Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database.
Abstract not provided.
Patel, Lekha↗
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Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database.
Abstract not provided.
Patel, Lekha↗
Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database.
Abstract not provided.
Patel, Lekha↗
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↗
Chance and necessity in the selection of nucleic acid catalysts
In Tom Stoppard's famous play [Rosencrantz and Guildenstern are Dead], the ill-fated heroes toss a coin 101 times. The first 100 times they do so the coin lands heads up. The chance of this happening is approximately 1 in 10(30), a sequence of events so rare that one might argue that it could only happen in such a delightful fiction. Similarly rare events, however, may underlie the origins of biological catalysis. What is the probability that an RNA, DNA, or protein molecule of a given random sequence will display a particular catalytic activity? The answer to this question determines whether a collection of such sequences, such as might result from prebiotic chemistry on the early earth, is extremely likely or unlikely to contain catalytically active molecules, and hence whether the origin of life itself is a virtually inevitable consequence of chemical laws or merely a bizarre fluke. The fact that a priori estimates of this probability, given by otherwise informed chemists and biologists, ranged from 10(-5) to 10(-50), inspired us to begin to address the question experimentally. As it turns out, the chance that a given random sequence RNA molecule will be able to catalyze an RNA polymerase-like phosphoryl transfer reaction is close to 1 in 10(13), rare enough, to be sure, but nevertheless in a range that is comfortably accessible by experiment. It is the purpose of this Account to describe the recent advances in combinatorial biochemistry that have made it possible for us to explore the abundance and diversity of catalysts existing in nucleic acid sequence space.
Review, Tutorial↗
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↗