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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2025

Explainable multi-fidelity Bayesian neural network for distribution system state estimation

Distribution System State Estimation (DSSE) is frequently constrained by limited real-time measurements, the uncertainties introduced by distributed energy resources, and the presence of bad data. To address them, this paper proposes an enhanced Multi-Fidelity Bayesian Neural Network (MFBNN) DSSE approach. A low-fidelity layer based on a Deep Neural Network (DNN) is first pre-trained on pseudo-measurement data to learn fundamental state features. Subsequently, a high-fidelity Bayesian Neural Network (BNN) layer leverages limited but high-quality real-time measurements to refine these features, thereby achieving accurate DSSE. Additionally, the deep SHapley Additive exPlanation (SHAP) is developed to quantify the influence of measurement data on DSSE through dual perspectives of global feature importance and local nodal contributions, establishing a hierarchical explainability framework for machine learning-based DSSE. Comparative studies conducted on the IEEE 13-bus system and a real-world 2135-node system from Dominion Energy demonstrate that the proposed method excels in estimation accuracy, even under situations of high noise levels, bad data, and missing data. Further comparisons with Weighted Least Squares (WLS) and other machine learning-based DSSE approaches verify that the proposed framework offers higher accuracy, improved interpretability, and enhanced robustness.

Bad data↗
DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Applying Bayesian inference and deterministic anisotropy to retrieve the molecular structure ∣Ψ(R)∣2 distribution from gas-phase diffraction experiments

Abstract Currently, our general approach to retrieving molecular structures from ultrafast gas-phase diffraction heavily relies on complex ab initio electronic or vibrational excited state simulations to make conclusive interpretations. Without such simulations, inverting this measurement for the structural probability distribution is typically intractable. This creates a so-called inverse problem. Here we address this inverse problem by developing a broadly applicable method that approximates the molecular frame structure ∣Ψ( R , t )∣ 2 distribution independent of these complex simulations. We retrieve the vibronic ground state ∣Ψ( R )∣ 2 for both simulated stretched NO 2 and measured N 2 O. From measured N 2 O, we observe 40 mÅ coordinate-space resolution from 3.75 Å −1 reciprocal space range and poor signal-to-noise, a 50X improvement over traditional Fourier transform methods. In simulated NO 2 diffraction experiments, typical to high signal-to-noise levels predict 100–1000X resolution improvements, down to 0.1 mÅ. By directly measuring the width of ∣Ψ( R )∣ 2 , we open ultrafast gas-phase diffraction capabilities to measurements beyond current analysis approaches. This method has the potential to effectively turn gas-phase ultrafast diffraction into a discovery-oriented technique to probe systems that are prohibitively difficult to simulate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Abstract In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

Engineering↗
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 OSTI2022

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Sensor Selection and Data Validation for Reliable Integrated System Health Management

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Garg, Sanjay↗
NASA NTRS2021

Systematic KMTNet Planetary Anomaly Search. I. OGLE-2019-BLG-1053Lb, a BuriedTerrestrial Planet

In order to exhume the buried signatures of “missing planetary caustics” in Korea Microlensing Telescope Network (KMTNet) data, we conducted a systematic anomaly search of the residuals from point-source point-lens fits, based on a modified version of the KMTNet Event Finder algorithm. This search revealed the lowest-mass-ratio planetary caustic to date in the microlensing event OGLE-2019-BLG-1053, for which the planetary signal had not been noticed before. The planetary system has a planet–host mass ratio ofq= (1.25±0.13) × 10−5. A Bayesian analysis yielded estimates of the mass of the host star, Mhost =-0.61+0.29 -0.24 Mo, the mass of its planet, Mplanet =-2.48 +1.19 -0.98 Mo, the projected planet – host separation, a^= 3.4 +0.5/-0.5 au, and the lens distance, DL =-6.8 +0.6 -0.90kpc.The discovery of this very-low-mass-ratio planet illustrates the utility of our method and opens a new window for a large and homogeneous sample to study the microlensing planet–host mass ratio function down to q∼ 10−5.

Exoplanet detection methods↗
DOE OSTI2022

Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations

Optimal experimental design (OED) is the general formalism of sensor placement and decisions on the data collection strategy for engineered or natural experiments. This problem is prevalent in many critical fields such as battery design, numerical weather prediction, geosciences, environmental and urban studies. State-of-the-art computational methods for experimental design do not accommodate correlation structure in observational errors produced by many expensive-to-operate devices such as X-ray machines, radars, and satellites. Discarding evident data correlations leads to biased results, higher expenses, and waste of valuable resources. We present a general formulation of the OED formalism for model-constrained large-scale Bayesian linear inverse problems, where measurement errors are generally correlated. The proposed approach utilizes the Hadamard product of matrices to formulate the weighted-likelihood, and is valid for both finite as well as infinite-dimensional Bayesian inverse problems. Extensive numerical experiments are carried out for empirical verification of the proposed approach using an advection-diffusion model, where the objective is to optimally place a small set of sensors, under a limited budget, to predict the concentration of a contaminant in a closed and bounded domain.

Correlated observations↗
NASA NTRS2005

Bayesian Inference in Satellite Gravity Inversion

To solve a geophysical inverse problem means applying measurements to determine the parameters of the selected model. The inverse problem is formulated as the Bayesian inference. The Gaussian probability density functions are applied in the Bayes's equation. The CHAMP satellite gravity data are determined at the altitude of 400 kilometer altitude over the South part of the Pannonian basin. The model of interpretation is the right vertical cylinder. The parameters of the model are obtained from the minimum problem solved by the Simplex method.

Kis, K. I.↗
NASA NTRS2005

Influence of Coronal Abundance Variations

The PI of this project was Jeff Scargle of NASA/Ames. Co-I's were Alma Connors of Eureka Scientific/Wellesley, and myself. Part of the work was subcontracted to Eureka Scientific via SAO, with Vinay Kashyap as PI. This project was originally assigned grant number NCC2-1206, and was later changed to NCC2-1350 for administrative reasons. The goal of the project was to obtain, derive, and develop statistical and data analysis tools that would be of use in the analyses of high-resolution, high-sensitivity data that are becoming available with new instruments. This is envisioned as a cross-disciplinary effort with a number of "collaborators" including some at SA0 (Aneta Siemiginowska, Peter Freeman) and at the Harvard Statistics department (David van Dyk, Rostislav Protassov, Xiao-li Meng, Epaminondas Sourlas, et al). We have developed a new tool to reliably measure the metallicities of thermal plasma. It is unfeasible to obtain high-resolution grating spectra for most stars, and one must make the best possible determination based on lower-resolution, CCD-type spectra. It has been noticed that most analyses of such spectra have resulted in measured metallicities that were significantly lower than when compared with analyses of high- resolution grating data where available (see, e.g., Brickhouse et al., 2000, ApJ 530,387). Such results have led to the proposal of the existence of so-called Metal Abundance Deficient, or "MAD" stars (e.g., Drake, J.J., 1996, Cool Stars 9, ASP Conf.Ser. 109, 203). We however find that much of these analyses may be systematically underestimating the metallicities, and using a newly developed method to correctly treat the low-counts regime at the high-energy tail of the stellar spectra (van Dyk et al. 2001, ApJ 548,224), have found that the metallicities of these stars are generally comparable to their photospheric values. The results were reported at the AAS (Sourlas, Yu, van Dyk, Kashyap, and Drake, 2000, BAAS 196, v32, #54.02), and at the conference on Statistical Challenges in Modem Astronomy (Sourlas, van Dyk, Kashyap, Drake, and Pease, 2003, SCMA 111, Eds. E.D.Feigelson, G.J.Babu, New York:Springer, p489-490). We also described the limitations of one of the most egregiously misused and misapplied statistical tests in astrophysical literature, the F-test for verifying model components (Protassov, van Dyk, Connors, Kashyap, and Siemiginowska, 2002, ApJ, 571,545). Indeed, a search through the ApJ archives turned up 170 papers in the 5 previous years that used the F-test explicitly in some form or the other, and with the vast majority of them not using it correctly! Indeed, looking at just 4 issues of the ApJ in 2001, we found 13 instances of its use, of which nine were demonstrably incorrect. Clearly, it is difficult to understate the importance of this issue. We also worked on speeding up Bayes Blocks and Sparse Bayes Blocks algorithms to make them more tractable for large searches. We also supported staistics students and postdocs in both explicit physics- model-based (spectra with tens of thousands of atomic lines) and "model-free" -- i.e. non-parametric or semi-parametric -- algorithms. Work on using more of the latter is just beginning; while using multi-scale methods for Poisson imaging has come to hition. In fact, "An Image Restoration Technique with Error Estimates", by D. Esch, A. Connors, M. Karovska, and D. van Dyk, was published by ApJ (Esch et a1.2004, ApJ, 610, 1213). The code has been delivered to M. Karovska for CXC; and is available for beta-testing upon request. The other large project we worked on was on the self-consistent modeling of logN-logs curves in the Poisson limit. logN-logs curves are a fundamental tool in the study of source populations, luminosity functions, and cosmological parameters. However, their determination is hampered by statistical effects such as the Eddington bias, incompleteness due to detection efficiency, faint source flux fluctuations, etc. We have develed a new and powerful method using the full Poisson machinery that allows us to model the logN-logs distribution of X-ray sources in a self-consistent manner. Because we properly account for all the above statistical effects, our modeling is valid over the full range of the data, and not just for strong sources, as is normally done. Using a Bayesian approach and modeling the fluxes with known functional forms such as simple or broken power-laws, and conditioning the expected photon counts on the fluxes, the background contamination, effective area, detector vignetting, and detection probability, we can delve deeply into the low counts regime and extend the usefulness of medium sensitivity surveys such as ChAMP by orders of magnitude. The built-in flexibility of the algorithm also allows a simultaneous analysis of multiple datasets. We have applied this analysis to a set a Chandra observations (Sourlas, Kashyap, Zezas, van Dyk, 2004, HEAD #8, #16.32)

Scargle, Jeffrey D.↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

25 ENERGY STORAGE↗
DOE OSTI2017

Maternal exposure to ozone and PM{sub 2.5} and the prevalence of orofacial clefts in four U.S. states

Background: While there is some evidence that maternal exposure to ambient air pollution is associated with orofacial clefts in offspring, the epidemiologic studies have been largely equivocal. We evaluated whether maternal exposure to elevated county-level ambient fine particulate matter with aerodynamic diameter ≤2.5 µm (PM{sub 2.5}) and ozone during early gestation was associated with a higher prevalence of orofacial clefts. Methods: Birth data consisting of 4.7 million births from 2001 to 2007 were obtained from National Birth Defects Prevention Network for four states — Arizona, Florida, New York (excluding New York City), and Texas. The air pollution exposure assessment for gestational weeks 5–10 was based on county-level average concentrations of PM{sub 2.5} and ozone data generated using a Bayesian fusion model available through CDC's Environmental Public Health Tracking Network. Two outcomes were analyzed separately: cleft lip with or without cleft palate, cleft palate alone. In logistic regression analyses, we adjusted for factors that were suspected confounders or modifiers of the association between the prevalence of orofacial clefts and air pollution, i.e., infant sex, race-ethnicity, maternal education, smoking status during pregnancy, whether this was mother's first baby, maternal age. Results: Each 10 µg/m{sup 3} increase in PM{sub 2.5} concentration was significantly associated with cleft palate alone (OR =1.43, 95% CI: 1.11–1.86). There was no significant association between PM{sub 2.5} concentration and cleft lip with or without cleft palate. No associations were observed between ozone exposure and the two outcomes of orofacial clefts. Conclusions: Our study suggests that PM{sub 2.5} significantly increased the risk of cleft palate alone, but did not change the incidence of cleft lip with or without palate. Ozone levels did not correlate with incidence of orofacial clefts. - Highlights: • This is a large study with about 4.7 million births and 7000 orofacial cleft cases. • PM{sub 2.5} significantly increased the risk of cleft palate alone. • PM{sub 2.5} did not change the incidence of cleft lip with or without palate. • Ozone levels did not correlate with incidence of orofacial clefts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The phase-space structure of nearby dark matter as constrained by the SDSS

Previous studies using numerical simulations have demonstrated that the shape of the cosmic web can be described by studying the Lagrangian displacement field. We extend these analyses, showing that it is now possible to perform a Lagrangian description of cosmic structure in the nearby Universe based on large-scale structure observations. Building upon recent Bayesian large-scale inference of initial conditions, we present a cosmographic analysis of the dark matter distribution and its evolution, referred to as the dark matter phase-space sheet, in the nearby universe as probed by the Sloan Digital Sky Survey main galaxy sample. We consider its stretchings and foldings using a tetrahedral tessellation of the Lagrangian lattice. The method provides extremely accurate estimates of nearby density and velocity fields, even in regions of low galaxy density. It also measures the number of matter streams, and the deformation and parity reversals of fluid elements, which were previously thought inaccessible using observations. We illustrate the approach by showing the phase-space structure of known objects of the nearby Universe such as the Sloan Great Wall, the Coma cluster and the Boötes void. We dissect cosmic structures into four distinct components (voids, sheets, filaments, and clusters), using the Lagrangian classifiers DIVA, ORIGAMI, and a new scheme which we introduce and call LICH. Because these classifiers use information other than the sheer local density, identified structures explicitly carry physical information about their formation history. Accessing the phase-space structure of dark matter in galaxy surveys opens the way for new confrontations of observational data and theoretical models. We have made our data products publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In-flight polarization angle calibration for LiteBIRD: blind challenge and cosmological implications

We present a demonstration of the in-flight polarization angle calibration for the JAXA/ISAS second strategic large class mission, LiteBIRD, and estimate its impact on the measurement of the tensor-to-scalar ratio parameter, r, using simulated data. We generate a set of simulated sky maps with CMB and polarized foreground emission, and inject instrumental noise and polarization angle offsets to the 22 (partially overlapping) LiteBIRD frequency channels. Our in-flight angle calibration relies on nulling the EB cross correlation of the polarized signal in each channel. This calibration step has been carried out by two independent groups with a blind analysis, allowing an accuracy of the order of a few arc-minutes to be reached on the estimate of the angle offsets. Both the corrected and uncorrected multi-frequency maps are propagated through the foreground cleaning step, with the goal of computing clean CMB maps. We employ two component separation algorithms, the Bayesian-Separation of Components and Residuals Estimate Tool (B-SeCRET), and the Needlet Internal Linear Combination (NILC). We find that the recovered CMB maps obtained with algorithms that do not make any assumptions about the foreground properties, such as NILC, are only mildly affected by the angle miscalibration. However, polarization angle offsets strongly bias results obtained with the parametric fitting method. Once the miscalibration angles are corrected by EB nulling prior to the component separation, both component separation algorithms result in an unbiased estimation of the r parameter. While this work is motivated by the conceptual design study for LiteBIRD, its framework can be broadly applied to any CMB polarization experiment. In particular, the combination of simulation plus blind analysis provides a robust forecast by taking into account not only detector sensitivity but also systematic effects.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1999

Brain segmentation and the generation of cortical surfaces

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

NASA Discipline Neuroscience↗