Bayesian calibration for summary statistics with applications to a cluster dynamics model.
Abstract not provided.
Robbe, Pieterjan↗
SEARCH · Engineering Papers
Results for “clustering statistics”
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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Test Vector Development for Verification and Validation of Heavy-Duty Autonomous Vehicle Operations
The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation. Continuing this prior work, this paper focuses on the generation of test cases based on the critical scenarios identified that can be used to prioritize either the most common nominal driving scenarios or the least common severe driving scenarios. These test cases can then be used validate, through simulation or real-world testing, the operating design domain (ODD) for a generalized driving mission and built upon to identify spatial and temporal impacts on the driving mission of an autonomous vehicle.
Siekmann, Adam↗
“Do You Want It Right or Do You Want It Now?” Using Good Science to Get Fast Answers
Presentation discusses use of data in radiological emergency response and different methods (applied statistics, spectral clustering, spectral anomaly detection, etc.) that could be used in conjunction with machine learning to optimize response to real-world radiological events.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Detection of spatial clustering in the 1000 richest SDSS DR8 redMaPPer clusters with nearest neighbor distributions
ABSTRACT Distances to the k-nearest-neighbor (kNN) data points from volume-filling query points are a sensitive probe of spatial clustering. Here, we present the first application of kNN summary statistics to observational clustering measurement, using the 1000 richest redMaPPer clusters (0.1 ≤ z ≤ 0.3) from the SDSS DR8 catalog. A clustering signal is defined as a difference in the cumulative distribution functions (CDFs) of kNN distances from fixed query points to the observed clusters versus a set of unclustered random points. We find that the k = 1, 2-NN CDFs of redMaPPer deviate significantly from the randoms’ across scales of 35 to 155 Mpc, which is a robust signature of clustering. In addition to kNN, we also measure the two-point correlation function for the same set of redMaPPer clusters versus random points, which shows a noisier and less significant clustering signal within the same radial scales. Quantitatively, the χ2 distribution for both the kNN-CDFs and the two-point correlation function measured on the randoms peak at χ2 ∼ 50 (null hypothesis), whereas the kNN-CDFs (χ2 ∼ 300, p = 1.54 × 10−36) pick up a much more significant clustering signal than the two-point function (χ2 ∼ 100, p = 1.16 × 10−6) when measured on redMaPPer. Finally, the measured 3NN and 4NN CDFs deviate from the predicted k = 3, 4-NN CDFs assuming an ideal Gaussian field, indicating a non-Gaussian clustering signal for redMaPPer clusters, although its origin might not be cosmological due to observational systematics. Therefore, kNN serves as a more sensitive probe of clustering complementary to the two point correlation function, providing a novel approach for constraining cosmology and galaxy–halo connection.
79 ASTRONOMY AND ASTROPHYSICS↗
Modeling neutrino-induced scale-dependent galaxy clustering for photometric galaxy surveys
Abstract The increasing statistical precision of photometric redshift surveys requires improved accuracy of theoretical predictions for large-scale structure observables to obtain unbiased cosmological constraints. In ΛCDM cosmologies, massive neutrinos stream freely at small cosmological scales, suppressing the small-scale power spectrum. In massive neutrino cosmologies, galaxy bias modeling needs to accurately relate the scale-dependent growth of the underlying matter field to observed galaxy clustering statistics. In this work, we implement a computationally efficient approximation of the neutrino-induced scale-dependent bias (NISDB). Through simulated likelihood analyses of Dark Energy Survey Year 3 (DESY3) and Legacy Survey of Space and Time Year 1 (LSSTY1) synthetic data that contain an appreciable NISDB, we examine the impact of linear galaxy bias and neutrino mass modeling choices on cosmological parameter inference. We find model misspecification of the NISDB approximation and neutrino mass models to decrease the constraining power of photometric galaxy surveys and cause parameter biases in the cosmological interpretation of future surveys. We quantify these biases and devise mitigation strategies.
Astronomy & Astrophysics↗
Atomistically-informed modeling of point defect clustering and evolution in irradiated ThO 2
A cluster dynamics (CD) model has been developed to investigate the nucleation and growth of point defect clusters, i.e., interstitial prismatic loops and nanoscale and sub-nanoscale voids, in ThO 2 during irradiation by energetic particles. The model considers cluster off-stoichiometry due to the asymmetry of point defect generation on the O and Th sublattices under irradiation, as well as the point defect diffusivities and the defect binding energies to clusters. The energies were established using detailed molecular dynamics simulations considering the statistical variability of cluster configuration. A high-order adaptive time-integration has been used to solve the model. The predicted loop density and their average size is in good agreement with reported experimental observations for proton irradiated ThO 2 at 600°C. In conclusion, the model did not predict void evolution due to the sluggish kinetics of cation vacancies, explaining the absence of voids in proton irradiated ThO 2 (and other oxides) at relatively low temperatures.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Clustering and runaway merging in a primordial black hole dominated universe
If primordial black holes (PBHs) are present in the early Universe, their contribution to the energy budget grows relative to that of radiation and generically becomes dominant unless the initial abundance is exponentially small. This black hole domination scenario is largely unconstrained for PBHs with masses ≲ 10 9 g, which evaporate prior to big bang nucleosynthesis. However, if the era of PBH domination is sufficiently long, the PBHs form clusters and can merge appreciably within these objects. We calculate the population statistics of these clusters within the Press-Schechter formalism and find that, for a wide range of PBH masses and Hubble rates at the onset of PBH domination, the mergers within PBH clusters can exhibit runaway behavior, where the majority of the cluster will eventually form a single black hole with a mass much greater than the original PBH mass. Finally, these mergers can dramatically alter the PBH mass distribution and leave behind merged relic black holes that evaporate after big bang nucleosynthesis and yield various observational signatures, excluding parameter choices previously thought to be viable.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
$\alpha$-Clustering in atomic nuclei from first principles with statistical learning and the Hoyle state character
A long-standing crucial question with atomic nuclei is whether or not α clustering occurs there. An α particle (helium-4 nucleus) comprises two protons and two neutrons, and may be the building block of some nuclei. This is a very beautiful and fascinating idea, and is indeed plausible because the α particle is particularly stable with a large binding energy. However, direct experimental evidence has never been provided. Here, we show whether and how α(-like) objects emerge in atomic nuclei, by means of state-of-the-art quantum many-body simulations formulated from first principles, utilizing supercomputers including K/Fugaku. The obtained physical quantities exhibit agreement with experimental data. The appearance and variation of the α clustering are shown by utilizing density profiles for the nuclei beryllium-8, -10 and carbon-12. With additional insight by statistical learning, an unexpected crossover picture is presented for the Hoyle state, a critical gateway to the birth of life.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Synthetic galaxy clusters and observations based on Dark Energy Survey Year 3 Data
ABSTRACT We develop a novel data-driven method for generating synthetic optical observations of galaxy clusters. In cluster weak lensing, the interplay between analysis choices and systematic effects related to source galaxy selection, shape measurement, and photometric redshift estimation can be best characterized in end-to-end tests going from mock observations to recovered cluster masses. To create such test scenarios, we measure and model the photometric properties of galaxy clusters and their sky environments from the Dark Energy Survey Year 3 (DES Y3) data in two bins of cluster richness $\lambda \in [30; 45)$, $\lambda \in [45; 60)$ and three bins in cluster redshift ($z\in [0.3; 0.35)$, $z\in [0.45; 0.5)$ and $z\in [0.6; 0.65)$. Using deep-field imaging data, we extrapolate galaxy populations beyond the limiting magnitude of DES Y3 and calculate the properties of cluster member galaxies via statistical background subtraction. We construct mock galaxy clusters as random draws from a distribution function, and render mock clusters and line-of-sight catalogues into synthetic images in the same format as actual survey observations. Synthetic galaxy clusters are generated from real observational data, and thus are independent from the assumptions inherent to cosmological simulations. The recipe can be straightforwardly modified to incorporate extra information, and correct for survey incompleteness. New realizations of synthetic clusters can be created at minimal cost, which will allow future analyses to generate the large number of images needed to characterize systematic uncertainties in cluster mass measurements.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Comparison of the Distribution of the In Situ Ring Current Density Based on Magnetic Field Observations From THEMIS, MMS and Cluster
Abstract We review and extend previous work on the ring current density distribution by applying the curlometer technique to multi‐spacecraft magnetic field data on different spatial scales, directly comparing the results of three space missions: Time History of Events and Macroscale Interactions during Substorms (THEMIS), The Magnetospheric Multiscale (MMS) and Cluster, that have so far accrued decades of data on a range of spatial separation scales. We suggest that the statistical results of Cluster data over two decades can illustrate well the overall form of the ring current (in the ring plane), but can underestimate the current density, while THEMIS (partially) and MMS are able to observe the detailed structure of the ring current and indeed show different behavior on some (smaller) spatial scales. These results suggest that, in addition to the local time and radial form, reported previously, the morphology of the ring current may also depend on magnetic latitude, as influenced by the tilt angle between the inner and outer magnetic fields.
Tan, X. [School of Space and Earth Sciences Beihan↗
A spatially-resolved model of neutron-irradiated tungsten coupling stochastic cluster dynamics and finite deformation plasticity
Structural materials used in nuclear reactors face severe degradation in mechanical properties, such as hardening and embrittlement. At the microscopic scale, this occurs due to creation and accumulation of irradiation-induced defects and their interaction with system dislocations. Although techniques exist which can model evolution of irradiation defects, for instance kinetic transport theory-based models, their interaction with mechanical deformation of the bulk material has not been investigated extensively. In this work, we demonstrate a novel spatially-resolved multiscale coupling between microscopic irradiation defect evolution, modeled using Stochastic Cluster Dynamics (SCD) and macroscopic mechanical deformation modeled using a finite-deformation plasticity model. SCD is used to determine the statistically averaged defect cluster spacing, dependent on operating conditions such as irradiation dose and temperature. This acts as an initial condition that governs the critical resolved shear stress of dislocation glide in the macroscopic plasticity model. This framework is used to predict mechanical behavior in post-mortem test of irradiated Tungsten samples, which has found its importance as structural material used in nuclear reactors. The results obtained using the coupled approach are in good agreement with experimental data of uniaxial tension tests. The model is able to capture the effect of temperature and irradiation dose on the material hardening. Two methods are proposed to estimate hardness – using Tabor's Law relating uniaxial yield stress to hardness and from flat-punch simulations. The results are in reasonable agreement with hardness data from micro-indentation experiments of irradiated Tungsten samples. Finally, the model is also able to reveal microstructural details such as spatial variation in defect density and local stress.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Illustrating galaxy–halo connection in the DESI era with illustrisTNG
ABSTRACT We employ the hydrodynamical simulation illustrisTNG to inform the galaxy–halo connection of the Luminous Red Galaxy (LRG) and Emission Line Galaxy (ELG) samples of the Dark Energy Spectroscopic Instrument (DESI) survey at redshift z ∼ 0.8. Specifically, we model the galaxy colours of illustrisTNG and apply sliding DESI colour–magnitude cuts, matching the DESI target densities. We study the halo occupation distribution (HOD) model of the selected samples by matching them to their corresponding dark matter haloes in the illustrisTNG dark matter run. We find the HOD of both the LRG and ELG samples to be consistent with their respective baseline models, but also we find important deviations from common assumptions about the satellite distribution, velocity bias, and galaxy secondary biases. We identify strong evidence for concentration-based and environment-based occupational variance in both samples, an effect known as ‘galaxy assembly bias’. The central and satellite galaxies have distinct dependencies on secondary halo properties, showing that centrals and satellites have distinct evolutionary trajectories and should be modelled separately. These results serve to inform the necessary complexities in modelling galaxy–halo connection for DESI analyses and also prepare for building high-fidelity mock galaxies. Finally, we present a shuffling-based clustering analysis that reveals a 10–15 ${{\ \rm per\ cent}}$ excess in the LRG clustering of modest statistical significance due to secondary galaxy biases. We also find a similar excess signature for the ELGs, but with much lower statistical significance. When a larger hydrodynamical simulation volume becomes available, we expect our analysis pipeline to pinpoint the exact sources of such excess clustering signatures.
79 ASTRONOMY AND ASTROPHYSICS↗
Plan Position Indicator Hydrometeor Field Statistics (PPIHYD) Evaluation Data Product Version 1.0
The PPIHYD evaluation data product provides distinct hydrometeor field statistics calculated from U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility scanning radar plan position indicator (PPI) scans. These statistics include the equivalent reflectivity factor and Doppler spectral width percentiles, min/max values, and first four moments (mean, standard deviation, skewness, and kurtosis) of distinct hydrometeor features (clustered hydrometeor fields). Statistics also include morphological properties, water content and precipitation rate parameterization-based estimates, and thermodynamic properties interpolated using the Interpolated Sonde value-added product (INTERPSONDE VAP). The data set is organized in tabular form and is accompanied by mask arrays with corresponding indices. This straightforward file structure simplifies scanning radar data processing and renders this data set useful for process understanding and model evaluation studies. This report describes the data set and its processing algorithm and provides some examples.
54 ENVIRONMENTAL SCIENCES↗
CASM Monte Carlo: Calculations of the thermodynamic and kinetic properties of complex multicomponent crystals
Monte Carlo techniques play a central role in statistical mechanics approaches that connect macroscopic thermodynamic and kinetic properties to the electronic structure of a material. This paper describes the implementation of Monte Carlo techniques for the study of multicomponent crystalline materials within the Clusters Approach to Statistical Mechanics (CASM) software suite, and demonstrates their use in model systems to calculate free energies and kinetic coefficients, study phase transitions, and construct phase diagrams from first principles. Many crystal structures are complex, with multiple sublattices occupied by differing sets of chemical species, along with the presence of vacancies or interstitial species. This imposes constraints on concentration variables, the form of thermodynamic potentials, and the values of kinetic transport coefficients. The framework used by CASM to formulate thermodynamic potentials and kinetic transport coefficients accounting for arbitrarily complex crystal structures is presented and demonstrated with examples of increasing complexity. Additionally, an overview of the capabilities of the CASM software specific to Monte Carlo methods is given, and a new CASM software package is introduced, casm-flow, which helps automate the setup, submission, management, and analysis of Monte Carlo simulations.
Cluster expansion↗
The Thermal and Kinematic Sunyaev–Zeldovich Effect in Galaxy Clusters and Filaments Using Multifrequency Temperature Maps of the Cosmic Microwave Background: A399–A401 Cluster Pair Case Study
We present a multifrequency and multi-instrument methodology to study the physical properties of galaxy clusters and cosmic filaments using cosmic microwave background observations. Our approach enables simultaneous measurement of both the thermal (tSZ) and kinematic Sunyaev–Zeldovich (kSZ) effects, incorporates relativistic corrections, and models astrophysical foregrounds such as thermal dust emission. We do this by jointly fitting a single physical model across multiple maps from multiple instruments at different frequencies, rather than fitting a model to a single Compton-y map. We demonstrate the success of this method by fitting the A399–A401 galaxy cluster pair and filament system using archival data from the Planck satellite and new, targeted deep data from the Atacama Cosmology Telescope, covering 11 different frequencies over 14 maps from 30 GHz to 545 GHz. Our tSZ results are consistent with previous work using Compton-y maps. We measure the line-of-sight peculiar velocities of the cluster–filament system using the kSZ effect and find statistical uncertainties on individual cluster peculiar velocities of ≲600 km s −1 , which are competitive with current state-of-the-art measurements. Additionally, we measure the optical depth of the filament component with a signal-to-noise of 8.5σ and reveal hints of its morphology. This modular approach is well-suited for application to future instruments across a wide range of millimeter and submillimeter wavebands.
Gill, Ajay S. [National Research Council of Canada↗
Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework
Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.
DFT-MD-ML workflow↗
CCCP and MENeaCS: (updated) weak-lensing masses for 100 galaxy clusters
ABSTRACT Large area surveys continue to increase the samples of galaxy clusters that can be used to constrain cosmological parameters, provided that the masses of the clusters are measured robustly. To improve the calibration of cluster masses using weak gravitational lensing we present new results for 48 clusters at 0.05 < z < 0.15, observed as part of the Multi Epoch Nearby Cluster Survey, and re-evaluate the mass estimates for 52 clusters from the Canadian Cluster Comparison Project. Updated high-fidelity photometric redshift catalogues of reference deep fields are used in combination with advances in shape measurements and state-of-the-art cluster simulations, yielding an average systematic uncertainty in the lensing signal below 5 per cent, similar to the statistical uncertainty for our cluster sample. We derive a scaling relation with Planck measurements for the full sample and find a bias in the Planck masses of 1 − b = 0.84 ± 0.04 (stat) ±0.05 (syst). We find no statistically significant trend of the mass bias with redshift or cluster mass, but find that different selections could change the bias by up to 0.07. We find a gas fraction of 0.139 ± 0.014 (stat) for eight relaxed clusters in our sample, which can also be used to infer cosmological parameters.
79 ASTRONOMY AND ASTROPHYSICS↗
Nanoscale microstructure and chemistry of transparent gahnite glass-ceramics revealed by atom probe tomography
The nanoscale microstructure and chemistry of a transparent gahnite glass-ceramic and its precursor glass were investigated using atom probe tomography, transmission electron microscopy, and nuclear magnetic resonance spectroscopy. In the annealed precursor glass, statistically significant ZrO clustering was observed along with Si depletion within 2 nm of the ZrO clusters. This clustering is expected to be the first step in the nucleation and crystallization of the nucleating agent, ZrO 2 . In the glass-ceramic, gahnite (ZnAl 2 O 4 ) crystallites were found to exist in proximal locations to ZrO 2 and not in a core-shell arrangement. The residual glass composition was directly measured by atom probe, showing higher concentrations of Al, Zn, and Zr than previously expected. At the interfaces between the crystallites and residual glass, no enrichment or depletion zones were found. Our findings provide answers to outstanding questions surrounding the nucleation and elemental partitioning, microstructure, and the residual glass composition of transparent gahnite glass-ceramics.
36 MATERIALS SCIENCE↗