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Landslide Runout Analysis Using Remote Sensing for Lower and Upper Barun Lakes
Landslides are recognized as common triggers of glacial lake outburst floods in high mountain regions, but the remoteness of dangerous glacial lakes makes monitoring and disaster management very difficult. Using a very high resolution digital elevation model produced from a series of stereo satellite images, we simulate debris flows and rock avalanche runout around two very dangerous lakes in high mountain Nepal. We constrain particularly likely sources of these slope failures to simulate 79,800 potential debris flows and rock avalanches of volumes ranging from 1,000 m3 to 1,000,000 m3. This allowed us to identify zones in and around dangerous glacial lakes that are most exposed to landslide runout hazards, along with the most frequent source areas that produced landslides that may run into either of the two glacial lakes. Although our empirical-statistical model presented limitations that numerical models attempt to overcome, our probabilistic approach combined with a very high resolution DEM may shed new light on how to identify sources and impact areas of debris flows and rock avalanches that may cascade into downstream disasters.
The Nonpotentiality of Steady-state Coronal Magnetic Field Derived with Time-relaxation Magnetohydrodynamics Simulations Using Helioseismic and Magnetic Imager Three-component Magnetic Field Data
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A Verification of MCNP6 FMESH Tally Capabilities
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Detecting Changes in Simulations
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Methods for determining absorbed dose information
The disclosure discusses methods for determining absorbed dose information. A tomography imaging device generates an anatomy image relating to anatomy of a particular patient. A tomography imaging device also generates multiple radioactivity images regarding radioactivity distribution of an internally administered pharmaceutical over time in the particular patient. The radioactivity images related to the radioactivity distribution over time are registered. Each radioactivity image is combined with each anatomy image to create activity images. A Monte Carlo simulation for each activity image is run to obtain absorbed dose-rate images of the pharmaceutical at multiple times. The absorbed dose-rate images are integrated over time to obtain a total absorbed dose image. The absorbed dose-rate images and the total absorbed dose image are used to obtain a biologically effective dose (BED) image. The BED image is used to obtain an equivalent uniform does (EUD) of BED values for a chosen anatomical region.
Machine Learning for DUNE Supernova Trigger
One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux originating from galactic core-collapse supernovae. These neutrinos provide an opportunity to study the end of life evolution of massive stars, and reveal information about the structure of core-collapse that is not visible in observations of the electromagnetic spectrum. Because of the rarity of these events, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, this will require sifting through a large quantity of data, motivating the development of a trigger algorithm to identify significant events and discard irrelevant data. Machine learning provides a potential approach to building this trigger. This project generates ADC and ground truth images of simulated neutrino interactions in a LArTPC detector to be used for machine learning, and uses them to train a sparse Convolutional Neural Network (C NN). The performance of this model when applied to the task of pixel classification based on interaction type is examined. This project found that the sparse CNN approach has the potential to have high accuracy in pixel classification, meaning it may be highly relevant to the development of a supernova neutrino trigger for the DUNE far detector.
Accurate Dark Energy Constraints via the Precise Characterization of Galaxy Intrinsic Alignment Coupled with Shear and Redshift Interference
One of the most fundamental mysteries we face in physics is understanding what the nature of the dark energy component of our Universe is that causes the accelerated expansion of space. A powerful way to study dark energy is via weak gravitational lensing due to large-scale structure in the Universe, which we observe as tiny distortions in the shapes of very distant galaxies due to the bending of light by foreground mass. The Dark Energy Survey (DES) and Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) were designed to enable unprecedented measurements of these distortions in the shapes of galaxies to study dark energy. The intrinsic shapes of galaxies before they are distorted by weak gravitational lensing can cancel out some of the effects of lensing to obscure or bias what we infer about the nature of dark energy. This research program has led to improved image-level simulations of the Rubin Observatory LSST data. These simulations have made it possible to characterize for the first time the cosmic impact of these intrinsic shapes on biasing weak gravitational lensing calibration. This work will inform crucial improvements in methodology for studying dark energy with weak gravitational lensing at the precision promised by the the Rubin Observatory LSST over the next decade to ensure we are able to infer unbiased information about the nature of dark energy.
SU-F-J-178: A Computer Simulation Model Observer for Task-Based Image Quality Assessment in Radiation Therapy
Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework was developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation of additional modules to include any aspect of the treatment process, and therefore has great potential for both assessment and optimization within radiation therapy.
Monte Carlo simulation of a biological object with optical coherent tomography structural images using a voxel-based geometry of a medium
We describe a Monte Carlo algorithm for simulating an interference signal and constructing a structural image of a biological object using optical coherent tomography (OCT). The algorithm is specific in that the object geometry is described by using a three-dimensional array of voxels combined into parallelograms, and the border cross checking is carried out by using an improved Smith algorithm. The geometry of the simulated object is reconstructed based on the structure of real biological tissues obtained by OCT. The algorithm efficiency is estimated by comparing experimental and corresponding simulated images of a subcutaneous blood vessel and upper layer of human skin in vivo . (biophotonics)
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.
Emerging standards for still image compression: A software implementation and simulation study
The software implementation is described of an emerging standard for the lossy compression of continuous tone still images. This software program can be used to compress planetary images and other 2-D instrument data. It provides a high compression image coding capability that preserves image fidelity at compression rates competitive or superior to most known techniques. This software implementation confirms the usefulness of such data compression and allows its performance to be compared with other schemes used in deep space missions and for data based storage.
Examination of Lunar Regolith Simulants By SEM-EDS and Imaging Raman Spectroscopy
The Artemis series lunar missions will include sample returns from the lunar south pole. Lunar regolith simulants (RS) generated in the lab provide opportunities to compare two surface science techniques: scanning electron microscopy (SEM-EDS) and Raman induced surface spectroscopy. The results will also be applicable for supporting future commercial lunar payload services (CLPS) and Artemis surface. Surface characterization contributes to continued development of lunar regolith studies and adds to various regolith databases. In characterizing various lunar regolith simulants, part of the aim should be to standardize techniques and utilize anticipated methods available for astromaterials studied during, or returned from, upcoming missions, particularly samples collected from the Moon’s south pole and permanently shadowed regions (PSRs).. An initial survey of available simulants has been started with Raman scanning process for particle counting the results of which will enrich the NASA-JSC Simulant Development Lab (SDL) simulant properties database and the Colorado School of Mines Planetary Simulant Data Base. An SEM-EDS dataset of raw regolith simulant materials are collected.
Simulation and studies of spaceborne synthetic aperture radar image quality with reduced bit rate
A computer simulation program that is used to study the effects of digitization in spaceborne synthetic aperture radar systems is described. An analytical study of the distortion noise introduced by the digitization process at various gain settings, sampling rates and bit error rates is presented and the results agree well with those obtained from the simulation program. The simulation program is also used to study the spatial frequency response of hard-limiting (quantizing to 1-bit) synthetic aperture radar systems. The implications of these results on synthetic aperture radar system design are discussed.
THE KELVIN-HELMHOLTZ INSTABILITY AT CORONAL MASS EJECTION BOUNDARIES IN THE SOLAR CORONA: OBSERVATIONS AND 2.5D MHD SIMULATIONS
The Atmospheric Imaging Assembly on board the Solar Dynamics Observatory observed a coronal mass ejection with an embedded filament on 2011 February 24, revealing quasi-periodic vortex-like structures at the northern side of the filament boundary with a wavelength of approximately 14.4 Mm and a propagation speed of about 310 {+-} 20 km s{sup -1}. These structures could result from the Kelvin-Helmholtz instability occurring on the boundary. We perform 2.5D numerical simulations of the Kelvin-Helmholtz instability and compare the simulated characteristic properties of the instability with the observations, where we obtain qualitative as well as quantitative accordance. We study the absence of Kelvin-Helmholtz vortex-like structures on the southern side of the filament boundary and find that a magnetic field component parallel to the boundary with a strength of about 20% of the total magnetic field has stabilizing effects resulting in an asymmetric development of the instability.
High accuracy optical rate sensor
Optical rate sensors, in particular CCD arrays, will be used on Space Station Freedom to track stars in order to provide inertial attitude reference. An algorithm to provide attitude rate information by directly manipulating the sensor pixel intensity output is presented. The star image produced by a sensor in the laboratory is modeled. Simulated, moving star images are generated, and the algorithm is applied to this data for a star moving at a constant rate. The algorithm produces accurate derived rate of the above data. A step rate change requires two frames for the output of the algorithm to accurately reflect the new rate. When zero mean Gaussian noise with a standard deviation of 5 is added to the simulated data of a star image moving at a constant rate, the algorithm derives the rate with an error of 1.9 percent at a rate of 1.28 pixels per frame.
ON FIBRILS AND FIELD LINES: THE NATURE OF Hα FIBRILS IN THE SOLAR CHROMOSPHERE
Observations of the solar chromosphere in the line core of the Hα line show dark elongated structures called fibrils that show swaying motion. We performed a three-dimensional radiation-MHD simulation of a network region and computed synthetic Hα images from this simulation to investigate the relation between fibrils and the magnetic field lines in the chromosphere. The periods, amplitudes, and phase speeds of the simulated fibrils are consistent with observations. We find that some fibrils trace out the same field line along the fibril’s length, while other fibrils sample different field lines at different locations along their length. Fibrils sample the same field lines on a timescale of ∼200 s. This is shorter than their own lifetime. Fibril-threading field lines carry slow-mode waves, as well as transverse waves propagating with the Alfvén speed. Transverse waves propagating in opposite directions cause an interference pattern with complex apparent phase speeds. The relationship between fibrils and field lines is governed by constant migration and swaying of the field lines, their mass loading and draining, and their visibility in Hα. Field lines are visible where they lie close to the optical depth unity surface. The location of the latter is at a height at which the column mass reaches a certain fixed value. The visibility of the field line is thus determined by its own mass density and by the mass density of the material above it. Using the swaying motion of fibrils as a tracer of chromospheric transverse oscillations must be done with caution.