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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Task Analytic Models to Guide Analysis and Design: Use of the Operator Function Model to Represent Pilot-Autoflight System Mode Problems

Task-analytic models structure essential information about operator interaction with complex systems, in this case pilot interaction with the autoflight system. Such models serve two purposes: (1) they allow researchers and practitioners to understand pilots' actions; and (2) they provide a compact, computational representation needed to design 'intelligent' aids, e.g., displays, assistants, and training systems. This paper demonstrates the use of the operator function model to trace the process of mode engagements while a pilot is controlling an aircraft via the, autoflight system. The operator function model is a normative and nondeterministic model of how a well-trained, well-motivated operator manages multiple concurrent activities for effective real-time control. For each function, the model links the pilot's actions with the required information. Using the operator function model, this paper describes several mode engagement scenarios. These scenarios were observed and documented during a field study that focused on mode engagements and mode transitions during normal line operations. Data including time, ATC clearances, altitude, system states, and active modes and sub-modes, engagement of modes, were recorded during sixty-six flights. Using these data, seven prototypical mode engagement scenarios were extracted. One scenario details the decision of the crew to disengage a fully automatic mode in favor of a semi-automatic mode, and the consequences of this action. Another describes a mode error involving updating aircraft speed following the engagement of a speed submode. Other scenarios detail mode confusion at various phases of the flight. This analysis uses the operator function model to identify three aspects of mode engagement: (1) the progress of pilot-aircraft-autoflight system interaction; (2) control/display information required to perform mode management activities; and (3) the potential cause(s) of mode confusion. The goal of this paper is twofold: (1) to demonstrate the use of the operator functio model methodology to describe pilot-system interaction while engaging modes And monitoring the system, and (2) to initiate a discussion of how task-analytic models might inform design processes. While the operator function model is only one type of task-analytic representation, the hypothesis of this paper is that some type of task analytic structure is a prerequisite for the design of effective human-automation interaction.

Degani, Asaf↗
NASA NTRSDate not supplied

Adaptive Learning for Reliability Analysis using Support Vector Machines

A novel algorithm is presented for adaptive learning of an unknown function that separates two regions of a domain.In the context of reliability analysis these two regions represent the failure domain, where a set of constraints or requirements are violated, and a safe domain where they are satisfied. The Limit State Function (LSF) separates these two regions. Evaluating the constraints for a given parameter point requires the evaluation of a computational model that may well be expensive. For this reason we wish to construct a meta-model that can estimate the LSFas accurately as possible, using only a limited amount of training data. This work presents an adaptive strategy employing a Support Vector Machine (SVM) as a meta-model to provide a semi-algebraic approximation of the LSF.We describe an optimization process that is used to select informative parameter points to add to training data at each iteration to improve the accuracy of this approximation. A formulation is introduced for bounding the predictions of the meta-model; in this way we seek to incorporate this aspect of Gaussian Process Models (GPMs) within anSVM meta-model. Finally, we apply our algorithm to two benchmark test cases, demonstrating performance that is comparable with, if not superior, to a standard technique for reliability analysis that employs GPMs

Adaptive learning↗
DOE OSTI2018

Comparative genomics and physiology of the genus Methanohalophilus , a prevalent methanogen in hydraulically fractured shale

Summary About 60% of natural gas production in the United States comes from hydraulic fracturing of unconventional reservoirs, such as shales or organic‐rich micrites. This process inoculates and enriches for halotolerant microorganisms in these reservoirs over time, resulting in a saline ecosystem that includes methane producing archaea. Here, we survey the biogeography of methanogens across unconventional reservoirs, and report that members of genus Methanohalophilus are recovered from every hydraulically fractured unconventional reservoir sampled by metagenomics. We provide the first genomic sequencing of three isolate genomes, as well as two metagenome assembled genomes (MAGs). Utilizing six other previously sequenced isolate genomes and MAGs, we perform comparative analysis of the 11 genomes representing this genus. This genomic investigation revealed distinctions between surface and subsurface derived genomes that are consistent with constraints encountered in each environment. Genotypic differences were also uncovered between isolate genomes recovered from the same well, suggesting niche partitioning among closely related strains. These genomic substrate utilization predictions were then confirmed by physiological investigation. Fine‐scale microdiversity was observed in CRISPR‐Cas systems of Methanohalophilus , with genomes from geographically distinct unconventional reservoirs sharing spacers targeting the same viral population. These findings have implications for augmentation strategies resulting in enhanced biogenic methane production in hydraulically fractured unconventional reservoirs.

Borton, Mikayla A.↗
DOE OSTI2024

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure–property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co 3 O 4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the sizedependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an “onset radius,” which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Transverse Densities of Octet Baryons from Chiral Effective Field Theory

Transverse densities describe the distribution of charge and current at fixed light-front time and provide a frame-independent spatial representation of hadrons as relativistic systems. In this paper, we calculate the transverse densities of the octet baryons at peripheral distances b=O(M π -1 ) in an approach that combines chiral effective field theory (χχEFT) and dispersion analysis. The densities are represented as dispersive integrals of the imaginary parts of the baryon electromagnetic form factors in the timelike region (spectral functions). The spectral functions on the two-pion cut at t>4M$2\atop{π}$ are computed using relativistic χEFT with octet and decuplet baryons in the extended on-mass-shell renormalization scheme. The calculations are extended into the ρ-meson mass region using a dispersive method that incorporates the timelike pion form-factor data. The approach allows us to construct densities at distances b>1 fm with controlled uncertainties. Finally, our results provide insight into the peripheral structure of nucleons and hyperons and can be compared with empirical densities and lattice-QCD calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Nekbone performance on GPUs with OpenACC and CUDA Fortran implementations

We present a hybrid GPU implementation and performance analysis of Nekbone, which represents one of the core kernels of the incompressible Navier–Stokes solver Nek5000. The implementation is based on OpenACC and CUDA Fortran for local parallelization of the compute-intensive matrix–matrix multiplication part, which significantly minimizes the modification of the existing CPU code while extending the simulation capability of the code to GPU architectures. Our discussion includes the GPU results of OpenACC interoperating with CUDA Fortran and the gather–scatter operations with GPUDirect communication. We demonstrate performance of up to 552 Tflops on 16, 384 GPUs of the OLCF Cray XK7 Titan.

Computer Science↗
DOE OSTI2019

Charging the Johannsen–Psaltis spacetime

In this paper we develop the charged version of the spacetime proposed by Johannsen and Psaltis. Rotation is introduced in the deformed Reissner–Nordström spacetime by complex coordinate transformation. The event horizon and Killing horizon are studied. Killing horizons are represented graphically also. The analysis of the determinant of the metric shows that the spacetime does not have Lorentz violating regions. Similarly, from the study of the closed time-like curves, we see that no such curves exist outside the central body. Expressions for the energy and angular momentum for a particle on the equatorial plane are determined. Location of the circular photon orbits and innermost stable circular orbits is also computed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing

As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.

Marian, Jaime [Univ. of California, Los Angeles, C↗
DOE OSTI2018

Four different dimensional Zn(II) coordination polymers as fluorescent sensor for detecting Hg{sup 2+}, Cr{sub 2}O{sub 7}{sup 2-} in aqueous solution

Highlights: • Four Zn-based coordination polymers were solvethermally synthesized. • Compound 1 and 2 could mutual conversion between different solvent ratios. • Both 1 and 2 exhibit high selectivity for Cr{sub 2}O{sub 7}{sup 2-} and Hg{sup 2+} ions in aqueous solution. • The mechanism of sensing properties were discussed in details. - Abstract: Four Zn(II) coordination polymers (CPs), [Zn(4-pzpt){sub 2}(H{sub 2}O)]{sub n} (1), {[Zn(4-pzpt)_2]·CH_3OH}{sub n} (2), [Zn{sub 2}(4-pzpt){sub 2}(suc)(H{sub 2}O)]{sub n} (3) and [Zn{sub 2}(4-pzpt){sub 2}(ndc)]{sub n} (4) were obtained from 3-(pyridin-4-yl)-5-(pyrazin-2-yl)-1H-1,2,4-triazole (4-Hpzpt), 1,4-Naphthalenedicarboxylic acid (H{sub 2}ndc) and succinic acid (H{sub 2}suc) ligands. Structural analysis reveals that 1 represents a five coordinated 0D structure. 2 possess a network featuring 2D 4{sup 4} grid-like layers structure. 3 displays a network featuring 2D (6{sup 3})-hcb layered structure. 4 shows a rare 3D (4,4)-connected topology with a Schläfli symbol of (4.6{sup 4.}7){sub 2}(6{sup 4.}7{sup 2}). Complexes 1 and 2 can mutual conversion between different solvent ratios. Luminescence properties reveal that 1 and 2 exhibit excellent selectivity for Hg{sup 2+} and Cr{sub 2}O{sub 7}{sup 2-} ions in aqueous solution, in which suggests that 1 and 2 is a promising multifunctional sensing materials. The possible sensing mechanism is also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Peripheral transverse densities of the baryon octet from chiral effective field theory and dispersion analysis

The baryon electromagnetic form factors are expressed in terms of two-dimensional densities describing the distribution of charge and magnetization in transverse space at fixed light-front time. In this paper, we calculate the transverse densities of the spin-1/2 flavor-octet baryons at peripheral distances b=O(M$-1\atop{π}$) using methods of relativistic chiral effective field theory (χ EFT) and dispersion analysis. The densities are represented as dispersive integrals over the imaginary parts of the form factors in the timelike region (spectral functions). The isovector spectral functions on the two-pion cut t > 4 M$2\atop{π}$ are calculated using relativistic χEFT including octet and decuplet baryons. The χEFT calculations are extended into the ρ meson mass region using an N/D method that incorporates the pion electromagnetic form factor data. The isoscalar spectral functions are modeled by vector meson poles. We compute the peripheral charge and magnetization densities in the octet baryon states, estimate the uncertainties, and determine the quark flavor decomposition. Finally, the approach can be extended to baryon form factors of other operators and the moments of generalized parton distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Adaptation of HepG2 cells to a steady-state reduction in the content of protein phosphatase 6 (PP6) catalytic subunit

Protein phosphatase 6 (PP6) is a ubiquitous Ser/Thr phosphatase involved in an array of cellular processes. To assess the potential of PP6 as a therapeutic target in liver disorders, we attenuated expression of the PP6 catalytic subunit in HepG2 cells using lentiviral-transduced shRNA. Two PP6 knock-down (PP6KD) cell lines (90% reduction of PP6-C protein content) were studied in depth. Both proliferated at a rate similar to control cells. However, flow cytometry indicated G2/M cell cycle arrest that was accounted for by a shift of the cells from a diploid to tetraploid state. PP6KD cells did not show an increase in apoptosis, nor did they exhibit reduced viability in the presence of bleomycin or taxol. Gene expression analysis by microarray showed attenuated anti-inflammatory signaling. Genes associated with DNA replication were downregulated. Mass spectrometry-based phosphoproteomic analysis yielded 80 phosphopeptides representing 56 proteins that were significantly affected by a stable reduction in PP6-C. Proteins involved in DNA replication, DNA damage repair and pre-mRNA splicing were overrepresented among these. PP6KD cells showed intact mTOR signaling. Our studies demonstrated involvement of PP6 in a diverse set of biological pathways and an adaptive response that may limit the effectiveness of targeting PP6 in liver disorders. - Highlights: • Lentiviral-transduced shRNA was used to generate a stable knockdown of PP6 in HepG2 cells. • Cells adapted to reduced PP6; cell proliferation was unaffected, and cell survival was normal. • However, PP6 knockdown was associated with a transition to a tetraploid state. • Genomic profiling showed downregulated anti-inflammatory signaling and DNA replication. • Phosphoproteomic profiling showed changes in proteins associated with DNA replication and repair.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Machine learning in electronic-quantum-matter imaging experiments

For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena. Today, however, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is to apply this approach to experimental data—for example, to the arrays of complex electronic-structure images17 obtained from atomic-scale visualization of EQM. Here we report the development and training of a suite of artificial neural networks (ANNs) designed to recognize different types of order hidden in such EQM image arrays. These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier-doped copper oxide Mott insulators. In these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs determine that this state is unidirectional, revealing a coincident nematic EQM state. Here, strong-coupling theories of electronic liquid crystals are consistent with these observations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Ptychographic reconstructions performed in real time and offline have equivalent quality

Abstract Ptychography is a burgeoning imaging technique that enables high-resolution, lensless reconstruction of complex samples by analysing overlapping diffraction patterns, making it invaluable in fields like materials science, biology, and nanotechnology. Real-time ptychographic reconstructions are gaining interest in the scientific community as they provide immediate feedback. Yet their potential to replace offline reconstructions remains uncertain, in part due to questions about the quality of the resulting images. This study quantitatively compares real-time and offline reconstructions at different overlap conditions. Offline reconstructions, using all diffraction patterns at once, and real-time reconstructions, where new frames are added to the reconstructions in small chunks as the diffraction patterns are recorded, were indistinguishable and identical in reconstruction quality. These results hold consistently across all tested overlap ratios. This study represents the first quantitative analysis of real-time ptychographic reconstruction using a growing dataset, demonstrating the potential for real-time reconstructions to replace or at least complement offline reconstructions.

Science & Technology - Other Topics↗
DOE OSTI2026

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗
DOE OSTI2014

Metastability, spectrum, and eigencurrents of the Lennard-Jones-38 network

We develop computational tools for spectral analysis of stochastic networks representing energy landscapes of atomic and molecular clusters. Physical meaning and some properties of eigenvalues, left and right eigenvectors, and eigencurrents are discussed. We propose an approach to compute a collection of eigenpairs and corresponding eigencurrents describing the most important relaxation processes taking place in the system on its way to the equilibrium. It is suitable for large and complex stochastic networks where pairwise transition rates, given by the Arrhenius law, vary by orders of magnitude. The proposed methodology is applied to the network representing the Lennard-Jones-38 cluster created by Wales's group. Its energy landscape has a double funnel structure with a deep and narrow face-centered cubic funnel and a shallower and wider icosahedral funnel. However, the complete spectrum of the generator matrix of the Lennard-Jones-38 network has no appreciable spectral gap separating the eigenvalue corresponding to the escape from the icosahedral funnel. We provide a detailed description of the escape process from the icosahedral funnel using the eigencurrent and demonstrate a superexponential growth of the corresponding eigenvalue. The proposed spectral approach is compared to the methodology of the Transition Path Theory. Finally, we discuss whether the Lennard-Jones-38 cluster is metastable from the points of view of a mathematician and a chemical physicist, and make a connection with experimental works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts

In this work, we present NENCI-2021, a benchmark database of ~8000 Non-Equilibirum Non-Covalent Interaction energies for a large and diverse selection of intermolecular complexes of biological and chemical relevance. To meet the growing demand for large and high-quality quantum mechanical data in the chemical sciences, NENCI-2021 starts with the 101 molecular dimers in the widely used S66 and S101 databases and extends the scope of these works by (i) including 40 cation–π and anion–π complexes, a fundamentally important class of non-covalent interactions that are found throughout nature and pose a substantial challenge to theory, and (ii) systematically sampling all 141 intermolecular potential energy surfaces (PESs) by simultaneously varying the intermolecular distance and intermolecular angle in each dimer. Designed with an emphasis on close contacts, the complexes in NENCI-2021 were generated by sampling seven intermolecular distances along each PES (ranging from 0.7× to 1.1× the equilibrium separation) and nine intermolecular angles per distance (five for each ion–π complex), yielding an extensive database of 7763 benchmark intermolecular interaction energies (E int ) obtained at the coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory. The E int values in NENCI-2021 span a total of 225.3 kcal/mol, ranging from -38.5 to +186.8 kcal/mol, with a mean (median) E int value of -1.06 kcal/mol (-2.39 kcal/mol). In addition, a wide range of intermolecular atom-pair distances are also present in NENCI-2021, where close intermolecular contacts involving atoms that are located within the so-called van der Waals envelope are prevalent—these interactions, in particular, pose an enormous challenge for molecular modeling and are observed in many important chemical and biological systems. A detailed symmetry-adapted perturbation theory (SAPT)- based energy decomposition analysis also confirms the diverse and comprehensive nature of the intermolecular binding motifs present in NENCI-2021, which now includes a significant number of primarily induction-bound dimers (e.g., cation–π complexes). NENCI-2021 thus spans all regions of the SAPT ternary diagram, thereby warranting a new four-category classification scheme that includes complexes primarily bound by electrostatics (3499), induction (700), dispersion (1372), or mixtures thereof (2192). A critical error analysis performed on a representative set of intermolecular complexes in NENCI-2021 demonstrates that the E int values provided herein have an average error of ±0.1 kcal/mol, even for complexes with strongly repulsive E int values, and maximum errors of ±0.2–0.3 kcal/mol (i.e., ~±1.0 kJ/mol) for the most challenging cases. For these reasons, we expect that NENCI-2021 will play an important role in the testing, training, and development of next-generation classical and polarizable force fields, density functional theory approximations, wavefunction theory methods, and machine learning based intra- and inter-molecular potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Molecular basis for lipid recognition by the prostaglandin D 2 receptor CRTH2

Significance Many amphipathic lipids, including cannabinoids, lysophospholipids, prostanoids, and leukotrienes, bind to G protein–coupled receptors (GPCRs) through the lipid bilayer. How these lipid ligands get anchored to their receptors and enter ligand-binding pockets is largely elusive. Our results revealed a distinctive lipid recognition mechanism for the prostaglandin D 2 (PGD 2 ) receptor CRTH2 that is not shared by other prostanoid receptors. Based on the structural analysis, we propose two representative binding modes of lipids in GPCRs. We also identified critical structural motifs and conformational changes of CRTH2 for capturing lipids from the lipidic environment and lipid transition to the binding pocket, thereby offering unprecedented molecular insight into the dynamic lipid-binding processes for GPCRs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

TYPE Ia SUPERNOVAE STRONGLY INTERACTING WITH THEIR CIRCUMSTELLAR MEDIUM

Owing to their utility for measurements of cosmic acceleration, Type Ia supernovae (SNe Ia) are perhaps the best-studied class of SNe, yet the progenitor systems of these explosions largely remain a mystery. A rare subclass of SNe Ia shows evidence of strong interaction with their circumstellar medium (CSM), and in particular, a hydrogen-rich CSM; we refer to them as SNe Ia-CSM. In the first systematic search for such systems, we have identified 16 SNe Ia-CSM, and here we present new spectra of 13 of them. Six SNe Ia-CSM have been well studied previously, three were previously known but are analyzed in depth for the first time here, and seven are new discoveries from the Palomar Transient Factory. The spectra of all SNe Ia-CSM are dominated by H{alpha} emission (with widths of {approx}2000 km s{sup -1}) and exhibit large H{alpha}/H{beta} intensity ratios (perhaps due to collisional excitation of hydrogen via the SN ejecta overtaking slower-moving CSM shells); moreover, they have an almost complete lack of He I emission. They also show possible evidence of dust formation through a decrease in the red wing of H{alpha} 75-100 days past maximum brightness, and nearly all SNe Ia-CSM exhibit strong Na I D absorption from the host galaxy. The absolute magnitudes (uncorrected for host-galaxy extinction) of SNe Ia-CSM are found to be -21.3 mag {<=} M{sub R} {<=} -19 mag, and they also seem to show ultraviolet emission at early times and strong infrared emission at late times (but no detected radio or X-ray emission). Finally, the host galaxies of SNe Ia-CSM are all late-type spirals similar to the Milky Way, or dwarf irregulars like the Large Magellanic Cloud, which implies that these objects come from a relatively young stellar population. This work represents the most detailed analysis of the SN Ia-CSM class to date.

79 ASTRONOMY AND ASTROPHYSICS↗