 739 were without μTag. A radiologist marked the location of the μTag using an in-house developed graphical user interface. The data set was partitioned into three independent subsets: a training set, a validation set, and a test set, consisting of 540, 560, and 700 images, respectively. A CAD system with modules that included preprocessing μTag enhancement, labeling, segmentation, feature analysis, classification, and detection was developed. The CAD system was developed using the training and the validation sets. Results: On the training set, the CAD achieved 81.5% sensitivity with 0.014 false positives (FPs) per image in a high specificity mode for the surgeons in the OR and 96.1% sensitivity with 0.81 FPs per image in a high sensitivity mode for the radiologists. On the independent test set, the CAD achieved 79.5% sensitivity with 0.003 FPs per image in a high specificity mode for the surgeons and 90.2% sensitivity with 0.23 FPs per image in a high sensitivity mode for the radiologists. Conclusions: To the best of the authors’ knowledge, this is the first time a 3D μTag is used to produce a recognizable, substantially similar 2D projection on radiographs regardless of orientation in space. It is the first time a CAD system is used to search for man-made objects over anatomic background. The CAD system for the μTags achieved reasonable performance in both the high specificity and the high sensitivity modes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-H-BRC-06: A Unified Machine-Learning Based Probabilistic Model for Automated Anomaly Detection in the Treatment Plan Data

Purpose: The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information. Methods In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error-free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network. Results: We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ∼0.08 seconds. Conclusion: The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and automated plan checks. The senior author received research grants from ViewRay Inc. and Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Compiler-Assisted Detection of Transient Memory Errors

The probability of bit flips in hardware memory systems is projected to increase significantly as memory systems continue to scale in size and complexity. Effective hardware-based error detection and correction requires that the complete data path, involving all parts of the memory system, be protected with sufficient redundancy. First, this may be costly to employ on commodity computing platforms and second, even on high-end systems, protection against multi-bit errors may be lacking. Therefore, augmenting hardware error detection schemes with software techniques is of consider- able interest. In this paper, we consider software-level mechanisms to comprehensively detect transient memory faults. We develop novel compile-time algorithms to instrument application programs with checksum computation codes so as to detect memory errors. Unlike prior approaches that employ checksums on computational and architectural state, our scheme verifies every data access and works by tracking variables as they are produced and consumed. Experimental evaluation demonstrates that the proposed comprehensive error detection solution is viable as a completely software-only scheme. We also demonstrate that with limited hardware support, overheads of error detection can be further reduced.

def-use analysis↗
DOE OSTI2016

Acoustic Methods to Monitor Protein Crystallization and to Detect Protein Crystals in Suspensions of Agarose and Lipidic Cubic Phase

Improvements needed for automated crystallography include crystal detection and crystal harvesting. A technique that uses acoustic droplet ejection to harvest crystals was previously reported. Here a method is described for using the same acoustic instrument to detect protein crystals and to monitor crystal growth. Acoustic pulses were used to monitor the progress of crystallization trials and to detect the presence and location of protein crystals. Crystals were detected, and crystallization was monitored in aqueous solutions and in lipidic cubic phase. Using a commercially available acoustic instrument, crystals measuring ~150 µm or larger were readily detected. Simple laboratory techniques were used to increase the sensitivity to 50 µm by suspending the crystals away from the plastic surface of the crystallization plate. This increased the sensitivity by separating the strong signal generated by the plate bottom that can mask the signal from small protein crystals. It is possible to further boost the acoustic reflection from small crystals by reducing the wavelength of the incident sound pulse, but our current instrumentation does not allow this option. In the future, commercially available sound-emitting transducers with a characteristic frequency near 300 MHz should detect and monitor the growth of individual 3 µm crystals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Pathosphere.org: pathogen detection and characterization through a web-based, open source informatics platform

The detection of pathogens in complex sample backgrounds has been revolutionized by wide access to next-generation sequencing (NGS) platforms. However, analytical methods to support NGS platforms are not as uniformly available. Pathosphere (found at Pathosphere.org) is a cloud - based open - sourced community tool that allows for communication, collaboration and sharing of NGS analytical tools and data amongst scientists working in academia, industry and government. The architecture allows for users to upload data and run available bioinformatics pipelines without the need for onsite processing hardware or technical support. The pathogen detection capabilities hosted on Pathosphere were tested by analyzing pathogen-containing samples sequenced by NGS with both spiked human samples as well as human and zoonotic host backgrounds. Pathosphere analytical pipelines developed by Edgewood Chemical Biological Center (ECBC) identified spiked pathogens within a common sample analyzed by 454, Ion Torrent, and Illumina sequencing platforms. ECBC pipelines also correctly identified pathogens in human samples containing arenavirus in addition to animal samples containing flavivirus and coronavirus. These analytical methods were limited in the detection of sequences with limited homology to previous annotations within NCBI databases, such as parvovirus. Utilizing the pipeline-hosting adaptability of Pathosphere, the analytical suite was supplemented by analytical pipelines designed by the United States Army Medical Research Institute of Infectious Diseases and Walter Reed Army Institute of Research (USAMRIID-WRAIR). These pipelines were implemented and detected parvovirus sequence in the sample that the ECBC iterative analysis previously failed to identify. By accurately detecting pathogens in a variety of samples, this work demonstrates the utility of Pathosphere and provides a platform for utilizing, modifying and creating pipelines for a variety of NGS technologies developed to detect pathogens in complex sample backgrounds. These results serve as an exhibition for the existing pipelines and web-based interface of Pathosphere as well as the plug-in adaptability that allows for integration of newer NGS analytical software as it becomes available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Bearing Fault Detection and Wear Estimation Using Machine Learning

Unpredicted bearings failures can be costly and dangerous. Current methods have been examining the use of machine learning algorithms to automatically detect bearing faults. Vibration specialists must react daily, as the sheer number of bearings and the machines they operate are critical and any downtime will result in loss of productivity and endanger personal. The method presented in this paper utilizes a novel approach to detect and determine bearings fault diameters. The detection algorithm uses the cubic support vector machine (SVM) for high accuracy inner and outer race fault detection. After detecting the type of fault, the data is then passed through a second layer that uses a modified K-means clustering and a linear interpolation to accurately estimate the diameter of the fault that was detected.

42 ENGINEERING↗
DOE OSTI2020

Workshop Report for Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications

Artificial intelligence (AI) promises powerful new capabilities in an expansive array of applications. One area is proliferation detection, where AI can provide transformative tools to achieve objectives currently inaccessible using conventional methods. In particular, AI affords the opportunity to use new indicators and process massive amounts of heterogeneous data that can increase sensitivity to proliferant activities and push proliferation detection to the earliest possible stages. However, existing AI tools are not ready to achieve such potential, falling short in regard to some important considerations involved in proliferation detection. One crucial aspect that requires improvement is the explainability of the most powerful AI algorithms (i.e., understanding how the algorithms actually arrive at their conclusions)-the lack of which prevents widespread adoption in national security missions. The Data Science and AI portfolio within the National Nuclear Security Administration's Office of Defense Nuclear Nonproliferation Research and Development is driving the development of next-generation AI for proliferation detection through the expertise and work of the national laboratories and partners in academia. The "Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications" workshop aimed to further this objective. The meeting was held virtually on Sept. 15-16, 2020 and included more than 170 participants, primarily from the national laboratories with additional contributions from university researchers and mission partners. Each day included a keynote presentation, four technical presentations about cutting-edge research in explainability, and a panel to explore considerations in applying explainability and developing AI systems that can accelerate the transition of emerging AI technologies among partners and end users to solve critical mission questions. This report summarizes the Next-Gen AI for Proliferation Detection workshop's content and findings.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Advancing Enhanced Raman Spectroscopy to Detect and Measure NOW Pheromones (CPRB 2022 proposal)

Insect sex pheromones are chemical compounds that insects release to attract their partners over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts. These pheromone-based strategies can include monitoring, mating disruption, mass trapping, attract-and-kill and push-pull. There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can be effectively used to reduce crop damage by this pest. Just how mating disruption works is not fully established and likely varies across products and target species. For instance, the extent to which synthetic pheromones compete with natural pheromone is not well understood, or in the case of monitoring, how efficaciously the insect follows the diffusing plumes, especially across large blocks and at plot borders. Furthermore, there may be specific conditions under which poor or impeded diffusion of synthetic pheromone diminishes the disruption effect, which could result in some males effectively locating females for reproduction. At the same time, many commercially available synthetic lures are also available, and growers have been effectively using them to track population development – although the attractive range of these lures remains unclear. That is, while pheromone lures can attract many moths, the relationship between trap capture and local populations, much less crop damage, remains unclear. Regardless of the emission source (aerosol puffers, meso emitters, lures etc.), we currently lack the ability to fully understand how these synthetic pheromone compounds diffuse away from their point-source of emission – and subsequently how this might affect the efficacy of mating disruption and/or the accuracy of monitoring efforts. Raman spectroscopy (RS) may offer a means to detect synthetic pheromone because of its analytical properties and recent technological advancements. In combination with nanostructured probes, RS has a demonstrated capability to detect volatiles at extremely low concentrations. While other methods for detecting pheromones have proven to be successful, such analytical methods like mass spectrometry (MS) gas chromatography (GC) require sampling in the fields and are not real-time. Other methods such as EAG/ EAD are also invasive (use antennas) but are of course valuable. Opto-electronic, micro and nano sensors just recently enticed interest. We fall in this latter category, proposing the use of optical spectroscopy (Raman) in a fiber aided by nanostructures to provide a chemical fingerprint without transductions and in real-time (i.e. no sampling). To date, we have been able to generate Raman signatures for the main chemicals in synthetic pheromones (a first for science, to the best of our knowledge), observe their trends at different concentrations, and also provide a clear distinction of such signatures in mixed samples via Principal Component Analysis (PCA) – all of which strengthens our conclusions on the ability of RS to detect emissions in more realistic conditions. We also established processes to enhance the detection sensitivity via preconcentration with Solid Phase Micro Extraction (SPME) fibers and Surface Enhanced Raman Spectroscopy (SERS) to increase the overall optical signals. Furthermore, we explored limitations and requirements for detecting from dispensers, lures, or live moths, including Dept. of Energy and CA State regulatory approvals. Finally, we setup some of our first “orchard in a box” experiments to measure pheromone diffusion along with developing appropriate models.

42 ENGINEERING↗