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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs." We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times 6$ km 2 area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. Here, we show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this article, we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Characterizing gravitational wave detector networks: from A # to cosmic explorer

Gravitational-wave observations by the laser interferometer gravitational-wave observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A # , given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped cosmic explorer (CE) observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein telescope with 10 km arms in Europe. We use a set of science metrics derived from the top priorities of several funding agencies to characterize the science capabilities of different networks. The presence of one or two A # observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy (MMA) and precision measurement of the Hubble parameter. Two CE observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming MMA. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two CE observatories is critical for accomplishing all the identified science metrics including the nuclear equation of state, cosmological parameters, the growth of black holes through cosmic history, but also make new discoveries such as the presence of dark matter within or around neutron stars and black holes, continuous gravitational waves from rotating neutron stars, transient signals from supernovae, and the production of stellar-mass black holes in the early Universe. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A # observatories.

Einstein telescope↗
DOE OSTI2024

Measuring neutron star radius with second and third generation gravitational wave detector networks

The next generation of ground-based interferometric gravitational wave detectors will observe mergers of black holes and neutron stars throughout cosmic time. A large number of the binary neutron star merger events will be observed with extreme high fidelity, and will provide stringent constraints on the equation of state of nuclear matter. In this paper, we investigate the systematic improvement in the measurability of the equation of state with increase in detector sensitivity by combining constraints obtained on the radius of a 1.4 M ⊙ neutron star from a simulated source population. Since the measurability of the equation of state depends on its stiffness, we consider a range of realistic equations of state that span the current observational constraints. We show that a single 40 km Cosmic Explorer detector can pin down the neutron star radius for a soft, medium and stiff equation of state with a precision of 10 m within a decade, whereas the current generation of ground-based detectors like the Advanced LIGO-Virgo network would take $\mathcal{O}(10_5)$ years to do so for a soft equation of state.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Utilization of the Critic Subnetwork of a Generative Adversarial Network as Detector of Morphological Material Change in Image Data

The resolution of computed tomography (CT) has become high enough to monitor morphological changes due to aging in materials in long-term applications. For this work, we explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect; but both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing gravity and growth of structure with gravitational waves and galaxies’ peculiar velocity

The low-redshift velocity field is a unique probe of the growth of cosmic structure and gravity. Here, we propose to use distances from gravitational wave (GW) detections, in conjunction with the redshifts of their host galaxies from wide field spectroscopic surveys (e.g., DESI, 4MOST, TAIPAN), to measure peculiar motions within the local Universe. Such measurement has the potential to constrain the growth rate f σ 8 and test gravity through determination of the gravitational growth index γ , complementing constraints from other peculiar velocity measurements. We find that binary neutron star mergers with associated counterpart at z ≲ 0.2 that will be detected by the Einstein Telescope (ET) will be able to constrain f σ 8 to ~ 3 % precision after 10 years of operations when combined with galaxy overdensities from DESI and TAIPAN. If a larger network of third generation GW detectors is available (e.g., including the Cosmic Explorer), the same constraints can be reached over a shorter timescale ( ~ 5 years for a 3 detectors network). The same events (plus information from their hosts’ redshifts) can constrain γ to σ γ ≲ 0.04 . This constraint is precise enough to discern general relativity from other popular gravity models at 3 σ . This constraint is improved to σ γ ~ 0.02 – 0.03 when combined with galaxy overdensities. The potential of combining galaxies’ peculiar velocities with gravitational wave detections for cosmology highlights the need for extensive optical to near-infrared follow-up of nearby gravitational wave events, or exquisite GW localization, in the next decade.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intra- hour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. Here, the framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% - 30% compared with state-of-the-art methods.

42 ENGINEERING↗
DOE OSTI2022

Coincidence measurements of radioxenon using passive implemented planar silicon (PIPS) detector

The Comprehensive Test Ban Treaty (CTBT) bans nuclear weapons testing and establishes the International Monitoring System (IMS) for the purpose of treaty verification. IMS is a detector network that confirms that global nuclear activity is peaceful in nature. These detector systems use β-γcoincidence measurements to characterize atmospheric samples containing radioxenon isotopes. The goal of this work is to use the system consisting of two sodium iodide detectors and two PIPSbox detectors to characterize isotopically pure samples of 131m Xe, 133m Xe, 133 Xe, and 135 Xe via β, γ, and β-γ coincidence measurements.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2005

Creation and testing of an artificial neural network based carbonate detector for Mars rovers

We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

neural networks↗
DOE OSTI2022

Comparison of measurement and simulation of ATLAS cavern radiation background

Sixteen Medipix2 pixel detector based (MPX) devices were operated at various positions within the ATLAS detector and cavern continuously from early 2008 up to 2013. In addition to photons, each MPX detector is capable to detect charged particles, and neutrons as it is covered with a mask of converter materials dividing its area into regions sensitive to thermal or fast neutrons. The MPX detector network was effectively used for real-time measurements of the spectral characteristics and composition of complex radiation fields in ATLAS. This article reports comparison of the results of measurements performed with MPX detectors during the LHC operation period in 2010 and 2011 with Monte Carlo simulations results from the FLUGG and GCALOR codes. For the purpose of this comparison, the MPX detectors were operated in tracking mode with low threshold (8–10 keV) allowing one to distinguish among particle categories based on the recognition of track patterns left by the particles in the MPX sensitive layer. The comparison of measurements with simulations shows that the agreement between measured and simulated data is satisfactory in most cases within a factor of two.

47 OTHER INSTRUMENTATION↗
NASA NTRS2020

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

We report the observation of a compact binary coalescence involving a 22.2–24.3 Me black hole and a compact object with a mass of 2.50–2.67Me (all measurements quoted at the 90% credible level). The gravitational-wavesignal, GW190814, was observed during LIGO’s and Virgo’s third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network. The source was localized to18.5 deg2 at a distance of-+2414541Mpc; no electromagnetic counterpart has been confirmed to date. The source has the most unequal mass ratio yet measured with gravitational waves,-+0.1120.0090.008, and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system. The dimensionless spin of the primary black hole is tightly constrained to 0.07. Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence. We estimate a merger rate density of 1–23 Gpc−3yr−1 for the new class of binary coalescence sources that GW190814 represents. Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters. However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries.

Gravitational wave astronomy↗
DOE OSTI2023

Constraining properties of asymmetric dark matter candidates from gravitational-wave observations

The accumulation of certain types of dark matter particles in neutron star cores due to accretion over long timescales can lead to the formation of a mini black hole. In this scenario, the neutron star is destabilized and implodes to form a black hole without significantly increasing its mass. When this process occurs in neutron stars in coalescing binaries, one or both stars might be converted to a black hole before they merge. Thus, in the mass range of ~1–2M ⊙ , the Universe might contain three distinct populations of compact binaries: one containing only neutron stars, the second population of only black holes, and a third, mixed population consisting of a neutron star and a black hole. However, it is unlikely to have a mixed population as the various timescales allow for both neutron stars to remain or collapse within a short timescale. In this paper, we explore the capability of future gravitational-wave detector networks, including upgrades of Advanced LIGO and Virgo, and new facilities such as the Cosmic Explorer and Einstein Telescope (XG network), to discriminate between different populations by measuring the effective tidal deformability of the binary, which is zero for binary black holes but nonzero for binary neutron stars. Furthermore, we show that observing the relative abundances of the different populations can be used to infer the timescale for neutron stars to implode into black holes, and in turn, provide constraints on the particle nature of dark matter. Furthermore, the XG network will infer the implosion timescale to within an accuracy of 0.01 Gyr at 90% credible interval and determine the dark matter mass and interaction cross section to within a factor of 2 GeV and 10 cm –2 , respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Impact of dark matter on tidal signatures in neutron star mergers with the Einstein Telescope

If dark matter (DM) accumulates inside neutron stars (NS), it changes their internal structure and causes a shift of the tidal deformability from the value predicted by the dense-matter equation of state (EOS). In principle, this shift could be observable in the gravitational-wave (GW) signal of binary neutron star (BNS) mergers. We investigate the effect of fermionic, noninteracting DM when observing a large number of GW events from DM-admixed BNSs with the precision of the proposed Einstein telescope (ET). Specifically, we study the impact on the recovery of the baryonic EOS and whether DM properties can be constrained. For this purpose, we create event catalogs of BNS mock events with DM fraction up to 1%, from which we reconstruct the posterior uncertainties with the Fisher matrix approach. Using this data, we perform joint Bayesian inference on the baryonic EOS, DM particle mass, and DM particle fraction in each event. Here, our results reveal that when falsely ignoring DM effects, the EOS posterior is biased toward softer EOSs, though the offset is rather small. Further, we find that within our assumptions of our DM model and population, ET will likely not be able to test the presence of DM in BNSs, even when combining many events and adding Cosmic Explorer (CE) to the next-generation detector network. Likewise, the potential constraints on the DM particle mass will remain weak because of degeneracies with the fraction and EOS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M ⊙

On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 8 5 - 14 + 21 M ⊙ and 6 6 - 18 + 17 M ⊙ (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M ⊙ . We calculate the mass of the remnant to be 14 2 - 16 + 28 M ⊙ , which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3 - 2.6 + 2.4 Gpc , corresponding to a redshift of 0.82 - 0.34 + 0.28 . The inferred rate of mergers similar to GW190521 is 0.13 - 0.11 + 0.30 Gpc - 3 yr - 1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors

This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial I ntelligence Applications t o Autonomous Cybersecurity (AI ATAC) prize challenges, funded by / completed in service of the US Navy. The experiment employed 100K files (50/50% benign/malicious) with a stratified distribution of file types, including ~1K zero-day program executables (increasing experiment size two orders of magnitude over previous work). We present an evaluation process of delivering a file to a fresh virtual machine donning the detection technology, waiting 90s to allow static detection, then executing the file and waiting another period for dynamic detection; this allows greater fidelity in the observational data than previous experiments, in particular, resource and time-to-detection statistics. To execute all 800K trials (100K files × 8 tools), a software framework is designed to choreograph the experiment into an automated, time-synced, and reproducible workflow with substantial parallelization. Software with base classes for this framework are provided. A cost-benefit model was configured to integrate the tools’ detection statistics into a comparable quantity by simulating costs of use. This provides a ranking methodology for cyber competitions and a lens for reasoning about the varied statistical results. The results provide insights on state of commercial malware detection.

Bridges, Robert↗