Statistics of limit root bundles relevant for exact matter spectra of F-theory MSSMs
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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Non-extensive statistics to the cosmological lithium problem
Big Bang nucleosynthesis (BBN) theory predicts the abundances of the light elements D, 3 He, 4 He, and 7 Li produced in the early universe. The primordial abundances of D and 4 He inferred from observational data are in good agreement with predictions, however, BBN theory overestimates the primordial 7 Li abundance by about a factor of three. This is the so-called "cosmological lithium problem." Solutions to this problem using conventional astrophysics and nuclear physics have not been successful over the past few decades, probably indicating the presence of new physics during the era of BBN. We have investigated the impact on BBN predictions of adopting a generalized distribution to describe the velocities of nucleons in the framework of Tsallis non-extensive statistics. This generalized velocity distribution is characterized by a parameter q, and reduces to the usually assumed Maxwell–Boltzmann distribution for q = 1. We find excellent agreement between predicted and observed primordial abundances of D, 4 He, and 7 Li for 1.069 ≤ q ≤ 1.082, suggesting a possible new solution to the cosmological lithium problem.
79 ASTRONOMY AND ASTROPHYSICS↗
Dynamics for a two-atom two-mode intensity-dependent Raman coupled model
We study the quantum dynamics of a two-atom Raman coupled model interacting with a quantized bimodal field with intensity-dependent coupling terms in a lossless cavity. The unitary transformation method used to solve the time-dependent problem also gives the eigensolutions of the interaction Hamiltonian. We study the atomic-population dynamics and dynamics of the photon statistics in the two cavity modes, and present evidence of cooperative effects in the production of antibunching and anticorrelations between the modes. We also investigate the effect of detuning on the evolution of second-order correlation functions and observe that the oscillations become more rapid for large detuning.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Global Statistics of Microphysical Properties of Cloud-Top Ice Crystals
Ice crystals in clouds are highly complex. Their sizes, macroscale shape (i.e., habit), mesoscale shape (i.e., aspect ratio of components) and microscale shape (i.e., surface roughness) determine optical properties and affect physical properties such as fall speeds, growth rates and aggregation efficiency. Our current understanding on the formation and evolution of ice crystals under various conditions can be considered poor. Commonly, ice crystal size and shape are related to ambient temperature and humidity, but global observational statistics on the variation of ice crystal size and particularly shape have not been available. Here we show results of a project aiming to infer ice crystal size, shape and scattering properties from a combination of MODIS measurements and POLDER-PARASOL multi-angle polarimetry. The shape retrieval procedure infers the mean aspect ratios of components of ice crystals and the mean microscale surface roughness levels, which are quantifiable parameters that mostly affect the scattering properties, in contrast to a habit. We present global statistics on the variation of ice effective radius, component aspect ratio, microscale surface roughness and scattering asymmetry parameter as a function of cloud top temperature, latitude, location, cloud type, season, etc. Generally, with increasing height, sizes decrease, roughness increases, asymmetry parameters decrease and aspect ratios increase towards unity. Some systematic differences are observed for clouds warmer and colder than the homogeneous freezing level. Uncertainties in the retrievals will be discussed. These statistics can be used as observational targets for modeling efforts and to better constrain other satellite remote sensing applications and their uncertainties.
Van Diedenhoven, Bastiaan↗
Discovering neutrino tridents at the Large Hadron Collider
Neutrino trident production of dilepton pairs is well recognized as a sensitive probe of both electroweak physics and physics beyond the Standard Model. Although a rare process, it could be significantly boosted by such new physics, and it also allows the electroweak theory to be tested in a new regime. We demonstrate that the forward neutrino physics program at the Large Hadron Collider offers a promising opportunity to measure for the first time, dimuon neutrino tridents with a statistical significance exceeding 5 σ , improving on the previous claims at the ∼ 3 σ level by the CHARM-II and CCFR collaborations while accounting for additional backgrounds later identified by the NuTeV collaboration. We present predictions for various proposed experiments and outline a specific experimental strategy to identify the signal and mitigate backgrounds, based on “reverse tracking” dimuon pairs in the FASER ν 2 detector. We also discuss prospects for constraining beyond Standard Model contributions to neutrino trident rates at high energies. Published by the American Physical Society 2024
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization
Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.
09 BIOMASS FUELS↗
Magnetic imaging and statistical analysis of the metamagnetic phase transition of FeRh with electron spins in diamond
Magnetic imaging based on nitrogen-vacancy (NV) centers in diamond has emerged as a powerful tool for probing magnetic phenomena in fields ranging from biology to physics. A key strength of NV sensing is its local-probe nature, enabling high-resolution spatial images of magnetic stray fields emanating from a sample. However, this local character can also form a drawback for analyzing the global properties of a system, such as a phase transition temperature. Here, we address this challenge by using statistical analyses of magnetic-field maps to characterize the first-order temperature-driven metamagnetic phase transition from the antiferromagnetic to the ferromagnetic state in FeRh. After imaging the phase transition and identifying the regimes of nucleation, growth, and coalescence of ferromagnetic domains, we statistically characterize the spatial magnetic-field maps to extract the transition temperature and thermal hysteresis width. By analyzing the spatial correlations of the maps in relation to the magnetocrystalline anisotropy and external magnetic field, we detect a reorientation of domain walls across the phase transition. The employed statistical approach can be extended to the study of other magnetic phenomena with NV magnetometry or other sensing techniques.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Uncertainty Quantification and Management for Multi-scale Nuclear Materials Modeling
Understanding and improving microstructural mechanical stability in metals and alloys is central to the development of high strength and high ductility materials for cladding and cores structures in advanced fast reactors. Design and enhancement of radiation-induced damage tolerant alloys are facilitated by better understanding the connection of various unit processes to collective responses in a multiscale model chain, including: dislocation nucleation, absorption and desorption at interfaces; vacancy production, radiation-induced segregation of Cr and Ni at defect clusters (point defect sinks) in BCC Fe-Cr ferritic/martensitic steels; investigation of interaction of interstitials and vacancies with impurities (V, Nb, Ta, Mo, W, Al, Si, P, S); time evolution of swelling (cluster growth) phenomena of irradiated materials; and energetics and kinetics of dislocation bypass of defects formed by interstitial clustering and formation of prismatic loops, informing statistical models of continuum character with regard to processes of dislocation glide, vacancy agglomeration and swelling, climb and cross slip.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Interaction-induced transition in the quantum chaotic dynamics of a disordered metal
We demonstrate that a weakly disordered metal with short-range interactions exhibits a transition in the quantum chaotic dynamics when changing the temperature or the interaction strength. For weak interactions, the system displays exponential growth of the out-of-time-ordered correlator (OTOC) of the current operator. The Lyapunov exponent of this growth is temperature-independent in the limit of vanishing interaction. With increasing the temperature or the interaction strength, the system undergoes a transition to a non-chaotic behaviour, for which the exponential growth of the OTOC is absent. We conjecture that the transition manifests itself in the quasiparticle energy-level statistics and also discuss ways of its explicit observation in cold-atom setups.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems
We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Input to the European strategy for particle physics: strong-field quantum electrodynamics
This document sets out the intention of the strong-field QED community to carry out, both experimentally and numerically, high-statistics parametric studies of quantum electrodynamics in the non-perturbative regime, at fields approaching and exceeding the critical or ‘Schwinger’ field of QED (F qed ≈ m 2 c 3 / eh ≈ 1.3 x 10 18 V/m) in the rest frame of a charged particle. In this regime, several exotic and fascinating phenomena are predicted to occur that have never been directly observed in the laboratory. These include Breit–Wheeler pair production, vacuum birefringence, and quantum radiation reaction. This experimental programme will also serve as a stepping stone towards studies of elusive phenomena such as elastic scattering of real photons and the conjectured perturbative breakdown of QED at extreme fields. State-of-the-art high-power laser facilities in Europe and beyond are starting to offer unique opportunities to study this uncharted regime at the intensity frontier, which is highly relevant also for the design of future multi-TeV lepton colliders. A transition from qualitative observational experiments to quantitative and high-statistics measurements can only be performed with large-scale collaborations and with systematic experimental programmes devoted to the optimisation of several aspects of these complex experiments, including detector developments, stability and tolerances studies, and laser technology.
Sarri, G. [Queen’s University Belfast (United King↗
Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds
Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Composition mediated serration dynamics in Zr-based bulk metallic glasses
The composition mediated serration dynamics in Zr-based bulk metallic glasses (BMGs) is investigated by statistics analyses of the elastic-energy density, and free volumes during shear-banding are beneficial to understand serrated-flow behavior. The amplitude and elastic-energy density display a gradually increasing and then decreasing trend with increasing the content of Zr. It is based on the free-volume theory describing the atomic-level structure of ternary Zr-Cu-Al BMGs. The good agreement between the molecular dynamics simulation and experimental results provides evidence for the variation of free volumes as the elementary mechanism of composition mediated serration dynamics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
FlameNEST: explicit profile likelihoods with the Noble Element Simulation Technique
We present FlameNEST, a framework providing explicit likelihood evaluations in noble element particle detectors using data-driven models from the Noble Element Simulation Technique. FlameNEST provides a way to perform statistical analyses on real data with no dependence on large, computationally expensive Monte Carlo simulations by evaluating the likelihood on an event-by-event basis using analytic probability elements convolved together in a single TensorFlow multiplication. Furthermore, this robust framework creates opportunities for simple inter-collaboration analyses which will be fundamental for the future of experimental dark matter physics.
47 OTHER INSTRUMENTATION↗
Using Dense Array Waveform Correlations to Build a Velocity Model with Stochastic Variability
In an ongoing effort to improve 3D seismic-wave propagation modeling for frequencies up to 10 Hz, we used cross correlations between vertical-component waveforms from an underground chemical explosion to estimate the statistical properties of small-scale velocity heterogeneities. The waveforms were recorded by a dense 2D seismic array deployed during the Source Physics Experiments for event number 5 (SPE-5) in a series of six underground chemical explosions, conducted at the Nevada National Security Site. The array consisted of 996 geophones with a 50–100 m grid spacing, deployed at the SPE site at the north end of the Yucca Flat basin. The SPE were conducted to investigate the generation and propagation of seismic and acoustic waves from underground explosions. Comparisons of decay rates of waveform cross correlations as function of interstation distance, computed for observed and synthetic seismograms from the SPE-5 chemical explosion, were used to constrain statistical properties of correlated stochastic velocity perturbations representing small-scale heterogeneities added to a geology-based velocity model of the Yucca Flat basin. Using comparisons between recorded and simulated waveform cross correlations, we were able to recover sets of statistical properties of small-scale velocity perturbations in the velocity model that produce the best-fit between the recorded and simulated ground motion. The stochastic velocity fluctuations in the velocity model that produced the smallest misfits have a horizontal correlation distance of between 400 and 800 m, a vertical correlation distance between 100 and 200 m, and a standard deviation of 10% from the nominal model velocity in the alluvium basin layers. They also have a horizontal correlation distance of 1000 m, a vertical correlation distance of 250 m, and a standard deviation of 6% in the underlying and consolidated sedimentary layers, up to a depth of 4 km. Comparisons between observed and simulated wavefields were used to assess the proposed small-scale heterogeneity enhancements to the Yucca Flat basin model. We found that adding a depth-resolved stochastic variability to the geology-based velocity model improves the overall performance of ground-motion simulations of an SPE-5 explosion in the modeled frequency range up to 10 Hz. The results may be applicable to other similar basins.
58 GEOSCIENCES↗
The effect of center-of-mass motion on photon statistics
We analyze the photon statistics of a weakly driven cavity quantum electrodynamics system and discuss the effects of photon blockade and photon-induced tunneling by effectively utilizing instead of avoiding the center-of-mass motion of a two-level atom trapped in the cavity. With the resonant interaction between atom, photon and phonon, it is shown that the bunching and anti-bunching of photons can occur with properly driving frequency. Our study shows the influence of the imperfect cooling of atom on the blockade and provides an attempt to take advantage of the center-of-mass motion.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel
We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.
36 MATERIALS SCIENCE↗
An approximately 300 Mpc void of rich clusters of galaxies
Kirshner et al. (1981) have reported an apparent absence of relatively bright galaxies over an enormous volume in the direction of Bootes. This result, if statistically significant, has important implications for theories of galaxy clustering and formation. In connection with the study of the correlation function of rich clusters of galaxies, Bahcall and Soneira (1982) have noticed a large area in the sky, extending approximately 100 deg across, which is completely void of nearby, but not distant, rich Abell clusters. This apparent absence of rich clusters may represent a physical void. If this interpretation is correct, the void extends approximately 150 Mpc/h along the line of sight. The sample studied is Abell's (1958) statistical sample of clusters of galaxies. The distribution of rich clusters is discussed, taking into account observed versus expected characteristics. Attention is also given to aspects of absorption and galaxy counts in the considered region.
Bahcall, N. A.↗