Final-state interactions in neutrino-induced proton knockout from argon in MicroBooNE
Not Available
Nikolakopoulos, A. (ORCID:0000000169638115)↗
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MSTAR2019 Code Description and User's Manual
This report describes an ideal cascade model of uranium enrichment that includes side feed and side product streams and flexible input options. It is based on the original MSTAR model developed by Ed Von Halle and implemented in a Visual Basic code. The current version allows the user to specify integer numbers of stages or the stage numbers of external flows instead of assays in those flows. The computational engine is written in FORTRAN-90 and is invoked by a user-friendly GUI written in C++. This version of MSTAR has been demonstrated to operate on Linux, Mac, and Windows platforms, and has undergone significant testing and quality analysis. A number of examples are presented to illustrate the operation of the code and guide the user. The code is extremely fast, and results are returned immediately. The input and output have been specially configured for analysis by the environmental sampling team of the International Atomic Energy Agency (IAEA). A number of additional output features are included to assist the user in visualizing computational results and downloading data to files for use in other analysis software.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Cascading Transformer Failure Probability Model Under Geomagnetic Disturbances
This paper develops a probabilistic model to assess the cascading failure of transformers in an electric power grid experiencing geomagnetic disturbances caused by a solar storm. We propose a model in which the probability of failure is a function of the intensity of the solar storm, the physical properties of the transformer, the geographical location of the transformer, and the flow of electrical power. We demonstrate the proposed model using the IEEE 14-bus system and several notional solar storms. The model quickly computes the initial and cascading failure probabilities of the transformers in the system as a first step towards quantifying the risks posed by future solar storms.
Shukla, Pratishtha↗
Search for heavy dark matter from dwarf spheroidal galaxies: leveraging cascades and subhalo models
Abstract TheFermiLarge Area Telescope (Fermi-LAT) has been widely used to search for Weakly Interacting Massive Particle (WIMP) dark matter signals due to its unparalleled sensitivity in the GeV energy band. The leading constraints for WIMP byFermi-LAT are obtained from the analyses of dwarf spheroidal galaxies within the Local Group, which are compelling targets for dark matter searches due to their relatively low astrophysical backgrounds and high dark matter content. In the meantime, the search for heavy dark matter with masses above TeV remains a compelling and relatively unexplored frontier. In this study, we utilize 14-yearFermi-LAT data to search for dark matter annihilation and decay signals in 8 classical dwarf spheroidal galaxies within the Local Group. We consider secondary emission caused by electromagnetic cascades of prompt gamma rays and electrons/positrons from dark matter, which enables us to extend the search withFermi-LAT to heavier dark matter cases. We also update the dark matter subhalo model with informative priors respecting the fact that they reside in subhalos of our Milky Way halo aiming to enhance the robustness of our results. We place constraints on dark matter annihilation cross section and decay lifetime for dark matter masses ranging from 10 3 GeV to 10 11 GeV, where our limits are more stringent than those obtained by many other high-energy gamma-ray instruments.
Astronomy & Astrophysics↗
Modeling the infrared cascade spectra of small PAHs: the 11.2 μm band
The profile of the 11.2 μm feature of the infrared (IR) cascade emission spectra of polycyclic aromatic hydrocarbon (PAH) molecules is investigated using a vibrational anharmonic method. Several factors are found to affect the profile including: the energy of the initially absorbed ultraviolet (UV) photon, the density of vibrational states, the anharmonic nature of the vibrational modes, the relative intensities of the vibrational modes, the rotational temperature of the molecule, and blending with nearby features. Each of these factors is explored independently and influence either the red or blue wing of the 11.2 μm feature. The majority impact solely the red wing, with the only factor altering the blue wing being the rotational temperature.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Complex, Integrative Agent-Based Model of Disinformation Cascades.
Abstract not provided.
Sweitzer, Matthew↗
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure
In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stochastic dynamical system where first-principles electromechanical dynamics are excited by small Gaussian disturbances in demand and generation around a specified operating point. In this context, a single line failure is interpreted in a large deviation context as a first escape event across a surface in phase space defined by line security constraints. The resulting system of stochastic differential equations admits a transverse decomposition of the drift, which leads to considerable simplification in evaluating the quasipotential (rate function) and, consequently, computation of exit rates. Tractable expressions for the rate of transmission line failure in a restricted network are derived from large deviation theory arguments and validated against numerical simulations. Extensions to realistic settings are considered, and individual line failure models are aggregated into a Markov model of cascading failure inspired by chemical kinetics. Cascades are generated by traversing a graph composed of weighted edges representing transitions to degraded network topologies. Numerical results indicate that the Markov model can produce cascades with qualitative power-law properties similar to those observed in empirical cascades.
Kinetic Monte Carlo, rare events, Cascading Failur↗
Modeling and control of cascaded bridgeless multilevel rectifier under unbalanced load conditions
The goal of this project is to model and control a novel unidirectional cascaded multilevel bridgeless rectifier as an active front end in medium and high voltage applications. This topology has many advantages over a conventional cascaded H-bridge rectifier, such as lower implementation cost, higher reliability, and greater flexibility with similar power quality. The complete design process of the proposed converter is developed step by step in order to meet all the desired objectives. The steady-state mathematical model is used to develop a method for the voltage balancing of dc cells. Power factor analysis is discussed to mathematically derive requirements for the number of partially controlled and fully controlled H-bridges in the proposed H-bridge converter. Power loss, efficiency, and cost comparison studies between the traditional cascaded H-Bridge converter and the proposed bridgeless converter demonstrate the advantages. After exploring various well-established control methods, a novel control strategy is proposed to achieve dc voltage balancing, fast and robust grid synchronization, power factor correction, and elimination of zero crossing current distortion under both balanced and unbalanced load conditions. The converter can also be used for reactive power compensation in a grid tied power system if a sufficient number of fully controlled H-bridge modules are included. Processor-In-the-Loop (PIL) simulation has been the utilized to validate the performance of discrete control structure. Simulation and experimental results validate the models and control method.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Implementation of INCL nuclear model in GENIE Generator
The Liège Intranuclear Cascade (INCL) model is a nuclear-physics model that simulates hadron (baryon, anti-baryon and meson) reactions on nuclei, for incident energies ranging from a few tens of MeV to 10-20 GeV. The INCL model has been well validated by hadron scattering data. In my work, I implement an interface in GENIE to use the INCL nuclear model in the simulations of both the initial state of the target nucleus and the Final State Interaction in neutrino-nucleus interaction. It has a consistent treatment of nuclear models in both neutrino interaction and hadron rescattering. A full event record including neutrino vertex and each vertex of hadron rescattering has been accomplished. Several processes, e.g. cluster production, Delta transportation and de-excitation will also be included as benefits of the implementation of the INCL model in GENIE. I will show some initial simulation results showcasing the new GENIE features and discuss plans for making them available for use in experimental analyses.
Liu, Liang [Fermilab] (ORCID:000000026753925X)↗
Implementation of INCL Nuclear Model in GENIE Generator
The Li ge Intranuclear Cascade (INCL) model is a nuclear-physics model that simulates hadron (baryon, anti-baryon and meson) reactions on nuclei, for incident energies ranging from a few tens of MeV to 10-20 GeV. The INCL model has been well validated by hadron scattering data. In my work, I implement an interface in GENIE to use the INCL nuclear model in the simulations of both the initial state of the target nucleus and the Final State Interaction in neutrino-nucleus interaction. It has a consistent treatment of nuclear models in both neutrino interaction and hadron rescattering. A full event record including neutrino vertex and each vertex of hadron rescattering has been accomplished. Several processes, e.g. cluster production, Delta transportation and de-excitation will also be included as benefits of the implementation of the INCL model in GENIE. I will show some initial simulation results showcasing the new GENIE features and discuss plans for making them available for use in experimental analyses.
Liu, Liang [Fermilab]↗
Cascade decays of heavy Higgs bosons through vectorlike quarks in two Higgs doublet models
We study cascade decays of heavy neutral Higgs bosons through vectorlike quarks. We focus on scenarios where decay modes into pairs of vectorlike quarks are not kinematically open which extends the sensitivity of the LHC to larger masses. Assuming only mixing with the third family of standard model quarks the new decay modes of heavy Higgs bosons are: H → t 4 t → Wbt, Ztt, htt and H → b 4 b → Wtb, Zbb, hbb, where t 4 (b 4 ) is the new up-type (down-type) quark mass eigenstate. In the numerical analysis we assume the CP even Higgs boson in the two Higgs doublet model type-II but the signatures are relevant for many other scenarios. We identify the region of the parameter space where these decay modes are significant or can even dominate, and thus they provide the best opportunities for the simultaneous discovery of a new Higgs boson and vectorlike quarks. We further explore the reach of the High Luminosity LHC for two representative decay modes,t 4 → Zt → ℓℓt and b 4 → Zb → ℓℓb, and found that cross sections at a 0.1 fb level can be probed with simple cut based analyses. We also find that the rates for Higgs cascade decays can be much larger than the rates for a single production of vectorlike quarks. Furthermore, the reach for vectorlike quarks in Higgs cascade decays and pair production extends to comparable masses.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A Projected Network Model of Online Disinformation Cascades
Within the past half-decade, it has become overwhelmingly clear that suppressing the spread of deliberate false and misleading information is of the utmost importance for protecting democratic institutions. Disinformation has been found to come from both foreign and domestic actors, but the effects from either can be disastrous. From the simple encouragement of unwarranted distrust to conspiracy theories promoting violence, the results of disinformation have put the functionality of American democracy under direct threat. Present scientific challenges posed by this problem include detecting disinformation, quantifying its potential impact, and preventing its amplification. We present a model on which we can experiment with possible strategies toward the third challenge: the prevention of amplification. This is a social contagion network model, which is decomposed into layers to represent physical, ''offline'', interactions as well as virtual interactions on a social media platform. Along with the topological modifications to the standard contagion model, we use state-transition rules designed specifically for disinformation, and distinguish between contagious and non-contagious infected nodes. We use this framework to explore the effect of grassroots social movements on the size of disinformation cascades by simulating these cascades in scenarios where a proportion of the agents remove themselves from the social platform. We also test the efficacy of strategies that could be implemented at the administrative level by the online platform to minimize such spread. These top-down strategies include banning agents who disseminate false information, or providing corrective information to individuals exposed to false information to decrease their probability of believing it. We find an abrupt transition to smaller cascades when a critical number of random agents are removed from the platform, as well as steady decreases in the size of cascades with increasingly more convincing corrective information. Finally, we compare simulated cascades on this framework with real cascades of disinformation recorded on Whatsapp surrounding the 2019 Indian election. We find a set of hyperparameter values that produces a distribution of cascades matching the scaling exponent of the distribution of actual cascades recorded in the dataset. We acknowledge the available future directions for improving the performance of the framework and validation methods, as well as ways to extend the model to capture additional features of social contagion.
42 ENGINEERING↗
An Enhanced OPA Model: Incorporating Dynamically Induced Cascading Failures
Not provided.
Engineering↗
From Ensemble Climate to Ensemble Impacts
Many climate-risk tools rely on ensemble mean projections or endpoint climate snapshots to characterize future hazards. Although convenient for communication, these representations remove the statistical, temporal, and physical information that real infrastructure systems respond to. Infrastructure degradation and failure arise from extremes, sequences, cumulative stress, compound hazards, and nonlinear fragility relationships, none of which survive ensemble averaging or temporal compression. Power-system failure statistics and cascading failure models further show that infrastructure risk is dominated by tail events and path-dependent dynamics rather than by mean conditions. This paper demonstrates why ensemble mean or endpoint-only climate representations are mathematically and physically inconsistent with engineering-grade risk analysis. We outline a model-resolved, time-series-based workflow that preserves extremes, variability, and sequencing by propagating each climate-model realization independently through hazard formation, exposure, fragility, and cascading failure mechanisms. Taking the ensemble of impacts—rather than the ensemble of climate—provides a defensible, physically coherent foundation for infrastructure resilience planning, regulatory compliance, and long-term investment decisions.
54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
Quantum Molecular Charge-Transfer Model for Multistep Auger–Meitner Decay Cascade Dynamics
The fragmentation of molecular cations following inner-shell decay processes in molecules containing heavy elements underpins the X-ray damage effects observed in X-ray scattering measurements of biological and chemical materials, as well as in medical applications involving Auger electron-emitting radionuclides. Traditionally, these processes are modeled using simulations that describe the electronic structure at an atomic level, thereby omitting molecular bonding effects. This work addresses the gap by introducing a novel approach that couples Auger–Meitner decay to nuclear dynamics across multiple decay steps, by developing a decay spawning dynamics algorithm and applying it to potential energy surfaces characterized with ab initio molecular dynamics simulations. We showcase the approach on a model decay cascade following K-shell ionization of IBr and subsequent Kβ fluorescence decay. We examine two competing channels that undergo two decay steps, resulting in ion pairs with a total 3+ charge state. This approach provides a continuous description of the electron transfer dynamics occurring during the multistep decay cascade and molecular fragmentation, revealing the combined inner-shell decay and charge transfer time scale to be approximately 75 fs. In conclusion, our computed kinetic energies of ion fragments show good agreement with experimental data.
Ab initio molecular dynamics↗
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation
Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larger increase in computation and memory requirements during inference. While this is a non-issue in traditional machine learning (ML) pipelines, the recent confluence of machine learning and fields like the Internet of Things (IoT) has rendered such large architectures infeasible for execution in low-resource settings. For some datasets, large monolithic pipelines may be overkill for simpler inputs. To address this problem, previous efforts have proposed decision cascades where inputs are passed through models of increasing complexity until the desired performance is achieved. However, we argue that cascaded prediction leads to sub-optimal throughput and increased computational cost due to wasteful intermediate computations. To address this, we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a non-cascading, cost-aware learning pipeline as an efficient alternative to cascaded decision architectures. Through experimental evaluation on both real-world and standard datasets, we demonstrate that PaSeR achieves better accuracy while minimizing computational cost relative to cascaded models. Further, we introduce a new metric IoU/GigaFlop to evaluate the balance between cost and performance. On the real-world task of battery material phase segmentation, PaSeR yields 179% improvement over SOTA MatPhase model and a 196% improvement over IDK Cascades under the IoU/GigaFlop metric. We also demonstrate PaSeR’s adaptability to complementary models trained on a noisy MNIST dataset, where it outperforms all baselines on IoU/GigaFlop by an average of 44%.
Srikshan, Bharat↗
NOvA central value tuning and uncertainties for the hN FSI model in GENIE 3 [Poster]
Hadrons produced from a neutrino scattering interaction can re-interact within the nucleus as it is traversed. These re-interactions are known as final state interactions (FSI). For 2020 NOvA chose to use the hN2018 semi-classical cascade FSI model in GENIE 3.0.6 [1] because, as opposed to the “effective” model hA, hN uses an explicit model [2] to predict how external pion scattering data is connected to propagation inside the nucleus.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗