Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PCA”

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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

S4: A spatial-spectral model for speckle suppression

High dynamic range imagers aim to block or eliminate light from a very bright primary star in order to make it possible to detect and measure far fainter companions; in real systems, a small fraction of the primary light is scattered, diffracted, and unocculted. We introduce S4, a flexible data-driven model for the unocculted (and highly speckled) light in the P1640 spectroscopic coronagraph. The model uses principal components analysis (PCA) to capture the spatial structure and wavelength dependence of the speckles, but not the signal produced by any companion. Consequently, the residual typically includes the companion signal. The companion can thus be found by filtering this error signal with a fixed companion model. The approach is sensitive to companions that are of the order of a percent of the brightness of the speckles, or up to 10{sup –7} times the brightness of the primary star. This outperforms existing methods by a factor of two to three and is close to the shot-noise physical limit.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

COSMIC EMULATION: FAST PREDICTIONS FOR THE GALAXY POWER SPECTRUM

The halo occupation distribution (HOD) approach has proven to be an effective method for modeling galaxy clustering and bias. In this approach, galaxies of a given type are probabilistically assigned to individual halos in N-body simulations. In this paper, we present a fast emulator for predicting the fully nonlinear galaxy–galaxy auto and galaxy–dark matter cross power spectrum and correlation function over a range of freely specifiable HOD modeling parameters. The emulator is constructed using results from 100 HOD models run on a large ΛCDM N-body simulation, with Gaussian Process interpolation applied to a PCA-based representation of the galaxy power spectrum. The total error is currently ∼1% in the auto correlations and ∼2% in the cross correlations from z = 1 to z = 0, over the considered parameter range. We use the emulator to investigate the accuracy of various analytic prescriptions for the galaxy power spectrum, parametric dependencies in the HOD model, and the behavior of galaxy bias as a function of HOD parameters. Additionally, we obtain fully nonlinear predictions for tangential shear correlations induced by galaxy–galaxy lensing from our galaxy–dark matter cross power spectrum emulator. All emulation products are publicly available at http://www.hep.anl.gov/cosmology/CosmicEmu/emu.html.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Is dark energy evolving?

We look for evidence for the evolution in dark energy density by employing Principal Component Analysis (PCA). Distance redshift data from supernovae and baryon acoustic oscillations (BAO) along with WMAP7 distance priors are used to put constraints on curvature parameter Ω{sub k} and dark energy parameters. The data sets are consistent with a flat Universe. The constraints on the dark energy evolution parameters obtained from supernovae (including CMB distance priors) are consistent with a flat ΛCDM Universe. On the other hand, in the parameter estimates obtained from the addition of BAO data the second principal component, which characterize a non-constant contribution from dark energy, is non-zero at 1σ. This could be a systematic effect and future BAO data holds key to making more robust claims.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Constraints on dark energy from new observations including Pan-STARRS

In this paper, we set the new limits on the equation of state parameter (EoS) of dark energy with the observations of cosmic microwave background radiation (CMB) from Planck satellite, the type Ia supernovae from Pan-STARRS and the baryon acoustic oscillation (BAO). We consider two parametrization forms of EoS: a constant w and time evolving w(a)=w{sub 0}+w{sub a}(1-a). The results show that with a constant EoS, w=-1.141±0.075 68% C.L.), which is consistent with ΛCDM at about 2σ confidence level. For a time evolving w(a) model, we get w{sub 0}=-1.09{sup +0.16}{sub -0.18} 1σ C.L.), w{sub a}=-0.34{sup +0.87}{sub -0.51} 1σ C.L.), and in this case ΛCDM can be comparable with our observational data at 1σ confidence level. In order to do the parametrization independent analysis, additionally we adopt the so called principal component analysis (PCA) method, in which we divide redshift range into several bins and assume w as a constant in each redshift bin (bin-w). In such bin-w scenario, we find that for most of the bins cosmological constant can be comparable with the data, however, there exists few bins which give w deviating from ΛCDM at more than 2σ confidence level, which shows a weak hint for the time evolving behavior of dark energy. To further confirm this hint, we need more data with higher precision.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Effect of the early reionization on the cosmic microwave background and cosmological parameter estimates

The early reionization (ERE) is supposed to be a physical process which happens after recombination, but before the instantaneous reionization caused by the first generation of stars. We investigate the effect of the ERE on the temperature and polarization power spectra of cosmic microwave background (CMB), and adopt principal components analysis (PCA) to model-independently reconstruct the ionization history during the ERE. In addition, we also discuss how the ERE affects the cosmological parameter estimates, and find that the ERE does not impose any significant influences on the tensor-to-scalar ratio r and the neutrino mass at the sensitivities of current experiments. The better CMB polarization data can be used to give a tighter constraint on the ERE and might be important for more precisely constraining cosmological parameters in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

COLLIMATION AND SCATTERING OF THE ACTIVE GALACTIC NUCLEUS EMISSION IN THE SOMBRERO GALAXY

We present an analysis of a data cube of the central region of M104, the Sombrero galaxy, obtained with the GMOS-IFU of the Gemini-South telescope, and report the discovery of collimation and scattering of the active galactic nucleus (AGN) emission in the circumnuclear region of this galaxy. Analysis with PCA Tomography and spectral synthesis revealed the existence of collimation and scattering of the AGN featureless continuum and also of a broad component of the H{alpha} emission line. The collimation and scattering of this broad H{alpha} component was also revealed by fitting the [N II] {lambda}{lambda}6548, 6583 and H{alpha} emission lines as a sum of Gaussian functions. The spectral synthesis, together with a V-I image obtained with the Hubble Space Telescope, showed the existence of circumnuclear dust, which may cause the light scattering. We also identify a dusty feature that may be interpreted as a torus/disk structure. The existence of two opposite regions with featureless continuum (P.A. = -18 Degree-Sign {+-} 13 Degree-Sign and P.A. = 162 Degree-Sign {+-} 13 Degree-Sign ) along a direction perpendicular to the torus/disk (P.A. = 72 Degree-Sign {+-} 14 Degree-Sign ) suggests that this structure is approximately edge-on and collimates the AGN emission. The edge-on torus/disk also hides the broad-line region. The proposed scenario is compatible with the unified model and explains why only a weak broad component of the H{alpha} emission line is visible and also why many previous studies detected no broad H{alpha}. The technique used here proved to be an efficient method not only for detecting scattered light, but also for testing the unified model in low-luminosity AGNs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Revealing Phase Heterogeneity in Vertically Aligned Nanocomposites via Plan-View Electron Energy Loss Spectroscopy

Hydrogen utilization in clean energy technologies is challenged by limited storage and transport within materials, owing to the complex hydrogen kinetics at interfaces [1]. Understanding these interfacial mechanisms at the nanoscale is crucial for developing improved materials for hydrogen applications, particularly proton-conducting fuel cells (PCFCs). Vertically aligned nanocomposites (VANs) grown by pulsed laser deposition (PLD) offer a unique platform for investigating the interfacial effects on hydrogen transport due to their well-defined interfaces parallel to the direction of charge transport [2-4]. To investigate hydrogen transport, the two phases within the VANs were chosen as BaZr 0.9 Y 0.1 O 3-x (BZY), a known proton conductor, and Pr 0.1 Ce 0.9 O 2-x (PCO), a mixed ionic-electronic conductor [5]. This PCO-BZY VANs architecture allows the investigation of how the interface between a proton conductor and a mixed conductor influences hydrogen transport. However, because of the small size of hydrogen, it is difficult to discern the nature of its interactions with interfaces from bulk measurements at the macroscale, thus necessitating nanoscale measurements [6]. Electron energy loss spectroscopy (EELS) allows for nanometer-resolution probing of the local atomic structure and chemistry at the BZY/PCO interface. In this study, plan-view analysis of PCO-BZY VANs films was employed to characterize the structure and phase distribution of the VANs and investigate the interface between the nanostructures. The films were imaged using scanning electron microscopy (SEM) in the Hitachi S-4800 SEM, collecting secondary electron images using mixed upper and lower detectors. Then, plan-view transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) EELS were employed using a JEOL ARM300 microscope operated at 300kV with a Gatan K3 GIF Continuum detector to study the distribution of the BZY and PCO phases through the film. As a result, spectrum images were acquired at a dispersion of 0.18eV per channel and denoised afterward by principal component analysis (PCA) method.

Griffin, Elizabeth [Northwestern University, Evans↗
DOE OSTI2022

The MaNGA firefly Value-Added-Catalogue: resolved stellar populations of 10,010 nearby galaxies

We present the Mapping Nearby Galaxies at Apache Point Observatory survey (MaNGA) FIREFLY Value Added Catalogue (VAC) – a catalogue of ~3.7 million spatially resolved stellar population properties across 10 010 nearby galaxies from the final data release of the MaNGA survey. The full spectral fitting code FIREFLY is employed to derive parameters such as stellar ages, metallicities, stellar and remnant masses, star formation histories, star formation rates, and dust attenuation. In addition to Voronoi-binned measurements, our VAC also provides global properties, such as central values and radial gradients. Two variants of the VAC are available: presenting the results from fits using the M11-MILES and the novel MaStar stellar population models. MaStar allows to constrain the fit over the whole MaNGA wavelength range, extends the age-metallicity parameter space, and uses empirical spectra from the same instrument as MaNGA. The fits employing MaStar models find, on average, slightly younger ages, higher mass-weighted metallicities, and smaller colour excesses. These differences are reduced when matching the wavelength range and converging template grids. We further report that FIREFLY stellar masses are systematically lower by ~0.3 dex than masses from the MaNGA PCA and Pipe3D VACs, but match masses from the NSA best with only ~0.1-dex difference. Finally, we show that FIREFLY stellar ages correlate with spectral index age indicators H δ A and D n (4000), though with a clear additional metallicity dependence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

ABSTRACT Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by using interpolation tables of pre-computed values, at the expense of making significant and sometimes even unjustified approximations. Here, we explore the capacity of machine learning to approximate cooling and heating functions with a generalized radiation field. Specifically, we use the machine learning algorithm XGBoost to predict cooling and heating functions calculated with the photoionization code cloudy at fixed metallicity, using different combinations of photoionization rates as features. We perform a constrained quadratic fit in metallicity to enable a fair comparison with traditional interpolation methods at arbitrary metallicity. We consider the relative importance of various photoionization rates through both a principal component analysis (PCA) and calculation of SHapley Additive exPlanation (shap) values for our XGBoost models. We use feature importance information to select different subsets of rates to use in model training. Our XGBoost models outperform a traditional interpolation approach at each fixed metallicity, regardless of feature selection. At arbitrary metallicity, we are able to reduce the frequency of the largest cooling and heating function errors compared to an interpolation table. We find that the primary bottleneck to increasing accuracy lies in accurately capturing the metallicity dependence. This study demonstrates the potential of machine learning methods such as XGBoost to capture the non-linear behaviour of cooling and heating functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Quantum decimation in Hilbert space: Coarse graining without structure

We introduce a technique to coarse grain quantum states in a finite-dimensional Hilbert space. Our method is distinguished from other approaches by not relying on structures such as a preferred factorization of Hilbert space or a preferred set of operators (local or otherwise) in an associated algebra. Rather, we use the data corresponding to a given set of states, either specified independently or constructed from a single state evolving in time. Our method is based on principle component analysis (PCA), and the resulting coarse-grained quantum states live in a lower-dimensional Hilbert space whose basis is defined using the underlying (isometric embedding) transformation of the set of fine-grained states we wish to coarse grain. Physically, the transformation can be interpreted to be an “entanglement coarse-graining” scheme that retains most of the global, useful entanglement structure of each state, while needing fewer degrees of freedom for its reconstruction. This scheme could be useful for efficiently describing collections of states whose number is much smaller than the dimension of Hilbert space, or a single state evolving over time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Active learning approach to simulations of strongly correlated matter with the ghost Gutzwiller approximation

Quantum embedding (QE) methods such as the ghost Gutzwiller approximation (gGA) offer a powerful approach to simulating strongly correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly correlated materials. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2013

Polarization-resolved second-harmonic generation microscopy as a method to visualize protein-crystal domains

Polarization-resolved second-harmonic generation microscopy is applied to the identification of multiple crystalline domains within protein-crystal samples. Polarization-resolved second-harmonic generation (PR-SHG) microscopy is described and applied to identify the presence of multiple crystallographic domains within protein-crystal conglomerates, which was confirmed by synchrotron X-ray diffraction. Principal component analysis (PCA) of PR-SHG images resulted in principal component 2 (PC2) images with areas of contrasting negative and positive values for conglomerated crystals and PC2 images exhibiting uniformly positive or uniformly negative values for single crystals. Qualitative assessment of PC2 images allowed the identification of domains of different internal ordering within protein-crystal samples as well as differentiation between multi-domain conglomerated crystals and single crystals. PR-SHG assessments of crystalline domains were in good agreement with spatially resolved synchrotron X-ray diffraction measurements. These results have implications for improving the productive throughput of protein structure determination through early identification of multi-domain crystals.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Zirconium tetrachloride revisited

We present that zirconium tetrachloride, ZrCl 4 , is a strategic material with wide-ranging applications. Until now, only one crystallographic study on ZrCl 4 has been reported [Krebs (1970). Z. Anorg. Allg. Chem. 378 , 263–272] and that was more than 40 years ago. The compound used for the previous determination was prepared from ZrO 2 and Cl 2 –CCl 4 , and single-crystal X-ray diffraction (SCXRD) studies on ZrCl 4 obtained from Zr metal have not yet been reported. In this context, we prepared ZrCl 4 from the reaction of Zr metal and Cl 2 gas in a sealed tube and investigated its structure at 100, 150, 200, 250, and 300 K. At 300 K, the SCXRD analysis indicates that ZrCl 4 crystallizes in the orthorhombic space group Pca 2 1 [ a = 6.262 (9), b = 7.402 (11), c = 12.039 (17) Å, and V = 558.0 (14) Å 3 ] and consists of infinite zigzag chains of edge-sharing ZrCl 6 octahedra. This chain motif is similar to that observed previously in ZrCl 4 , but the structural parameters and space group differ. Finally, in the temperature range 100–300 K, no phase transformation was identified, while elongation of intra-chain Zr...Zr [3.950 (1) Å at 100 K and 3.968 (5) Å at 300 K] and inter-chain Cl...Cl [3.630 (3) Å at 100 K and 3.687 (9) Å at 300 K] distances occurred.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microfocus diffraction from different regions of a protein crystal: structural variations and unit-cell polymorphism

Real macromolecular crystals can be non-ideal in a myriad of ways. This often creates challenges for structure determination, while also offering opportunities for greater insight into the crystalline state and the dynamic behavior of macromolecules. To evaluate whether different parts of a single crystal of a dynamic protein, EutL, might be informative about crystal and protein polymorphism, a microfocus X-ray synchrotron beam was used to collect a series of 18 separate data sets from non-overlapping regions of the same crystal specimen. A principal component analysis (PCA) approach was employed to compare the structure factors and unit cells across the data sets, and it was found that the 18 data sets separated into two distinct groups, with large R values (in the 40% range) and significant unit-cell variations between the members of the two groups. This categorization mapped the different data-set types to distinct regions of the crystal specimen. Atomic models of EutL were then refined against two different data sets obtained by separately merging data from the two distinct groups. A comparison of the two resulting models revealed minor but discernable differences in certain segments of the protein structure, and regions of higher deviation were found to correlate with regions where larger dynamic motions were predicted to occur by normal-mode molecular-dynamics simulations. The findings emphasize that large spatially dependent variations may be present across individual macromolecular crystals. This information can be uncovered by simultaneous analysis of multiple partial data sets and can be exploited to reveal new insights about protein dynamics, while also improving the accuracy of the structure-factor data ultimately obtained in X-ray diffraction experiments.

Thompson, Michael C. (ORCID:0000000260992027)↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2021

Principal Component Analysis of Nuclear Cable Insulation Subjected to Elevated Temperature and Gamma Radiation

In nuclear power plants (NPPs), the aging of electrical cable insulation occurs due to elevated temperature, ionizing radiation, and other environmental factors. To ensure the safe and efficient operation of NPPs, determination of key indicators of cable aging is critical to predict the remaining useful lifetime of electrical cable insulation. In this work, the effects of simultaneous and sequential thermal and gamma radiation on the aging of cross-linked polyethylene (XLPE) electrical cable insulation are investigated. The chemical changes of the insulation were monitored non-destructively through the use of Fourier transform infrared (FTIR) spectroscopy. The FTIR spectra were measured stepwise after predetermined exposure intervals, with a total irradiation dose up to 320 kGy at a dose rate of 300 Gy/hr in two exposure scenarios; simultaneously aged samples were heated at 150 °C during irradiation, while sequentially aged samples were heated at 150 °C for designated durations followed by corresponding times of irradiation at ambient temperature. A data-driven approach using principal component analysis (PCA) was developed to highlight changes in the carbonyl region of the infrared spectra of the aged samples due to oxidation and to differentiate oxidation rates under the simultaneous and sequential exposure conditions. Findings indicate that the sequential aging scenario may be more conservative than the simultaneous aging scenario for XLPE electrical cable insulation.

Li, Donghui↗