BB-CVXOPT: Basic Block Execution Count Estimation and Extrapolation using Constrained Convex Optimization
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Basic Block, Convex Optimization, Performance Pred↗
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Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations
Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data
Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.
54 ENVIRONMENTAL SCIENCES↗
Special Nuclear Material Mass Estimates from Neutron Singles Count Rate [Poster]
The objective of this research was to create an algorithm to provide an estimate of special nuclear material (SNM) mass using only neutron count rate data from a Radioisotope Identification Device (RIID), rather than using time-correlated data from a neutron multiplicity counter. To meet this objective neutron count rate measurements of a 252 Cf source were taken at varying distances with an ORTEC Detective X, FLIR Identifinder 2, and an ORTEC RADEAGLET-R. An algorithm was created to estimate mass of SNM utilizing the singles rate equation and the measured absolute efficiency curves.
FLIR↗
A spatially regularized detector for emergent/re-emergent disease outbreaks
Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.
59 BASIC BIOLOGICAL SCIENCES↗
Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting
Many existing object counting methods rely on density map estimation (DME) of the discrete grid representation by decoding extracted image semantic features from designed convolutional neural networks (CNNs). Relying on discrete density maps not only leads to information loss dependent on the original image resolution, but also has a scalability issue when analyzing high-resolution images with cubically increasing memory complexity. Furthermore, none of the existing methods can offer reliable uncertainty quantification (UQ) for the derived count estimates. To overcome these limitations, we design UNcertainty-aware, hypernetwork-based Implicit neural representations for Counting (UNIC) to assign probabilities and the corresponding counting confidence over continuous spatial coordinates. We derive a sampling-based Bayesian counting loss function and develop the corresponding model training algorithm. UNIC outperforms existing methods on the Remote Sensing Object Counting (RSOC) dataset with reliable UQ and improved interpretability of the derived count estimates. Our code is available at https://github.com/SiyuanXu-tamu/UNIC.
97 MATHEMATICS AND COMPUTING↗
Characterizing Shielded Special Nuclear Material by Neutron Capture Gamma-Ray Multiplicity Counting
We present a new neutron multiplicity counting analysis and measurement method for neutron-shielded fissile material using neutron-capture gamma rays. Neutrons absorbed in shielding produce characteristic gamma rays that preserve the otherwise lost neutron multiplicity signature. Neutron multiplicity counting provides estimates of fission parameters, such as neutron leakage multiplication, spontaneous fissioner (e.g. Pu-240) mass, and (α,n) ratio. Standard neutron multiplicity counting can incorporate the new neutron-capture gamma-ray multiplicity counting technique to characterize previously degenerate or intractable source configurations by maximizing the multiplicity signature. The new method decouples neutron source-detector interferences, such as reflection and thermalization time in the detector, that could improve measurements of the mean neutron lifetime. We also develop a detector prototype for the multiplicity counting of neutron-capture gamma rays and present detector design considerations, such as detection material and shielding, to optimize the detection of the 2.2 MeV hydrogen capture gamma ray. We simulate the prototype neutron- capture gamma-ray multiplicity counter against the BeRP ball in polyethylene shells to inform future measurements.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Illuminating otoliths: New insights for life history of Balistes triggerfishes
Our understanding of fish life-history strategies is informed by key biological processes, such as growth, survival/mortality, recruitment and sexual maturation, used to characterize fish stocks (populations). Characterizing the life-history traits of fish populations requires the application of accurate age estimation for managed species. Grey triggerfish Balistes capriscus and queen triggerfish Balistes vetula are important reef-associated species for commercial and recreational fisheries in the Atlantic Ocean. Both species exhibit a unique reproductive strategy for large-bodied fisheries-targeted reef fishes in that they are nesting benthic spawners and invest substantial energy in defence and care of their benthic nests and fertilized eggs. Until recently, our understanding of the life-history strategies of triggerfishes assumed the main method used to obtain age estimates, increments counted from thin sections of the first dorsal spine, provided an accurate characterization of population age-based parameters. However, results from bomb radiocarbon validation studies on the two Balistes species demonstrated that spines do not provide accurate ages, but sagittal otoliths do. The main goal of the current study was to provide an updated understanding for triggerfish life-history strategies by using otolith-based age estimates to characterize population age structure and growth for grey triggerfish and queen triggerfish from waters of the south-eastern U.S. Atlantic. The current study is the first to report on sex-specific age and growth information for grey triggerfish using the Δ 14 C-validated otolith-based age estimation method and the results indicate that the previous characterization of Balistes species as exhibiting moderately rapid growth and as relatively short-lived, based on spine-derived age estimates, are flawed. Otolith-based ages indicated that grey triggerfish and queen triggerfish are moderately slow-growing and long-lived species, attaining maximum ages of 21 and 40 years, respectively. Management efforts for triggerfishes should evaluate these new insights and incorporate the results of otolith-based age estimation into future population monitoring efforts.
59 BASIC BIOLOGICAL SCIENCES↗
Using X-ray spectrum imaging to quantify fission gas in high-burnup UO2 fuel
Analysis of fission gas bubbles (FGBs) in light-water reactor (LWR) fuel is needed to improve the understanding and predictive capability of fuel evolution under normal- and transient conditions. One of the most important parameters of a FGB is the pressure of the gas, primarily Xe, inside it. Bubble volume and location (inter- vs. intragranular) are important considerations as well. However, experimental analysis of such bubbles is challenging due to their small size and embedded nature, and usefulness of the data requires large numbers be analyzed. This paper proposes a method to measure the pressure of Xe bubbles using X-ray spectrum imaging (XSI) in scanning transmission electron microscopy (STEM). From X-ray generation and instrumental parameters, the number of Xe atoms yielding a given number of Xe L-series X-ray counts is estimated, and then from the estimated volume of the bubble the Xe density is estimated, which can then be converted to pressure via an equation-of-state. We apply the method to XSI datasets from high burnup (HBu) fuel from North Anna 1 reactor in as-irradiated and post-LOCA-test conditions and find Xe pressures in the FGBs clustered around 1 GPa.
Parish, Chad [ORNL] (ORCID:0000000312097439)↗
Counting dots or counting reads? Complementary approaches to estimate virus-to-microbe ratios
Not Available
Roux, Simon (ORCID:0000000258315895)↗
Observation of the antimatter hypernucleus $^4_{\bar{\Lambda}}\overline{\hbox{H}}$
Matter-antimatter asymmetry is a research topic of fundamental interest, as it is the basis for the existence of the matter world, which survived annihilation with antimatter in the early Universe. High energy nuclear collisions create conditions similar to the Universe microseconds after the Big Bang, with comparable amounts of matter and antimatter. Much of the antimatter created escapes the rapidly expanding fireball without annihilation, making such collisions an effective experimental tool to create heavy antimatter nuclear objects and study their properties. In this paper, we report the first observation of the antimatter hypernucleus $^4_{\bar{\Lambda}}\overline{\hbox{H}}$, composed of an $\bar{\Lambda}$, an antiproton and two antineutrons. The discovery was made through its two-body decay after production in ultrarelativistic heavy ion collisions by the STAR experiment at the Relativistic Heavy Ion Collider. In total, 15.6 candidate $^4_{\bar{\Lambda}}\overline{\hbox{H}}$ antimatter hypernuclei are obtained with an estimated background count of 6.4. Lifetimes of the antihypernuclei $^3_{\bar{\Lambda}}\overline{\hbox{H}}$ and $^4_{\bar{\Lambda}}\overline{\hbox{H}}$ are measured and compared with lifetimes of their corresponding hypernuclei, testing the symmetry between matter and antimatter. Various production yield ratios among (anti)hypernuclei and (anti)nuclei are also measured and compared with theoretical model predictions, shedding light on their production mechanism.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Collision Tracking in OpenMC: Methods and Applications in Neutron Noise, Neutron Imaging, Time-of-Flight, and Multiplicity Counting
We present the development and application of a collision tracking feature within the OpenMC Monte Carlo particle transport code, designed for diverse applications such as neutron spectroscopy, scatter camera system, neutron noise, and multiplicity counting simulations. This feature enables the tracking of individual particle collisions, with potential applications in nuclear nonproliferation, reactor physics, and nuclear security. Additionally, the feature holds potential for the calibration of neutron detectors, specifically in converting light output into energy deposited within the detectors. The implementation consists of a set of filters—such as reaction type, energy, cell, and material—that constrain the set of collisions that are tracked, extensions to the Python API to enable simple input specification, and support for writing either OpenMC’s native HDF5-based format or the Monte Carlo particle list format. This feature was added to the official OpenMC release in version 0.15.3. In this work, the feature will be applied to showcase scenarios such as time-of-flight simulations, scatter-camera imaging for neutron source localization, neutron-noise analysis to extract integral kinetic parameters such as the prompt decay constant α, and multiplicity counting to estimate the mass of special nuclear materials. Ultimately, this feature aims to expand the application scope of open-source Monte Carlo particle transport codes such as OpenMC.
Monte Carlo code↗
encore : an O ( N g2) estimator for galaxy N -point correlation functions
ABSTRACT We present a new algorithm for efficiently computing the N-point correlation functions (NPCFs) of a 3D density field for arbitrary N. This can be applied both to a discrete spectroscopic galaxy survey and a continuous field. By expanding the statistics in a separable basis of isotropic functions built from spherical harmonics, the NPCFs can be estimated by counting pairs of particles in space, leading to an algorithm with complexity $\mathcal {O}(N_\mathrm{g}^2)$ for Ng particles, or $\mathcal {O}(N_\mathrm{FFT}\log N_\mathrm{FFT})$ when using a Fast Fourier Transform with NFFT grid-points. In practice, the rate-limiting step for N > 3 will often be the summation of the histogrammed spherical harmonic coefficients, particularly if the number of radial and angular bins is large. In this case, the algorithm scales linearly with Ng. The approach is implemented in the encore code, which can compute the 3PCF, 4PCF, 5PCF, and 6PCF of a BOSS-like galaxy survey in ${\sim}100$ CPU-hours, including the corrections necessary for non-uniform survey geometries. We discuss the implementation in depth, along with its GPU acceleration, and provide practical demonstration on realistic galaxy catalogues. Our approach can be straightforwardly applied to current and future data sets to unlock the potential of constraining cosmology from the higher point functions.
79 ASTRONOMY AND ASTROPHYSICS↗
The 3D Lyman- α forest power spectrum from eBOSS DR16
We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-α (Ly α) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of ~205 000 quasar spectra in the redshift range 2 ≤ z ≤ 4 at an effective redshift z = 2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp( i k ∙ r), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly α skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly α skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range $(0.02 ≤ k ≤ 0.35)$ h Mpc -1 at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly α forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.
79 ASTRONOMY AND ASTROPHYSICS↗
Massive Early-type Galaxies in the HSC-SSP: Flux Fraction of Tidal Features and Merger Rates
Here we present a statistical study on tidal features around massive early-type galaxies (ETGs). Utilizing the imaging data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), we measure the flux fraction of tidal features (f tidal ) in 2649 ETGs with stellar mass M * > 10 11 M ⊙ and redshift 0.05 < z < 0.15 using automated techniques. The Wide layer of HSC-SSP reaches a depth of ~28.5 mag arcsec –2 in the i band. Under this surface brightness limit, we find that about 28% of these galaxies harbor prominent tidal features with f tidal > 1%, among which the number of ETGs decreases exponentially with f tidal , with a logarithmic slope of ~100. Within the stellar mass range we probe, we note that f tidal increases by a factor of 2 from M * ≈ 10 11 to 10 12 M ⊙ . We also perform a pair count to estimate the merger rate of these massive ETGs. Combining the merger rates with f tidal , we estimate that the typical lifetime of tidal features is ~3 Gyr, consistent with previous studies.
79 ASTRONOMY AND ASTROPHYSICS↗
Study of Classifiers for U-235 Source Signatures Using Gamma Spectral Measurements
Signatures associated with low-level U-235 sources are studied from a classification analytics perspective, using NaI gamma-ray spectral measurements from detectors located at various distances from the source. Data sets collected at a shielded facility are utilized, wherein the source is introduced via a conduit into a formation of 21 NaI detectors deployed over 6 x 6 meters area in a formation of two concentric circles and one spiral. The activity levels in the spectral regions associated with potential U-235 signatures are estimated as counts at 1 second intervals, and are used as features to train classifiers for detecting the presence of the source. Eight different classifiers are trained and tested using the background and source measurements collected over multiple experimental runs. As expected, the classifier performance improved overall as measurements from the detectors closer to source are used, but also revealed unexpectedly low performance by two detectors that are identically produced and configured as others. Six of eight classifiers have an overall comparable performance, for example, three of them achieved zero training error and 99% detection at 4% false alarm rate for a detector located 1.3 meters away from the source. Also, larger training sets led to improved classification performance across all classifiers, and interestingly, the classifiers with the minimum training error did not necessarily achieve the highest classification performance on test data.
Rao, Nageswara↗
Improving Effective Mass Estimations in Pu-Metal Annuli
Determining the effective mass of 240 Pu in a plutonium metal item can be achieved through a number of destructive and non-destructive assay techniques. However, these techniques have one or more shortcomings. These include the need for large quantities of plutonium, long measurement time, or lack of sufficient accuracy. While efforts have been made to mitigate these issues by estimating 240 Pu quantities through neutron coincidence counting techniques, these estimates are subject to systematic bias, and their estimates are not well characterized when other factors of the annulus’ physical form and composition are accounted for. In this work, we expand upon these non-destructive assay techniques via the implementation of random forest machine learning models, which produce correction functions that augment and improve the effective mass estimates derived from classical leakage multiplication, singles, doubles, and triples multiplicity counting equations.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Continuum radiated power density $(P^{\textrm{C}}_{\textrm{rad}})$ and effective charge $(Z_{\textrm{eff}})$ estimates from multi-energy photon-counting measurements
Multi-energy soft x-ray pinhole cameras have been designed, built, calibrated, and operated at Madison symmetric Torus, Alcator C-Mod, and more recently at Tokamak a configuration variable and Tungsten Environment in Steady-State Tokamak (WEST), to measure plasma emission across multiple energy ranges. Here we describe a new methodology to estimate the local continuum radiated power density and the plasma effective charge (Z eff ) directly from photon-counting measurements of the line-free continuum emission (Bremsstrahlung and Radiative Recombination) in several energy bands between 11 and 18 keV. This capability is particularly valuable for confinement systems using metal plasma-facing components, where x-ray losses from interactions with the sputtered wall can represent a significant fraction of the total radiated power (P rad ). The approach leverages a well-characterized detector responsivity, modeled by a complementary error function, and interprets the differential multi-energy measurements between adjacent energy levels through the probability density function of a Gaussian distribution. The implementation of this diagnostic technique is currently under development on the WEST tokamak, aiming at the goal of providing real-time P rad and Z eff measurements during long-pulse operation (up to 1000 s) in the 2026 campaign.
WEST↗