Bulletin of the American Physical Society
2018 Annual Meeting of the APS Four Corners Section
Volume 63, Number 16
Friday–Saturday, October 12–13, 2018; University of Utah, Salt Lake City, Utah
Session C08: Laboratory Astrophysics and Computational Techniques |
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Chair: David Neilsen, Brigham Young University Room: CSC 13 |
Friday, October 12, 2018 10:45AM - 11:09AM |
C08.00001: Laboratory Studies of Cryogenic Outer Solar System Geologic Materials Invited Speaker: Jennifer Hanley The Physics and Astronomy Department at Northern Arizona University hosts one of only a handful of laboratories around the world devoted to studies of astrophysical ices. Simple molecules like CH4, N2, CO, CO2, O2, CH3OH, C2H6, and NH3 are important geological materials in the cold outer regions of the solar system. Their mobility and distinct material properties enable geological activity and produce a spectacular variety of exotic landforms, even at extremely low temperatures. But frustratingly little is known of the basic mechanical and optical properties of these volatile ices, and especially of their mixtures. |
Friday, October 12, 2018 11:09AM - 11:21AM |
C08.00002: Testing Backwards Integration As A Method Of Finding New Kuiper Belt Object Families. Nathan Benfell The age of young asteroid collisional families is often determined by using reversed simulations (i.e. backwards integration) of the solar system. This method is not used for discovering young asteroid families and is limited by unpredictable factors unique to the Asteroid Belt (e.g. the Yarkovsky Effect). The Kuiper Belt is absent of these unpredictabilities, and thus we theorized that backwards integrations could be an advantageous method for both Kuiper Belt Object (KBO) family finding and characterization. My work thus far has centered on determining the most effective means of designing these simulations to ensure faithful reproductions of the actual solar system. After running several integrations, I will share what has been learned thus far regarding important parameters, potential pitfalls, and discovery potential. |
Friday, October 12, 2018 11:21AM - 11:33AM |
C08.00003: An Improved Ultra-High Energy Cosmic-Ray Point-Source Anisotropy Scan Method Jeff Johnsen, Frederic Sarazin The anisotropy scan of ultra-high energy cosmic ray events against a catalog of proposed point-sources has traditionally not incorporated measurement uncertainties, allowing a possible sensitivity to spurious correlation. This updated method folds measurement uncertainties into the scan statistics, implements a post-trial p-value process, and estimates scan result uncertainties using event-set bootstrapping. Simulated event sets are used to asses the efficacy of these changes and provide an exemplar case in lieu of measured cosmic ray events. |
Friday, October 12, 2018 11:33AM - 11:45AM |
C08.00004: Machine Learning Weather Classification with Fluorescence Detector Pedestal Data at the Telescope Array Cosmic Observatory Greg Furlich Telescope Array Cosmic Ray Observatory has completed 10 years of operations with the Fluorescence Detector (FD) sites of Black Rock (BR) and Long Ridge (LR) achieving a duty cycle of 10% and 9% respectively. Included in the data is nights with cloudy weather which are excluded from our cosmic ray analysis. Weather observations are recorded every half hour while the FDs are operating by shift runners. However a more robust and uniform weather classification method is desired for flagging and excluding bad weather. A series of snapshot of the night sky was created using the detector's photomultiplier tubes (PMTs) pedestals. We classfied the night's weather using machine learning methods and the PMT pedestal snapshots. Results of this machine learning weather classification will be presented. |
Friday, October 12, 2018 11:45AM - 11:57AM |
C08.00005: Utah Rocks – Elemental composition and climate evolution James Hurford-Reynolds, Dr. Michelle Arnold X-ray florescent (XRF) involve the bombardment of x-rays onto an object with the goal of dislodging inner electrons from the electron shells of the individual elements. By measuring the energy emitted by the subsequently de-exciting electrons, in the form of characteristic x-rays, the elemental composition can be determined. The technology in this area has had major breakthroughs in recent years allowing these detectors to become smaller to the point where a detector can be a handheld device. The newfound portability of XRF devices has allowed them to be used in an even wider array, from determining lead contents in human bones to determining if a site has the potential to be profitable for mining. XRF detectors can also be used in determining the evolution of a climate based on the accumulation of different elements within each layer of sedimentary rock. The reading of different element concentrations within sedimentary rock determines the environment present during the formation of that layer. Preliminary data regarding the evolution of Utah climate will be presented. |
Friday, October 12, 2018 11:57AM - 12:09PM |
C08.00006: Neutron Capture Process for a Diverse Sample of Stars Anthony Garcia To understand the cosmic origin of heavy elements, constraints on the neutron capture (n-capture) process are imposed using stellar abundance measurements for nuclei heavier then iron (Z≥26). Examples of the n-capture process are; aging stars nearing the end of their lives (slow n-capture) and supernovae or neutron star mergers (rapid n-capture). Sites and mechanics of neutron capture production are still lacking substantial abundance observations to constrain proposed models. We are extending abundance analysis of n-capture elements in a selection of stars with diverse stellar parameters by analyzing dozens of atomic transitions in the wavelength range 3100Å-4300Å. Our sample includes spectra from 40 stars covering an overall chemical enrichment of 1/1000 solar and slightly above solar. We are analyzing: four first peak slow n-capture elements strontium (Z=38), yttrium (Z=39), zirconium (Z=40), and niobium (Z=41); and four rapid n-capture elements europium (Z=63), terbium (Z=65), thulium (Z=69), and iridium (Z=77). Three slow/rapid n-capture elements (slow n-capture process contribution ~50%) palladium (Z=46), silver (Z=47), and hafnium (Z=72) will also be measured. Our abundance measurements along with literature n-capture abundances will be compared to current stellar yields. |
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