Search NASA⌕ Search

SEARCH · Search NASA

Results for “Variance analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated Hybrid Variance Reduction on Advanced Architectures in the Shift Monte Carlo Code

Monte Carlo transport methods are the most accurate schemes for solving problems with complex energy and spatial features, but they come with a high computational cost. Although hybrid methods have enabled the use of Monte Carlo transport for a large class of problems, they still require significant computing resources. Modern multicore CPUs with large numbers of compute cores and graphical processing units (GPUs) provide opportunities to optimize the memory and run-time costs of hybrid Monte Carlo methods. This paper documents the development and analysis of three Monte Carlo transport algorithms that support hybrid transport using the consistent adjoint-driven importance sampling (CADIS) and forward-weighted CADIS methods in the Shift Monte Carlo code: history-based transport using static and dynamic threading on multicore CPUs and event-based transport enabling weight window tracking on GPUs. The results are shown for two challenging hybrid problems on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. The results show that all three methods yield good performance and enable solutions of difficult fixed-source transport problems in less than 2 min on 20 nodes of Frontier. Dynamic threading was observed to give up to 20% better scaling behavior than static threading. Moreover, the AMD Instinct 250X GPU was found to give 9 to 11 times greater throughput per graphics compute die than the best CPU performance. In conclusion, additional opportunities for optimization of hybrid transport on GPUs are discussed.

Denovo↗
DOE OSTI2016

Identification of PM{sub 10} characteristics involved in cellular responses in human bronchial epithelial cells (Beas-2B)

Notwithstanding evidence is present that physicochemical characteristics of ambient particles attribute to adverse health effects, there is still some lack of understanding in this complex relationship. At this moment it is not clear which properties (such as particle size, chemical composition) or sources of the particles are most relevant for health effects. This study investigates the in vitro toxicity of PM{sub 10} in relation to PM chemical composition, black carbon (BC), endotoxin content and oxidative potential (OP). In 2013–2014 PM{sub 10} was sampled (24 h sampling, 108 sampling days) in ambient air at three sites in Flanders (Belgium) with different pollution characteristics: an urban traffic site (Borgerhout), an industrial area (Zelzate) and a rural background location (Houtem). To characterize the toxic potential of PM{sub 10}, airway epithelial cells (Beas-2B cells) have been exposed to particles in vitro. Different endpoints were studied including cell damage and death (cell viability) using the Neutral red Uptake assay, the production of pro-inflammatory molecules by interleukin 8 (IL-8) induction and DNA-damaging activity using the FPG-modified Comet assay. The endotoxin levels in the collected samples were analysed and the capacity of PM{sub 10} particles to produce reactive oxygen species (OP) was evaluated by electron paramagnetic resonance (EPR) spectroscopy. Chemical characteristics of PM{sub 10} (BC, As, Cd, Cr, Cu, Mn, Ni, Pb, Zn) and meteorological conditions were recorded on the sampling days. PM{sub 10} particles exhibited dose-dependent cytotoxicity in Beas-2B cells and were found to significantly induce the release of IL-8 in samples from the three locations. Oxidatively damaged DNA was observed in exposed Beas-2B cells. Endotoxin levels above the detection limit were detected in half of the samples. OP was measurable in all samples. Associations between PM{sub 10} characteristics and biological effects of PM{sub 10} were assessed by single and multiple regression analyses. The reduction in cell viability was significantly correlated with BC, Cd and Pb. The induction of IL-8 in Beas-2B cells was significantly associated with Cu, Ni and Zn and endotoxin. Endotoxin levels explained 33% of the variance in IL-8 induction. A significant interaction between ambient temperature and endotoxin on the pro-inflammatory activity was seen. No association was found between OP and the cellular responses. This study supports the hypothesis that, on an equal mass basis, PM{sub 10} induced biological effects differ due to differences in PM{sub 10} characteristics. Metals (Cd, Cu, Ni and Zn), BC, and endotoxin were among the main determinants for the observed biological responses. - Highlights: • On an equal mass basis, PM{sub 10} sampled at an urban, rural and industrial site induced different cellular effects in Beas-2B. • Endotoxin levels and oxidative potential (OP) were analysed in the PM{sub 10} samples. • Black carbon, cadmium and lead were correlated with decreased cell viability. • Endotoxin levels explained the majority of the variance in il-8 induction. • Oxidatively damaged DNA was observed in all the samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Requirements on the gain calibration for LiteBIRD polarisation data with blind component separation

The detection of primordial B modes of the cosmic microwave background (CMB) could provide information about the early stages of the Universe's evolution. The faintness of this signal requires exquisite calibration accuracy and control of instrumental systematic effects which otherwise could bias the measurements. In this work, we study the impact of an imperfect relative polarisation gain calibration on the recovered value of the tensor-to-scalar ratio r for the LiteBIRD experiment, through the application of the blind Needlet Internal Linear Combination (NILC) foreground-cleaning method. We derive requirements on the relative calibration accuracy of the overall polarisation gain (Δg ν ) for each LiteBIRD frequency channel. Our results show that minimum variance techniques, as NILC, are less sensitive to systematic gain calibration uncertainties compared to a parametric approach, if the latter is not equipped with a proper modelling of these instrumental effects. In this study, the most stringent requirements are found in the channels where the CMB signal is relatively brighter, with the tightest constraints at 166 GHz (Δg ν ≈ 0.16%). This differs from the outcome of an analogous analysis performed with a parametric method, where the tightest requirements are obtained for the foreground-dominated channels. Gain calibration uncertainties, corresponding to the derived requirements, are then simultaneously propagated into all frequency channels. By doing so, we find that the overall impact on estimated r is lower than the total gain systematic budget for LiteBIRD approximately by a factor 5, due to the correlations of the impacts of gain calibration uncertainties in different frequency channels. In order to decouple the systematic effect from the specific choice of the model, we derive the requirements assuming constant spectral parameters for the foreground emission. To assess the robustness of the obtained results against more realistic scenarios, we repeat the analysis assuming sky models of intermediate and high complexity. In these further cases, we adopt an optimised NILC pipeline, called the Multi-Clustering NILC (MC-NILC). We find that the impact of gain calibration uncertainties on r is lower than the LiteBIRD gain systematics budget for the intermediate-complexity sky model. For the high-complexity case, instead, it would be necessary to tighten the requirements by a factor 1.8.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Estimation and Uncertainty Analysis of Impacts of Future Heat Waves on Mortality in the Eastern United States

Background: It is anticipated that climate change will influence heat-related mortality in the future. However, the estimation of excess mortality attributable to future heat waves is subject to large uncertainties, which have not been examined under the latest greenhouse gas emission scenarios. Objectives: We estimated the future heat wave impact on mortality in the eastern United States (~ 1,700 counties) under two Representative Concentration Pathways (RCPs) and analyzed the sources of uncertainties. Methods Using dynamically downscaled hourly temperature projections in 2057-2059, we calculated heat wave days and episodes based on four heat wave metrics, and estimated the excess mortality attributable to them. The sources of uncertainty in estimated excess mortality were apportioned using a variance-decomposition method. Results: In the eastern U.S., the excess mortality attributable to heat waves could range from 200-7,807 with the mean of 2,379 persons/year in 2057-2059. The projected average excess mortality in RCP 4.5 and 8.5 scenarios was 1,403 and 3,556 persons/year, respectively. Excess mortality would be relatively high in the southern and eastern coastal areas. The major sources of uncertainty in the estimates are relative risk of heat wave mortality, the RCP scenarios, and the heat wave definitions. Conclusions: The estimated mortality risks from future heat waves are likely an order of magnitude higher than its current level and lead to thousands of deaths each year under the RCP8.5 scenario. The substantial spatial variability in estimated county-level heat mortality suggests that effective mitigation and adaptation measures should be developed based on spatially resolved data.

climate change↗
DOE OSTI2015

INTERPRETING THE UNRESOLVED INTENSITY OF COSMOLOGICALLY REDSHIFTED LINE RADIATION

Intensity mapping experiments survey the spectrum of diffuse line radiation rather than detect individual objects at high signal-to-noise ratio. Spectral maps of unresolved atomic and molecular line radiation contain three-dimensional information about the density and environments of emitting gas and efficiently probe cosmological volumes out to high redshift. Intensity mapping survey volumes also contain all other sources of radiation at the frequencies of interest. Continuum foregrounds are typically ∼10{sup 2}–10{sup 3} times brighter than the cosmological signal. The instrumental response to bright foregrounds will produce new spectral degrees of freedom that are not known in advance, nor necessarily spectrally smooth. The intrinsic spectra of foregrounds may also not be well known in advance. We describe a general class of quadratic estimators to analyze data from single-dish intensity mapping experiments and determine contaminated spectral modes from the data themselves. The key attribute of foregrounds is not that they are spectrally smooth, but instead that they have fewer bright spectral degrees of freedom than the cosmological signal. Spurious correlations between the signal and foregrounds produce additional bias. Compensation for signal attenuation must estimate and correct this bias. A successful intensity mapping experiment will control instrumental systematics that spread variance into new modes, and it must observe a large enough volume that contaminant modes can be determined independently from the signal on scales of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogues from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multiband deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalogue created from N-body simulations. Here, it yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of σ Δz = 0.007, which is a 60 per cent improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Effect of compressibility on strong shock and turbulence interactions

The interactions between turbulent flows and shock waves are important in many natural processes as well as scientific and engineering applications. One of the fundamental building blocks in these complex processes and applications is the canonical problem of isotropic turbulence and a normal shock. Unfortunately, even this fundamental problem is not well understood. Recent direct numerical simulation (DNS) results of perfect gas flow showed some new trends in turbulent statistics as mean Mach number is increased. In this paper, we first conduct extensive DNS studies on canonical strong shock and turbulence interaction problem of perfect gas flow with mean Mach numbers ranging from 2 to 30, with the emphasis on investigating the effect of compressibility. The results show that maximum values of variance of streamwise vorticity fluctuations first increase and then decrease as shock strength is increased. The peak of streamwise vorticity fluctuations is observed for shock and turbulence interactions with Mach 2.8 shock. For stronger than Mach 2.8 shocks, there is a decrease in streamwise vorticity fluctuations. The amplification of Reynolds stress R11 decreases as mean Mach number is increased till 8.8, which is consistent with findings of linear interaction analysis. This trend, however, reverses as shock strength is increased beyond Mach 8.8. For stronger than Mach 8.8 shocks, Reynolds stress R11 is amplified as mean Mach number keeps increasing. Since gas temperature increases dramatically after strong shocks, we are also working on DNS of nonequilibrium flow, where non-equilibrium flow effects including internal energy excitations, translation-vibration energy relaxation, and chemical reactions among different species are considered based on the 5-species air chemistry and recently thermal property models. The code and corresponding thermo-chemical models have been tested on two cases of non-equilibrium flow over cylinders.

Wang, Xiaowen↗
DOE OSTI2017

HARMONIC SPACE ANALYSIS OF PULSAR TIMING ARRAY REDSHIFT MAPS

In this paper, we propose a new framework for treating the angular information in the pulsar timing array (PTA) response to a gravitational wave (GW) background based on standard cosmic microwave background techniques. We calculate the angular power spectrum of the all-sky gravitational redshift pattern induced at the Earth for both a single bright source of gravitational radiation and a statistically isotropic, unpolarized Gaussian random GW background. The angular power spectrum is the harmonic transform of the Hellings and Downs curve. We use the power spectrum to examine the expected variance in the Hellings and Downs curve in both cases. Finally, we discuss the extent to which PTAs are sensitive to the angular power spectrum and find that the power spectrum sensitivity is dominated by the quadrupole anisotropy of the gravitational redshift map.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Enhancing capillary trapping effectiveness through proper time scheduling of injection of supercritical CO 2 in heterogeneous formations

The impact of geologic heterogeneity on capillary trapping of supercritical CO 2 (scCO 2 ) has been recognized and appraised through laboratory experimentation and modeling. However, how different injection strategies can be optimized to improve capillary trapping has not received adequate attention. Here, we present a study based on stochastic analysis to show the impact of injection scheduling on capillary trapping of scCO 2 in heterogeneous geological formations. Improvement of trapping efficiency consists of maximizing the trapped volume within a predefined secure zone of the storage formation with the goal of avoiding plume intersection with potential leakage pathways. Using knowledge acquired from physical experiments in intermediate-scale tanks with controlled heterogeneity, we conduct numerical simulations following a stochastic approach to extend the observed outcomes to a series of equally probable permeability scenarios. Our simulations involve the same combination of surrogate fluids that are used in the experiments to mimic viscosity and density contrasts between scCO 2 and brine. To account for uncertainty of formation heterogeneity, several permeability fields with three different variances and two horizontal correlation lengths are generated. The same volume of scCO 2 is emplaced using four different modes of injection scheduling. Results suggest that injection strategies aimed at enhancing capillary trapping of scCO 2 and increasing the probability of constraining the plume within a secure zone are strictly related to the specific heterogeneity of the reservoir. Finally, this preliminary theoretical finding suggests the potential for maximizing secure capillary trapping through the proper selection of injection schedule to fit the formation heterogeneity.

58 GEOSCIENCES↗
DOE OSTI2018

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Uncertainty Quantification and Global Sensitivity Analysis of Subsurface Flow Parameters to Gravimetric Variations During Pumping Tests in Unconfined Aquifers

We study the contribution of typically uncertain subsurface flow parameters to gravity changes that can be recorded during pumping tests in unconfined aquifers. We do so in the framework of a Global Sensitivity Analysis and quantify the effects of uncertainty of such parameters on the first four statistical moments of the probability distribution of gravimetric variations induced by the operation of the well. System parameters are grouped into two main categories, respectively, governing groundwater flow in the unsaturated and saturated portions of the domain. We ground our work on the three-dimensional analytical model proposed by Mishra and Neuman (2011), which fully takes into account the richness of the physical process taking place across the unsaturated and saturated zones and storage effects in a finite radius pumping well. The relative influence of model parameter uncertainties on drawdown, moisture content, and gravity changes are quantified through (a) the Sobol' indices, derived from a classical decomposition of variance and (b) recently developed indices quantifying the relative contribution of each uncertain model parameter to the (ensemble) mean, skewness, and kurtosis of the model output. Our results document (i) the importance of the effects of the parameters governing the unsaturated flow dynamics on the mean and variance of local drawdown and gravity changes; (ii) the marked sensitivity (as expressed in terms of the statistical moments analyzed) of gravity changes to the employed water retention curve model parameter, specific yield, and storage, and (iii) the influential role of hydraulic conductivity of the unsaturated and saturated zones to the skewness and kurtosis of gravimetric variation distributions. The observed temporal dynamics of the strength of the relative contribution of system parameters to gravimetric variations suggest that gravity data have a clear potential to provide useful information for estimating the key hydraulic parameters of the system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

An analysis of the uncertainty and bias in DCE-MRI measurements using the spoiled gradient-recalled echo pulse sequence

Purpose: The pharmacokinetic parameters derived from dynamic contrast-enhanced (DCE) MRI have been used in more than 100 phase I trials and investigator led studies. A comparison of the absolute values of these quantities requires an estimation of their respective probability distribution function (PDF). The statistical variation of the DCE-MRI measurement is analyzed by considering the fundamental sources of error in the MR signal intensity acquired with the spoiled gradient-echo (SPGR) pulse sequence. Methods: The variance in the SPGR signal intensity arises from quadrature detection and excitation flip angle inconsistency. The noise power was measured in 11 phantoms of contrast agent concentration in the range [0–1] mM (in steps of 0.1 mM) and in onein vivo acquisition of a tumor-bearing mouse. The distribution of the flip angle was determined in a uniform 10 mM CuSO{sub 4} phantom using the spin echo double angle method. The PDF of a wide range of T1 values measured with the varying flip angle (VFA) technique was estimated through numerical simulations of the SPGR equation. The resultant uncertainty in contrast agent concentration was incorporated in the most common model of tracer exchange kinetics and the PDF of the derived pharmacokinetic parameters was studied numerically. Results: The VFA method is an unbiased technique for measuringT1 only in the absence of bias in excitation flip angle. The time-dependent concentration of the contrast agent measured in vivo is within the theoretically predicted uncertainty. The uncertainty in measuring K{sup trans} with SPGR pulse sequences is of the same order, but always higher than, the uncertainty in measuring the pre-injection longitudinal relaxation time (T1{sub 0}). The lowest achievable bias/uncertainty in estimating this parameter is approximately 20%–70% higher than the bias/uncertainty in the measurement of the pre-injection T1 map. The fractional volume parameters derived from the extended Tofts model were found to be extremely sensitive to the variance in signal intensity. The SNR of the pre-injection T1 map indicates the limiting precision with which K{sup trans} can be calculated. Conclusions: Current small-animal imaging systems and pulse sequences robust to motion artifacts have the capacity for reproducible quantitative acquisitions with DCE-MRI. In these circumstances, it is feasible to achieve a level of precision limited only by physiologic variability.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Scalable Combinatorial Tools for Health Disparities Research

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.

Environmental Sciences & Ecology↗
DOE OSTI2024

Ensemble modeling of global lake evaporation under climate change

Global projections of lake evaporation are typically based on simulations using single mechanistic models. However, because of its complex interactions with various lake physical properties, environmental and anthropogenic drivers, lake evaporation is highly variable and sensitive to the choice of model used. In this study, we present a multi-model analysis to investigate differences across global simulations of lake evaporation during the warm-season using three different lake models driven by outputs from four general circulation models (GCM) (i.e. 12 model combinations in total) for historic and future scenarios. Our results suggest substantial differences among lake-climate model simulations of lake evaporation. These differences varied throughout the 20th and 21st century, with model driver data explaining 74% of the variance in future projections of warm- season lake evaporation. Our projections indicate that, by the end of the 21st century (2070–2099), global annual lake evaporation rates will increase by 10–27% under Representative Concentration Pathways (RCPs) 2.6–8.5. Finally, we highlight the importance of using a multi-model approach for the prediction of future global lake evaporation responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

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↗
DOE OSTI2025

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗
DOE OSTI2020

Medium amplitude parallel superposition (MAPS) rheology. Part 2: Experimental protocols and data analysis

An experimental protocol is developed to directly measure the new material functions revealed by medium amplitude parallel superposition (MAPS) rheology. This protocol measures the medium amplitude response of a material to a simple shear deformation composed of three sine waves at different frequencies, revealing a rich dataset consisting of up to 19 measurements of the third-order complex modulus at distinct three-frequency coordinates. We discuss how the choice of input frequencies influences the features of the MAPS domain studied by the experiment. A polynomial interpolation method for reducing the bias of measured values from spectral leakage and reducing variance due to noise is discussed, including a derivation of the optimal range of amplitudes for the input signal. This leads to the conclusion that conducting the experiment in a stress-controlled fashion possesses a distinct advantage to the strain-controlled mode. The experimental protocol is demonstrated through measurements of the MAPS response of a model complex fluid: a surfactant solution of wormlike micelles. The resulting dataset is indeed large and feature-rich, while still acquired in a time comparable to similar medium amplitude oscillatory shear (MAOS) experiments. We demonstrate that the data represent measurements of an intrinsic material function by studying its internal consistency, compatibility with low-frequency predictions for Coleman–Noll simple fluids, and agreement with data obtained via MAOS amplitude sweeps. Finally, the data are compared to predictions from the corotational Maxwell model to demonstrate the power of MAPS rheology in determining whether a constitutive model is consistent with a material’s time-dependent response.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2018

Reconstruction of the remote dipole and quadrupole fields from the kinetic Sunyaev Zel’dovich and polarized Sunyaev Zel’dovich effects

The kinetic Sunyaev Zel’dovich (kSZ) and polarized Sunyaev Zel’dovich (pSZ) effects are temperature and polarization anisotropies induced by the scattering of cosmic microwave background (CMB) photons from structure in the post-reionization universe. In the case of the kSZ effect, small angular scale anisotropies in the optical depth are modulated by the CMB dipole field, i.e., the CMB dipole observed at each spacetime point, which is sourced by the primordial dipole and especially the local peculiar velocity. In the case of the pSZ effect, similar small-scale anisotropies are modulated by the CMB quadrupole field, which receives contributions from both scalar and tensor modes. Statistical anisotropies in the cross-correlations of CMB temperature and polarization with tracers of the inhomogeneous distribution of electrons provide a means of isolating and reconstructing the dipole and quadrupole fields. In this paper, we present a set of unbiased minimum variance quadratic estimators for the reconstruction of the dipole and quadrupole fields, and forecast the ability of future CMB experiments and large-scale structure surveys to perform this reconstruction. Furthermore, consistent with previous work, we find that a high fidelity reconstruction of the dipole and quadrupole fields over a variety of scales is indeed possible, and demonstrate the sensitivity of the pSZ effect to primordial tensor modes. Using a principle component analysis, we estimate how many independent modes could be accessed in such a reconstruction. We also comment on a few first applications of a detection of the dipole and quadrupole fields, including a reconstruction of the primordial contribution to our locally observed CMB dipole, a test of statistical homogeneity on large scales from the first modes of the quadrupole field, and a reconstruction technique for the primordial potential on the largest scales.

79 ASTRONOMY AND ASTROPHYSICS↗