Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database.
Abstract not provided.
Patel, Lekha↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Low-Radioactivity Argon for Rare-Event Searches
39Ar and 42Ar produce irreducible backgrounds for several argon-based dark matter and neutrino experiments. The use of low-radioactivity underground argon (UAr) could be a solution to the problem. The DarkSide-50 experiment demonstrated that argon derived from underground sources can contain a much lower concentration of 39Ar than atmospheric argon. Following this success, the Global Argon Dark Matter Collaboration (GADMC) is procuring hundreds of tons of UAr for the DarkSide-20k detector. To serve the broader community need, it is increasingly important to identify new sources of low-radioactivity argon. In addition, understanding the underground production mechanisms of argon radioisotopes and devising methods to measure them at ultra-low levels is necessary. The use of low-radioactivity argon could be crucial to expanding the physics goals and sensitivity of next-generation large-scale argon-based experiments.
Radioargon isotopes, Underground argon, Isotope pr↗
Accelerating Rare Events Estimation for the Safety Evaluation of Advanced Reactor Technologies
Poster for the LDRD presentation event.
97 MATHEMATICS AND COMPUTING↗
Variational Path Sampling of Rare Dynamical Events
This article reviews the concepts and methods of variational path sampling. These methods allow computational studies of rare events in systems driven arbitrarily far from equilibrium. Based upon a statistical mechanics of trajectory space and leveraging the theory of large deviations, they provide a perspective from which dynamical phenomena can be studied with the same types of ensemble reweighting ideas that have been used for static equilibrium properties. Applications to chemical, material, and biophysical systems are highlighted.
Singh, Aditya N↗
GANISP: A GAN-Assisted Importance Splitting Probability Estimator: Preprint
To reduce the variance of rare event probability estimator, genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. It is shown that a random perturbation strategy may be effective for some systems but may also fail for others, thereby preventing variance reduction in the probability estimate. To address this limitation, it is proposed to use a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).
generative adversarial network↗
GANISP: A GAN-Assisted Importance Splitting Probability Estimator
Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).
generative adversarial network↗
Progress of the CHILLAX Experiment (HEPCAT Year 1 Report)
A significant facet of modern experimental particle physics research is that of searching for hypothetical rare events. In this context, “rare” means “unusual to detect in the laboratory”, and can be a confirmed phenomenon (e.g. coherent elastic neutrino-nucleus scattering, which has a high cross section at low, difficult to detect energies), or an as-of-yet unobserved phenomenon that may not exist (e.g. dark matter - standard model matter non-gravitational interaction). A salient challenge for experimentalists is to devise detectors optimized for observing such rare events.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Learning Forecasts of Rare Stratospheric Transitions from Short Simulations
Abstract Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event’s progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.
Finkel, Justin↗
The detection of marine microseismic activity with the CUORE tonne-scale cryogenic experiment
Vibrations from experimental setups and the environment are a persistent source of noise for low-temperature calorimeters searching for rare events, including neutrinoless double beta ( 0νββ ) decay or dark matter interactions. Such noise can significantly limit experimental sensitivity to the physics case under investigation. Here, we report the detection of marine microseismic vibrations using mK-scale calorimeters. This study employs a multi-device analysis correlating data from CUORE, the leading experiment in the search for 0νββ decay with mK-scale calorimeters, and the Copernicus Earth Observation program, revealing the seasonal impact of Mediterranean Sea activity on CUORE’s energy thresholds, resolution, and sensitivity over four years. The detection of marine microseisms underscores the need to address faint environmental noise in ultra-sensitive experiments. Understanding how such noise couples to the detector and developing mitigation strategies is essential for next-generation experiments. We demonstrate one such strategy: a noise decorrelation algorithm implemented in CUORE using auxiliary sensors, which reduces vibrational noise and improves detector performance. Enhancing sensitivity to 0νββ decay and to rare events with low-energy signatures requires identifying unresolved noise sources, advancing noise reduction methods, and improving vibration suppression systems, all of which inform the design of next-generation rare event experiments.
experimental nuclear physics↗
Cosmogenic activation of silicon
In the production of 3 H, 7 Be, and 22 Na by interactions of cosmic-ray particles with silicon can produce radioactive backgrounds in detectors used to search for rare events. Through controlled irradiation of silicon CCDs and wafers with a neutron beam that mimics the cosmic-ray neutron spectrum, followed by direct counting, we determined that the production rate from cosmic-ray neutrons at sea level is (112 ± 24) atoms/( kg day ) for 3 H, (8.1 ± 1.9) atoms/( kg day ) for 7 Be, and (43.0 ± 7.2) atoms/( kg day ) for 22 Na . Complementing these results with the current best estimates of activation cross sections for cosmic-ray particles other than neutrons, we obtain a total sea-level cosmic-ray production rate of (124 ± 25) atoms/(kg day) for 3 H, (9.4 ± 2.0) atoms/( kg day ) for 7 Be, and (49.6 ± 7.4) atoms/( kg day ) for 22 Na. These measurements will help constrain background estimates and determine the maximum time that silicon-based detectors can remain unshielded during detector fabrication before cosmogenic backgrounds impact the sensitivity of next-generation rare-event searches.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations
In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.
Edge computing↗
Benchmarking machine learning strategies for phase-field problems
Abstract We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between model performance and data requirements is investigated to understand the impact on predictions and robustness of these approaches. Finally, systematic errors are analyzed to identify specific events or inadvertent rare events triggering high errors. Quantitative metrics evaluating the local and global description of the microstructure evolution, along with other scalar metrics representative of phase-field problems, are used across these four analysis areas. This benchmarking framework provides a path to evaluate the effectiveness and limitations of machine-learning strategies applied to phase-field problems, ultimately facilitating their practical application.
36 MATERIALS SCIENCE↗
GANISP (GAN-assisted Importance SPlitting)
Genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be adjusted. Typically, a random perturbation is applied to the offspring. For some cases, this cloning technique may not be adequate and prevent variance reduction in the probability estimate. A GAN-based replication process is proposed to address this limitation. The perturbations applied to the clones are physically consistent instead of being randomly chosen. The proposed method allows reducing the variance in the probability estimation.
Hassanaly, Malik↗
Computational modeling of graphite degradation in molten salt reactors: Role of infiltration
Molten salt reactors (MSRs) often employ graphite as a moderator and reflector. An important challenge for deploying graphite in these reactors is that, due to limited experimental data, our understanding of graphite’s structural integrity in molten salt environments remains incomplete. Here, this study addresses heat generation from fuel-bearing salt that has infiltrated open pores in the graphite, driven primarily by pressure differentials. This is one of multiple identified physical and chemical mechanisms through which molten salt could potentially degrade graphite. Thermally driven stresses are quantified using the Molten-Salt Reactor Experiment (MSRE) graphite moderator elements as a case study. Finite element simulations predict stress distributions at varying infiltration levels, indicating that thermal stresses increase with higher infiltration. Rare-event simulations using the parallel subset simulation framework identify the combinations and corresponding ranges of input parameters that lead to stresses above a specified threshold. In particular, combinations involving high infiltration amounts, high power density, and low thermal conductivity tend to induce the highest stresses. Under the inputs and assumptions considered in this work, the magnitudes of the thermally driven stresses are quite low, with a very low likelihood of causing failure due to exceeding the graphite’s tensile strength. Additionally, rare-event simulations were performed for two more scenarios: a scaled-up moderator geometry and a localized hotspot in the original geometry. Both cases resulted in increased susceptibility to failure, though not to a detrimental extent. Furthermore, the combined effects of irradiation and infiltration-induced thermal stresses were evaluated. The results showed that thermal stresses from infiltration were negligible compared to those caused by irradiation. The findings of such a study are inherently component-specific, but the methodology presented here could be used for similar assessments of salt-infiltration effects in other graphite components.
36 - MATERIALS SCIENCE↗
Evaluation of Light-Element Reactions in the Resolved Resonance Region
Light-element reactions at low energies in the resolved resonance region are important for a range of applications in basic and applied sciences including nuclear reactors, nonproliferation, cultural heritage, forensics and environmental control, rare event investigations and nuclear astrophysics. In this paper, we report on an effort to evaluate charged-particle cross sections in the resolved resonance region and produce evaluated nuclear data files for further processing and inclusion in evaluated data libraries. We discuss the open issues in R-matrix calculations as we extend to higher energies, such as dealing with the rapidly growing number of open channels and merging with the regime of smooth cross sections described by the statistical model, and present attempts to address these issues in neutron-induced reactions relevant to nuclear reactor applications.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Supervised learning and the finite-temperature string method for computing committor functions and reaction rates
A central object in the computational studies of rare events is the committor function. Though costly to compute, the committor function encodes complete mechanistic information of the processes involving rare events, including reaction rates and transition-state ensembles. Under the framework of transition path theory, Rotskoff et al. [Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference, Proceedings of Machine Learning Research (PLMR, 2022), Vol. 145, pp. 757–780] proposes an algorithm where a feedback loop couples a neural network that models the committor function with importance sampling, mainly umbrella sampling, which collects data needed for adaptive training. Here, in this work, we show additional modifications are needed to improve the accuracy of the algorithm. The first modification adds elements of supervised learning, which allows the neural network to improve its prediction by fitting to sample-mean estimates of committor values obtained from short molecular dynamics trajectories. The second modification replaces the committor-based umbrella sampling with the finite-temperature string (FTS) method, which enables homogeneous sampling in regions where transition pathways are located. We test our modifications on low-dimensional systems with non-convex potential energy where reference solutions can be found via analytical or finite element methods, and show how combining supervised learning and the FTS method yields accurate computation of committor functions and reaction rates. We also provide an error analysis for algorithms that use the FTS method, using which reaction rates can be accurately estimated during training with a small number of samples. The methods are then applied to a molecular system in which no reference solution is known, where accurate computations of committor functions and reaction rates can still be obtained.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Search for neutrinoless β-plus-EC decay of 120 Te with CUORE
The Cryogenic Underground Observatory for Rare Events (CUORE) is a large-scale cryogenic experiment searching for neutrinoless double-beta decay (0νββ) in 130 Te. The CUORE detector is made of natural tellurium, providing the possibility of rare event searches on isotopes other than 130 Te. In this work we describe a search for neutrinoless positron-emitting electron capture (β + EC ) decay in 120 Te with a total TeO 2 exposure of 355.7 kg yr, corresponding to 0.2405 kg yr of 120 Te. Albeit 0νββ with two final-state electrons represents the most promising channel, the emission of a positron and two 511-keV γ 's make 0νβ + EC decay signature extremely clear. To fully exploit the potential offered by the detector modularity we include events with different topology and perform a simultaneous fit of five selected signal signatures. Using blinded data we extract a median exclusion sensitivity of 3.4 × 10 22 yr at 90% credibility interval (C.I.). After unblinding we find no evidence of 0νβ + EC signal and set a 90% C.I. Bayesian lower limit of 2.9 × 10 22 yr on 120 Te half-life. This result improves by an order of magnitude the existing limit from the combined analysis of CUORE-0 and Cuoricino.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗