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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

THE ACTIVE ASTEROIDS

Some asteroids eject dust, unexpectedly producing transient, comet-like comae and tails. First ascribed to the sublimation of near-surface water ice, mass-losing asteroids (also called 'main-belt comets') can in fact be driven by a surprising diversity of mechanisms. In this paper, we consider 11 dynamical asteroids losing mass, in nine of which the ejected material is spatially resolved. We address mechanisms for producing mass loss including rotational instability, impact ejection, electrostatic repulsion, radiation pressure sweeping, dehydration stresses, and thermal fracture, in addition to the sublimation of ice. In two objects (133P and 238P) the repetitive nature of the observed activity leaves ice sublimation as the only reasonable explanation, while in a third ((596) Scheila), a recent impact is the cause. Another impact may account for activity in P/2010 A2, but this tiny object can also be explained as having shed mass after reaching rotational instability. Mass loss from (3200) Phaethon is probably due to cracking or dehydration at extreme ({approx}1000 K) perihelion temperatures, perhaps aided by radiation pressure sweeping. For the other bodies, the mass-loss mechanisms remain unidentified, pending the acquisition of more and better data. While the active asteroid sample size remains small, the evidence for an astonishing diversity of mass-loss processes in these bodies is clear.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

DISTANCES TO DARK CLOUDS: COMPARING EXTINCTION DISTANCES TO MASER PARALLAX DISTANCES

We test two different methods of using near-infrared extinction to estimate distances to dark clouds in the first quadrant of the Galaxy using large near-infrared (Two Micron All Sky Survey and UKIRT Infrared Deep Sky Survey) surveys. Very long baseline interferometry parallax measurements of masers around massive young stars provide the most direct and bias-free measurement of the distance to these dark clouds. We compare the extinction distance estimates to these maser parallax distances. We also compare these distances to kinematic distances, including recent re-calibrations of the Galactic rotation curve. The extinction distance methods agree with the maser parallax distances (within the errors) between 66% and 100% of the time (depending on method and input survey) and between 85% and 100% of the time outside of the crowded Galactic center. Although the sample size is small, extinction distance methods reproduce maser parallax distances better than kinematic distances; furthermore, extinction distance methods do not suffer from the kinematic distance ambiguity. This validation gives us confidence that these extinction methods may be extended to additional dark clouds where maser parallaxes are not available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The X-ray luminosity functions of field low-mass X-ray binaries in early-type galaxies: Evidence for a stellar age dependence

We present direct constraints on how the formation of low-mass X-ray binary (LMXB) populations in galactic fields depends on stellar age. In this pilot study, we utilize Chandra and Hubble Space Telescope (HST) data to detect and characterize the X-ray point source populations of three nearby early-type galaxies: NGC 3115, 3379, and 3384. The luminosity-weighted stellar ages of our sample span ≈3-10 Gyr. X-ray binary population synthesis models predict that the field LMXBs associated with younger stellar populations should be more numerous and luminous per unit stellar mass than older populations due to the evolution of LMXB donor star masses. Crucially, the combination of deep Chandra and HST observations allows us to test directly this prediction by identifying and removing counterparts to X-ray point sources that are unrelated to the field LMXB populations, including LMXBs that are formed dynamically in globular clusters, Galactic stars, and background active galactic nuclei/galaxies. We find that the 'young' early-type galaxy NGC 3384 (≈2-5 Gyr) has an excess of luminous field LMXBs (L {sub X} ≳ (5-10) × 10{sup 37} erg s{sup –1}) per unit K-band luminosity (L{sub K} ; a proxy for stellar mass) than the 'old' early-type galaxies NGC 3115 and 3379 (≈8-10 Gyr), which results in a factor of ≈2-3 excess of L {sub X}/L{sub K} for NGC 3384. This result is consistent with the X-ray binary population synthesis model predictions; however, our small galaxy sample size does not allow us to draw definitive conclusions on the evolution field LMXBs in general. We discuss how future surveys of larger galaxy samples that combine deep Chandra and HST data could provide a powerful new benchmark for calibrating X-ray binary population synthesis models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

In this work, domain switching pathways in ferroelectric materials visualized by dynamic piezoresponse force microscopy (PFM) are explored via variational autoencoder, which simplifies the elements of the observed domain structure, crucially allowing for rotational invariance, thereby reducing the variability of local polarization distributions to a small number of latent variables. For small sampling window sizes the latent space is degenerate, and variability is observed only in the direction of a single latent variable that can be identified with the presence of domain wall. For larger window sizes, the latent space is 2D, and the disentangled latent variables can be generally interpreted as the degree of switching and complexity of domain structure. Applied to multiple consecutive PFM images acquired while monitoring domain switching, the polarization switching mechanism can thus be visualized in the latent space, providing insight into domain evolution mechanisms and their correlation with the microstructure.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Quasar UV/X-ray relation luminosity distances are shorter than reverberation-measured radius–luminosity relation luminosity distances

ABSTRACT We use measurements of 59/58 quasars (QSOs), over a redshift range 0.0041 ≤ z ≤ 1.686, to do a comparative study of the radius–luminosity (R − L) and X-ray−UV luminosity (LX − LUV) relations and the implication of these relations for cosmological parameter estimation. By simultaneously determining R − L or LX − LUV relation parameters and cosmological parameters in six different cosmological models, we find that both R − L and LX − LUV relations are standardizable but provide only weak cosmological parameter constraints, with LX − LUV relation data favouring larger current non-relativistic matter density parameter Ωm0 values than R − L relation data and most other available data. We derive LX − LUV and R − L luminosity distances for each of the sources in the six cosmological models and find that LX − LUV relation luminosity distances are shorter than R − L relation luminosity distances as well as standard flat ΛCDM model luminosity distances. This explains why LX − LUV relation QSO data favour larger Ωm0 values than do R − L relation QSO data or most other cosmological measurements. While our sample size is small and only spans a small z range, these results indicate that more work is needed to determine whether the LX − LUV relation can be used as a cosmological probe.

Astronomy & Astrophysics↗
DOE OSTI2014

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity to different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers. P01-CA59827;R01CA142840.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-P-52: Dose-Volume Toxicity Analysis of Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma

Purpose: To investigate the relationship between physical and biological effective dose (BED) to liver toxicity for SBRT fractionations. Methods: A total of 16 patients (13–10×3Gy, 2–5×10Gy and 1–3×15Gy were selected. Physical dose distributions were converted to voxel based BED values using the linear-quadratic (LQ) and linear-quadratic linear (LQ-L) models for doses per fraction larger than 6Gy. Patients were graded for effective toxicity (post-treatment minus pre-treatment grades) using the RTOG Late Radiation Morbidity Scoring Schema associated with Radiation Induced Liver Disease (RILD). Evaluated physical dose-volume levels consisted of V10Gy, V15Gy, V20Gy, V25Gy and V30Gy which were then converted to BED values corresponding to V16.7Gy3, V30Gy3, V46.7Gy3, V66.7Gy3 and V90Gy3, respectively. All levels were normalized to their respective patient normal liver volumes (NLV) and evaluated for correlation to RILD. Results were measured quantitatively using R-squared regression analysis. Results: Mean Dose Tolerable to Normal Liver (MDTNL) against RILD grade resulted in an R-squared value of 0.0104. NLV-normalized physical dose linear regression fit of V10Gy, V15Gy, V20Gy, V25Gy and V30Gy against RILD yielded R-squared values of 0.1041, 0.0895, 0.0698, 0.0398 and 0.0009 while BED levels of V16.7Gy3, V30Gy3, V46.7Gy3, V66.7Gy3 and V90Gy3 resulted in values of 0.0002, 0.0153, 0.0533, 0.0427 and 0.0072, respectively. The average NLV-normalized V10Gy, V15Gy, V20Gy, V25Gy and V30Gy per grade plotted against RILD grade yielded R-squared correlations of 0.8092, 0.6362, 0.5899, 0.5846 and 0.0224 while the BED levels of V16.7Gy3, V30Gy3, V46.7Gy3, V66.7Gy3 and V90Gy3 resulted in R-squared correlations of 0.0003, 0.3831, 0.8476, 0.678 and 0.076, respectively. Conclusion: Regression analysis between physical dose, BED and RILD showed strong correlation for the V30Gy and V46.7Gy3 dose-levels. Average BED and physical dose per grade both exhibit strong correlations to RILD however, a lack of statistical significance exists due to small patient sample size.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-CAMPUS-IeP1-03: Establishment of Provincial Diagnostic Reference Levels in Pediatric Imaging

Purpose: To establish provincial diagnostic reference levels (DRLs) in pediatric general radiography and computed tomography (CT) as a tool for the optimization of exposure parameters. Methods: Patient dose survey was conducted in the only pediatric hospital in the province of Nova Scotia. The DRLs were established as the 75th percentile of patient dose distributions in different age groups. For routine radiography projections the DRLs were determined in terms of entrance surface dose (ESD) calculated from the radiation output measurements and the tube current-exposure time product (mAs) recorded for each examination. Patient thickness was measured by the technologist during the examination. The CR and DR systems, employing respectively a fixed technique and phototiming, were evaluated separately; a two-tailed Student’s t-test was used to determine the significance of differences between the means of dose distributions. The CT studies included routine head, chest, abdomen/pelvis, and chest/abdomen/pelvis. The volume CT dose index (CTDIvol) and dose-length product (DLP) values were extracted retrospectively from PACS. The correction factors based on the effective diameter of the patient were applied to the CT dosimetry metrics based on the standard phantoms. Results: The provincial DRLs were established in the following age groups: newborn, 1, 5, 10, and 15 year olds. In general radiography the DR systems demonstrated slightly lower dose than the CR for all views, however the differences were not statistically significant (p > 0.05) for all examinations. In CT the provincial DRLs were lower than the published data, except for head DLPs in all age categories. This might be due to the small patient sample size in the survey. Future work will include additional CT data collection over an extended period of time. Conclusion: Provincial DRLs were established in the dedicated children’s hospital to provide guidance for the other facilities in examinations of pediatric patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Combining Emergent Constraints for Climate Sensitivity

A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology (Final Project)

The search for simple and elegant equations to explain and predict observable natural phenomena has been a driving force in scientific theory for several centuries. The great successes of Newton's laws, Maxwell's equations, the Schrodinger equation, and the theory of General Relativity have encouraged this perspective, and perhaps a beautiful equation comprising a “theory of everything” remains to be discovered. But now scientists are grappling with systems of ever-increasing complexity, fraught with nonlinearity, and with the quantities of interest often only indirectly observed. Nature may resist a simple description, and the most important discoveries of the next century may be complex theories with countless variables and parameters. The era of big data opens up a promising new approach to scientific discovery in this setting. The predictions of modern theories, even if complex or nonlinear, can be examined through detailed and computationally-intensive simulations that take days or weeks to run and fill petabytes of disk space. The upcoming generation of scientific instruments will provide petabytes of observations at unprecedented resolution and depth. The challenge is to compare data to simulations so as to test theories and identify those that best match the observations. The data are numerous and rich but are subject to noise and various systematic biases. The simulations are large and detailed but are so costly that the effective sample size is small. Moreover, the mapping from data to simulation to the parameters of interest is typically complicated and ill-conditioned. Although these challenges, and the solutions we propose, apply across the sciences, we will ground our efforts by focusing on cosmology. Here, we are truly entering the era of big data in several senses. Ever larger experiments survey larger and larger cosmological volumes, resulting in enormous amounts of pixel data to process and extract the information of cosmological interest. These include large imaging surveys, which collect all the light in broad wavelength ranges over significant fractions of the sky, and spectroscopic surveys, which measure the light as a function of wavelength to map out 3D distances. These experiments are designed to shed light on the big unanswered questions in cosmology. Distilling the petabytes of data into observed quantities for comparison with theory is a daunting task. But these large experiments do not only result in large amounts of data to analyze: if we wish to compare to theories of physics, then we need increasingly extensive predictions for the observed quantities as well, which requires ever-larger cosmological simulations that take substantial -- and at times prohibitive -- computational resources to produce. Our goal is to develop statistical and machine learning methods for using observed and simulated data to advance machine learning with applications cosmology. In particular, we focus our research efforts on the following tasks: The challenge for automated science is that it is computationally impossible and statistically dangerous to consider every possible model in order to find the best one. We will develop Bayesian Optimization based active-learning methods that accelerate both the execution of the simulations and the search for best-fitting parameters. The key idea is to make the simulations adaptive -- across resolutions, time, and parameters -- using the data to search as the simulation runs; Most ML methods operate on simple finite dimensional feature vectors. However, many cosmology and other science applications require ML methods that can operate on more complex objects as inputs or outputs such as functions, distributions, or sets and point clouds. Our goal is to develop efficient ML methods for this problem and demonstrate their applicability in Cosmology, Astrophysics, and other science problems.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1996

Space research activities during missions of the past

For over 30 yr, men and women have lived for various periods of time in a weightless (or free-fall) environment while orbiting the Earth. During these years, we have learned that humans function quite well for short periods of weightlessness, that is, for up to a little more than a year. Some space flight missions have provided physiologic data, including cardiovascular data, from the spacefarers. In fact, some missions have provided laboratories for systematic study of cardiovascular responses and adaptation to space flight. However, the opportunity to obtain physiologic data from people in space is a rarity. It is important to remember that the population sample sizes are small, other stresses may confound the effects of weightlessness, and in some situations the crewmembers are subjects for several experiments at the same time. Furthermore, comparison of cardiovascular data from space flight to data obtained on the ground is sometimes difficult because the subject's posture on the ground is not always reported; in a gravity environment, posture influences the hydrostatic gradient. This over view describes what we have learned about cardiovascular function during flight and after return to Earth.

NASA Discipline Cardiopulmonary↗
NASA NTRS2012

Laser Diffraction Techniques Replace Sieving for Lunar Soil Particle Size Distribution Data

Sieving was used extensively until 1999 to determine the particle size distribution of lunar samples. This method is time-consuming, and requires more than a gram of material in order to obtain a result in which one may have confidence. This is demonstrated by the difference in geometric mean and median for samples measured by [1], in which a 14-gram sample produced a geometric mean of approx.52 micrometers, whereas two other samples of ~1.5 grams resulted in gave means of approx.63 and approx.69 micrometers. Sample allocations for sieving are typically much smaller than a gram, and many of the sample allocations received by our lab are 0.5 to 0.25 grams in mass. Basu [2] has described how the finest fraction of the soil is easily lost in the sieving process, and this effect is compounded when sample sizes are small.

Cooper, Bonnie L.↗
NASA NTRS2013

First Cloud-to-Ground Lightning Timing Study

NASA's LSP, GSDO and other programs use the probability of cloud-to-ground (CG) lightning occurrence issued by the 45th Weather Squadron (45 WS) in their daily and weekly lightning probability forecasts. These organizations use this information when planning potentially hazardous outdoor activities, such as working with fuels, or rolling a vehicle to a launch pad, or whenever personnel will work outside and would be at-risk from lightning. These organizations would benefit greatly if the 45 WS could provide more accurate timing of the first CG lightning strike of the day. The Applied Meteorology Unit (AMU) has made significant improvements in forecasting the probability of lightning for the day, but forecasting the time of the first CG lightning with confidence has remained a challenge. To address this issue, the 45 WS requested the AMU to determine if flow regimes, wind speed categories, or a combination of the two could be used to forecast the timing of the first strike of the day in the Kennedy Space Center (KSC)/Cape Canaveral Air Force Station (CCAFS) lightning warning circles. The data was stratified by various sea breeze flow regimes and speed categories in the surface to 5,000-ft layer. The surface to 5,000-ft layer was selected since that is the layer the 45 WS uses to predict the behavior of sea breeze fronts, which are the dominant influence on the occurrence of first lightning in Florida during the warm season. Due to small data sample sizes after stratification, the AMU could not determine a statistical relationship between flow regimes or speed categories and the time of the first CG strike.. As expected, although the amount and timing of lightning activity varies by time of day based on the flow regimes and speed categories, there are extended tails of low lightning activity making it difficult to specify times when the threat of the first lightning flash can be avoided. However, the AMU developed a graphical user interface with input from the 45 WS that allows forecasters to visualize the climatological frequencies of the timing of the first lightning strike. This tool should contribute directly to the 45 WS goal of improving lightning timing capability for its NASA, US Air Force and commercial customers.

statistical analysis↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
DOE OSTI2022

An Exact and Near-Exact Distribution Approach to the Behrens–Fisher Problem

The Behrens–Fisher problem occurs when testing the equality of means of two normal distributions without the assumption that the two variances are equal. This paper presents approaches based on the exact and near-exact distributions for the test statistic of the Behrens–Fisher problem, depending on different combinations of even or odd sample sizes. We present the exact distribution when both sample sizes are odd and the near-exact distribution when one or both sample sizes are even. The near-exact distributions are based on a finite mixture of generalized integer gamma (GIG) distributions, used as an approximation to the exact distribution, which consists of an infinite series. The proposed tests, based on the exact and the near-exact distributions, are compared with Welch’s t-test through Monte Carlo simulations, in particular for small and unbalanced sample sizes. The results show that the proposed approaches are competent solutions to the Behrens–Fisher problem, exhibiting precise sizes and better powers than Welch’s approach for those cases. Numerical studies show that the Welch’s t-test tends to be a bit more conservative than the test statistics based on the exact or near-exact distribution, in particular when sample sizes are small and unbalanced, situations in which the proposed exact or near-exact distributions obtain higher powers than Welch’s t-test.

Hong, Serim↗
DOE OSTI2013

A STATISTICAL STUDY OF TRANSVERSE OSCILLATIONS IN A QUIESCENT PROMINENCE

The launch of the Hinode satellite has allowed for seeing-free observations at high-resolution and high-cadence making it well suited to study the dynamics of quiescent prominences. In recent years it has become clear that quiescent prominences support small-amplitude transverse oscillations, however, sample sizes are usually too small for general conclusions to be drawn. We remedy this by providing a statistical study of transverse oscillations in vertical prominence threads. Over a 4 hr period of observations it was possible to measure the properties of 3436 waves, finding periods from 50 to 6000 s with typical velocity amplitudes ranging between 0.2 and 23 km s{sup –1}. The large number of observed waves allows the determination of the frequency dependence of the wave properties and derivation of the velocity power spectrum for the transverse waves. For frequencies less than 7 mHz, the frequency dependence of the velocity power is consistent with the velocity power spectra generated from observations of the horizontal motions of magnetic elements in the photosphere, suggesting that the prominence transverse waves are driven by photospheric motions. However, at higher frequencies the two distributions significantly diverge, with relatively more power found at higher frequencies in the prominence oscillations. These results highlight that waves over a large frequency range are ubiquitous in prominences, and that a significant amount of the wave energy is found at higher frequency.

79 ASTRONOMY AND ASTROPHYSICS↗