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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. Furthermore, the result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

42 ENGINEERING↗
DOE OSTI2017

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, in the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

A unifying Bayesian framework for merging X-ray diffraction data

Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R 2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

36 MATERIALS SCIENCE↗
DOE OSTI2015

THE DEGENERACY OF M33 MASS MODELING AND ITS PHYSICAL IMPLICATIONS

The Local Group galaxy M33 exhibits a regular spiral structure and is close enough to permit high resolution analysis of its kinematics, making it an ideal candidate for rotation curve studies of its inner regions. Previous studies have claimed the galaxy has a dark matter halo with an Navarro-Frenk-White profile, based on statistical comparisons with a small number of other profiles. We apply a Bayesian method from our previous paper to place the dark matter density profile in the context of a continuous, and more general, parameter space. For a wide range of initial assumptions we find that models with inner log slope γ{sub in} < 0.9 are strongly excluded by the kinematics of the galaxy unless the mass-to-light ratio of the stellar components in the 3.6 μm band satisfies Y{sub 3.6} ≥ 2. Such a high Y{sub 3.6} is inconsistent with current modeling of the stellar population of M33. This suggests that M33 is a galaxy whose dark matter halo has not been significantly modified by feedback. We discuss possible explanations of this result, including ram pressure stripping during earlier interactions with M31.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

WE-E-17A-05: Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis

Purpose: Previous studies have shown that CT or 18F-FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non-Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non-enhanced CT and 18F-FDG PET heterogeneity textural features on primary NSCLC tumors. Methods: One hundred patients with non-metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F-FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi-automatically delineated on non-enhanced CT using 3D SlicerTM. Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co-occurrence, difference, and run-length matrices. In addition to these textural features, first order histogram-derived metrics were computed on the whole morphological CT tumor volume, as well as on sub-volumes corresponding to fine, medium or coarse textures determined through various levels of LoG-filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis. Results: Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram-derived kurtosis and uniformity, as well as Low Grey-level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5. Conclusion: Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET images have complementary and independent prognostic value in NSCLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

On the morphology of the gamma-ray galactic centre excess

ABSTRACT The characteristics of the galactic centre excess (GCE) emission observed in gamma-ray energies – especially the morphology of the GCE – remain a hotly debated subject. The manner in which the dominant diffuse gamma-ray background is modelled has been claimed to have a determining effect on the preferred morphology. In this work, we compare two distinct approaches to the galactic diffuse gamma-ray emission background: the first approach models this emission through templates calculated from a sequence of well-defined astrophysical assumptions, while the second approach divides surrogates for the background gamma-ray emission into cylindrical galactocentric rings with free independent normalizations. At the latitudes that we focus on, we find that the former approach works better, and that the overall best fit is obtained for an astrophysically motivated fit when the GCE follows the morphology expected of dark matter annihilation. Quantitatively, the improvement compared with the best ring-based fits is roughly 6500 in the χ2 and roughly 4000 in the log of the Bayesian evidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Population Subdivision in the Gopher Frog (Rana capito) across the Fragmented Longleaf Pine-Wiregrass Savanna of the Southeastern USA

Delineating genetically distinct population segments of threatened species and quantifying population connectivity are important steps in developing effective conservation and management strategies aimed at preventing extinction. The gopher frog (Rana capito) is a xeric-adapted, pond-breeding species endemic to the Gulf and Atlantic coastal plains of the southeastern United States. This species has experienced extensive habitat loss and fragmentation in the formerly widespread longleaf pine-wiregrass savanna where it lives, resulting in individual abundance declines and population extinctions throughout its range. We used individual-based clustering methods along with Bayesian inference of historical migration based on almost 1500 multilocus microsatellite genotypes to examine genetic structure in this taxon. Clustering analyses identified panhandle and peninsular populations in Florida as distinct genetic clusters separated by the Aucilla River, consistent with the division between the Coastal Plain and peninsular mitochondrial lineages, respectively. Analysis of historical migration indicated an east–west population divergence event followed by immigration to the east. Together, our results indicate that the genetically distinct Coastal Plain and peninsular Florida lineages should be considered separately for conservation and management purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and A Posteriori Error Estimation Methods

It was demonstrated that a posteriori analyses in general and in particular one that uses adjoint methods can accurately and efficiently compute numerical error estimates and sensitivity for critical Quantities of Interest (QoIs) that depend on a large number of parameters. Activities include: analysis and implementation of several time integration techniques for solving system of ODEs as typically obtained from spatial discretization of PDE systems; multirate integration methods for ordinary differential equations; formulation and analysis of an iterative multi-discretization Galerkin finite element method for multi-scale reaction-diffusion equations; investigation of an inexpensive postprocessing technique to estimate the error of finite element solution of the second-order quasi-linear elliptic problems measured in some global metrics; investigation of an application of the residual-based a posteriori error estimates to symmetric interior penalty discontinuous Galerkin method for solving a class of second order quasi-linear elliptic problems; a posteriori analysis of explicit time integrations for system of linear ordinary differential equations; derivation of accurate a posteriori goal oriented error estimates for a user-defined quantity of interest for two classes of first and second order IMEX schemes for advection-diffusion-reaction problems; Postprocessing finite element solution; and A Bayesian Framework for Uncertain Quantification of Porous Media Flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Semi‐Mechanistic Model for Partitioning Evapotranspiration Reveals Transpiration Dominates the Water Flux in Drylands

Abstract Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well‐suited to dryland ecosystems, such as high sensitivity of plant water‐use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine‐scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi‐mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process‐based models, and loosely constrains time‐varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross‐wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub‐yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem‐scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.

Environmental Sciences & Ecology↗
DOE OSTI2017

Near-IR Transmission Spectrum of HAT-P-32b using HST/WFC3

We report here the analysis of the near-infrared transit spectrum of the hot Jupiter HAT-P-32b, which was recorded with the Wide Field Camera 3 (WFC3) on board the Hubble Space Telescope. HAT-P-32b is one of the most inflated exoplanets discovered, making it an excellent candidate for transit spectroscopic measurements. To obtain the transit spectrum, we have adopted different analysis methods, both parametric and non-parametric (Independent Component Analysis, ICA), and compared the results. The final spectra are all consistent within 0.5σ. The uncertainties obtained with ICA are larger than those obtained with the parametric method by a factor of ∼1.6–1.8. This difference is the trade-off for higher objectivity due to the lack of any assumption about the instrument systematics compared to the parametric approach. The ICA error bars are therefore worst-case estimates. To interpret the spectrum of HAT-P-32b we used T-REx, our fully Bayesian spectral retrieval code. As for other hot Jupiters, the results are consistent with the presence of water vapor (logH{sub 2}O=−3.45{sub −1.65}{sup +1.83}), clouds (top pressure between 5.16 and 1.73 bar). Spectroscopic data over a broader wavelength range are needed to de-correlate the mixing ratio of water vapor from clouds and identify other possible molecular species in the atmosphere of HAT-P-32b.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

Zhu, Yitan↗
DOE OSTI2019

Likelihood non-Gaussianity in large-scale structure analyses

The standard present-day large-scale structure (LSS) analyses make a major assumption in their Bayesian parameter inference – that the likelihood has a Gaussian form. For summary statistics currently used in LSS, this assumption, even if the underlying density field is Gaussian, cannot be correct in detail. We investigate the impact of this assumption on two recent LSS analyses: the Beutler et al. power spectrum multipole ($P_ℓ$) analysis and the Sinha et al. group multiplicity function ($ζ$) analysis. Using non-parametric divergence estimators on mock catalogues originally constructed for covariance matrix estimation, we identify significant non-Gaussianity in both the $P_ℓ$ and $ζ$ likelihoods. We then use Gaussian mixture density estimation and independent component analysis on the same mocks to construct likelihood estimates that approximate the true likelihood better than the Gaussian pseudo-likelihood. Using these likelihood estimates, we accurately estimate the true posterior probability distribution of the Beutler et al. and Sinha et al. parameters. Likelihood non-Gaussianity shifts the fσ8 constraint by -0.44σ, but otherwise does not significantly impact the overall parameter constraints of Beutler et al. For the $ζ$ analysis, using the pseudo-likelihood significantly underestimates the uncertainties and biases the constraints of the Sinha et al. halo occupation parameters. For log $M_1$ and α, the posteriors are shifted by +0.43σ and -0.51σ and broadened by 42 per cent and 66 per cent, respectively. The divergence and likelihood estimation methods we present provide a straightforward framework for quantifying the impact of likelihood non-Gaussianity and deriving more accurate parameter constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The DESI PRObabilistic Value-added Bright Galaxy Survey (PROVABGS) Mock Challenge

The PRObabilistic Value-added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass ($M*$), star formation rate (SFR), stellar metallicity (Z), and stellar age ($t_{age}$), for >10 million galaxies of the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey. Full posterior distributions of the galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and Legacy Surveys photometry. In this work, we present the SED model, the neural emulator for the model, and the Bayesian inference framework of PROVABGS. Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI spectra and photometry, constructed using the L-Galaxies semi-analytic model. We compare the inferred galaxy properties to the true values of the simulation using a hierarchical Bayesian framework to quantify accuracy and precision. Overall, we accurately infer the true $M*$, SFR, Z, and $t_{age}$ of the simulated galaxies. However, the priors on galaxy properties induced by the SED model have a significant impact on the posteriors, which we characterize in detail. This work also demonstrates that a joint analysis of spectra and photometry significantly improves the constraints on galaxy properties over photometry alone and is necessary to mitigate the impact of the priors. With the methodology presented and validated in this work, PROVABGS will maximize information extracted from DESI observations and extend current galaxy studies to new regimes and unlock cutting-edge probabilistic analyses. https://github.com/changhoonhahn/provabgs/

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters

We use 1169 Pan-STARRS supernovae (SNe) and 195 low-z (z <0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper(I) we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation.Combining SNe with Cosmic Microwave Background (CMB) constraints from Planck, we measure the dark energy equation of state parameter w to be -0.989±0.057 (stat+sys). If w evolves with redshift as w(a) =w 0 +w a (1-α), we find w 0 =-0.912 ± 0.149 and w a = -0.513 ± 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample.We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, σ$^{CC}_{w}$=0.012, is the third-smallest source of systematic uncertainty in this work. We find limited(1.6σ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high-z analyses. Our data provide one of the best current constraints on w, demonstrating that samples with~5% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data

In this report, we present preliminary research into nonparametric clustering methods for multi-source imagery data and quantifying the performance of these models. In many domain areas, data sets do not necessarily follow well-defined and well-known probability distributions, such as the normal, gamma, and exponential. This is especially true when combining data from multiple sources describing a common set of objects (which we call multimodal analysis), where the data in each source can follow different distributions and need to be analyzed in conjunction with one another. This necessitates nonparametric density estimation methods, which allow the data to better dictate the distribution of the data. One prominent example of multimodal analysis is multimodal image analysis, when we analyze multiple images taken using different radar systems of the same scene of interest. We develop uncertainty analysis methods, which are inherent in the use of probabilistic models but often not taken advance of, to assess the performance of probabilistic clustering methods used for analyzing multimodal images. This added information helps assess model performance and how much trust decision-makers should have in the obtained analysis results. The developed methods illustrate some ways in which uncertainty can inform decisions that arise when designing and using machine learning models.

42 ENGINEERING↗
DOE OSTI2017

MC 2 : A Deeper Look at ZwCl 2341.1+0000 with Bayesian Galaxy Clustering and Weak Lensing Analyses

ZwCl 2341.1+0000, a merging galaxy cluster with disturbed X-ray morphology and widely separated (~3 Mpc) double radio relics, was thought to be an extremely massive (10 - 30 X 10 14 M⊙) and complex system with little known about its merger history. We present JVLA 2-4 GHz observations of the cluster, along with new spectroscopy from our Keck/DEIMOS survey, and apply Gaussian Mixture Modeling to the three-dimensional distribution of 227 con rmed cluster galaxies. After adopting the Bayesian Information Criterion to avoid over tting, which we discover can bias total dynamical mass estimates high, we nd that a three-substructure model with a total dynamical mass estimate of 9:39 ± 0:81 X 10 14 M⊙ is favored. We also present deep Subaru imaging and perform the rst weak lensing analysis on this system, obtaining a weak lensing mass estimate of 5:57±2:47X10 14 M⊙. This is a more robust estimate because it does not depend on the dynamical state of the system, which is disturbed due to the merger. Our results indicate that ZwCl 2341.1+0000 is a multiple merger system comprised of at least three substructures, with the main merger that produced the radio relics occurring near to the plane of the sky, and a younger merger in the North occurring closer to the line of sight. Dynamical modeling of the main merger reproduces observed quantities (relic positions and polarizations, subcluster separation and radial velocity difference), if the merger axis angle of ~10 +34 -6 degrees and the collision speed at pericenter is ~1900 +300 -200 km/s.

79 ASTRONOMY AND ASTROPHYSICS↗