Session A03: Contributed Talks I

Cloud-Aerosol Differentiation In Analysis of Laser Radar Atmospheric Side-Scatter Signals

Laser radar side-scatter signals from a CCD Camera Lidar (CLidar) form images of side-scattered intensity over time and altitude for laser light transmitted vertically into the atmosphere and detected by a ground-based CCD camera aimed adjacent to the laser. Atmospheric structure layers at various altitudes are revealed through analysis of multiple images of the laser side-scatter. For atmospheric studies, being able to differentiate between layered structures that represent clouds and layers of suspended particulates, more known as aerosols. The images from this optical remote sensing instrumentation are analyzed through a program developed in IDL (Interactive Data Language). From the image analysis, aerosol extinction plots are extracted from each data set. Typical aerosol extinction plots reduce to nearly zero after the laser altitude reaches past the atmospheric mixing layer. To interpret these extinction plots to determine if the layers detected are aerosol layers of interest or thin clouds (that may be invisible to the naked eye), we merge data from additional scientific instruments. Through analysis of radiosonde data extracted from the University of Wyoming's database, the relative humidity as a function of increasing altitude is plotted. For each layer of interest on the aerosol extinction plots, the respective altitude of the radiosonde data is analyzed. The two graphs are analyzed concurrently to find strong evidence if the peaks observed in the extinction plot are viable aerosols of interest or potentially thin layers of clouds.   

DETECTING ATMOSPHERIC STRUCTURE WITH CLIDAR ANALYSES

Atmospheric models often make the simplifying assumption that the lowest layer of the atmosphere is well mixed due to convection. Because of this mechanism, this layer is often called the convective boundary layer. By detecting the presence of small particulates suspended in the atmosphere (aerosols) through their ability to scatter incident laser light, we can examine this assumption locally to see if their dynamics over time indicate a well-mixed distribution. Atmospheric aerosol extinction data collected from camera-based Lidar (CLidar) analyses demonstrate instances of atmospheric behavior which does not match the well-mixed assumption. The CLidar system consists of a laser, which is transmitted vertically into the atmosphere and imaged from the side with a CCD camera. The aerosol extinction represents how much of the laser light is removed from the beam through scattering and absorption within a certain altitude increment. By graphing this relationship with altitude and investigating how the plots vary with time, we visualize the amount of scattering occurring at each altitude over time. These studies show that on occasion multiple layers of increased scattering within the atmosphere persist throughout a dataset.   

Computer Vision for Mini-filament Eruption Detection on the Solar Surface

Small-scale filament eruptions on the sun have previously been documented in the scientific literature. However, robust techniques to identify and semantically segment them in imagery data have not been developed. In this talk, we outline preliminary work in using deep learning algorithms, and convolutional neural networks particularly, to locate mini-filament eruptions. We train a ResNet50 model on H-alpha data, using cross-entropy loss as the criterion for optimization and an Adam optimizer with a learning rate of 0.01. The primary long-term objective of this work is to find correlations between the occurrences of these eruptions and coronal jets. Automated computer vision methods provide more efficient mechanisms to study long term trends and correlations that would not be possible with conventional methods.   

Post-merger gravitational wave searches using the Cross-Correlation Algorithm

After the multi-messenger detection of the binary neutron star merger GW170817, associated with gamma-ray burst (GRB) 170817a, one big open question left is the nature of the compact remnant which acts as a central engine for the GRB. In the context of cosmological GRBs, it has been suggested that X-ray afterglows showing light-curve plateaus at timescales of order 10$^{\mathrm{2\thinspace }}$-10$^{\mathrm{4\thinspace }}$s since the GRB/merger could harbor a long-lived central engine, such as a long-lived highly magnetized NS (magnetar). Newly born magnetars have also been proposed as potential gravitational wave (GW) sources. Motivated by these considerations, we present first results from a new GW data analysis method (the Cross Correlation Algorithm-CoCoA) targeting long-lived GWs from magnetars formed in binary NS mergers associated with GRBs. We show how our search method improves substantially on previously published results for post-merger GW searches in GW170817, but requires a more restrictive hypothesis on the GW signal properties. We conclude by discussing the prospects for these types of searches in future runs of the LIGO detectors.   

A new explanation for breaking the Light Speed Barrier based on the Hubble constant

In 1929, Hubble presented the observational evidence for one of science's greatest discoveries the expanding universe. Hubble showed that galaxies are receding away from us with a velocity that is proportional to their distance from us: more distant galaxies recede faster than nearby galaxies. Hubble's classic graph of the observed velocity vs. distance for nearby galaxies is presented in graph which this graph has become a scientific landmark that is regularly reproduced in astronomy textbooks. The graph reveals a linear relation between galaxy velocity (v) and its distance (d): v $=$H$_{\mathrm{0}}$ d . By considering that the universe was created in the effect of the Big Bang. At first initial energy is applied to the entire universe, causing a linear motion and a rotational motion. In this paper we are going to clarify the meaning of Hubble's receding speed by considering these two types of motion, and then we will calculate the speed of the objects at the edges of the universe which have higher speeds than light. To support this claim, we note that there is miscalculation happened in the estimation of released initial Energy of the Big Bang and if it is calculated by Energy density, the initial energy which released is so more than which has estimated before.