Astrophysics and Astronomy Commons^™

Continuum Damping Effects In Nuclear Collisions, Hossein Sadeghi, Mahdieh Ghafouri

Karbala International Journal of Modern Science

The Time-Dependent Skyrme Hartree-Fock (TDSHF) calculations have been conducted to study 100Sn+16O, 116Sn+16O, and 122Sn+16O collisions on a 3-Dimensional (3D) mesh with SV-bas SF. For the 100Sn+16O collision, the continuum damping width of the rotational amplitudes in Ecm = 100, 150, 200, and 250 MeV has been achieved around 108, 185, 277, and 318, with the time evolution width for z2 around 15/5, 13/5, 13/9, or 14/3 fm2. The quadrupole deformation, kinetic energy, and rotational amplitude are studied. It is seen that the compound nucleus becomes uniform and spherical as time grows. The results of the time evolution show the …

From Equal-Mass To Extreme-Mass-Ratio Binary Inspirals: Simulation Tools For Next Generation Gravitational Wave Detectors, Samuel Douglas Cupp

LSU Doctoral Dissertations

Current numerical codes can successfully evolve similar-mass binary black holes systems, and these numerical waveforms contributed to the success of the LIGO Collaboration's detection of gravitational waves. LIGO requires high resolution numerical waveforms for detection and parameter estimation of the source. Great effort was expended over several decades to produce the numerical methods used today. However, future detectors will require further improvements to numerical techniques to take full advantage of their detection capabilities. For example, the Laser Interferometer Space Antenna (LISA) will require higher resolution simulations of similar-mass-ratio systems than LIGO. LISA will also be able to detect extreme-mass-ratio inspiral …

Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Showcase.

Optimization Of Orbital Trajectories Using Neuroevolution Of Augmenting Topologies, Nathan Wetherell

University Scholar Projects

This project aims to determine the feasibility of using NeuroEvolution of Augmenting Topologies (NEAT), an advanced neural network evolution scheme, to optimize orbital transfer trajectories. More specifically, this project compares a genetically evolved neural network to a standard Hohmann transfer between Earth and Mars. To test these two methods, an N-body simulation environment was created to accurately determine the result of gravitational interactions on a theoretical spacecraft when combined with planned engine burns. Once created, this simulation environment was used to train the neural networks created using the NEAT Python module. A genetic algorithm was used to modify the topology …

“Lasso The Moon? Is It Possible? What About Hack The Moon? Today’S International Framework For Activities On The Moon”, Diane M. Janosek, Armando Seay, Josa P. Natera

Military Cyber Affairs

The global interest in the moon and outer space continues to skyrocket. The current U.S. commercial investment in space is $350 billion annually, and it is expected to grow to $1 Trillion or more by 2040. The U.S. military investment in space defense and research likewise continues to grow, with the total investment amount remaining classified. With the frequent activity in space, as well as concerns about attacks to US space assets to and from space, the U.S, created the United States Space Command and its Space Force. With private space travel, nanosatellites, lunar exploration, and the proliferation of space …

On The Horizon: Nanosatellite Constellations Will Revolutionize The Internet Of Things (Iot), Diane Janosek

Seattle Journal of Technology, Environmental, & Innovation Law

The Internet of Things has experienced exponential growth and use across the globe with 25.1 billion devices currently in use. Until recently, the functionality of the IoT was dependent on secure data flow between internet terrestrial stations and the IoT devices. Now, a new alternative path of data flow is on the horizon.

IoT device manufacturers are now looking to outer space nanosatellite constellations to connect to a different type of internet. This new internet is no longer terrestrial with fiber cables six feet underground but now looking up, literally, 200 to 300 miles above the earth, to communicate, connect …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Deep Learning Detection In The Visible And Radio Spectrums, Greg Clancy Murray

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep learning models with convolutional neural networks are being used to solve some of the most difficult problems in computing today. Complicating factors to the use and development of deep learning models include lack of availability of large volumes of data, lack of problem specific samples, and the lack variations in the specific samples available. The costs to collect this data and to compute the models for the task of detection remains a inhibitory condition for all but the most well funded organizations. This thesis seeks to approach deep learning from a cost reduction and hybrid perspective — incorporating techniques …

Machine Learning And Computer Vision In Solar Physics, Haodi Jiang

Dissertations

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation.

First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in …

Developing A Practice In Remote Sensing For Next-Generation Human Rights Researchers, Theresa Harris, Jonathan Drake, Umesh K. Haritashya, Wumi Asubiaro Dada, Fredy Cumes

Biennial Conference: The Social Practice of Human Rights

Remote sensing is increasingly recognized as an important tool for documenting human rights abuses. When used alongside interviews, case studies, surveys, forensic science, and other well-established research methods in human rights and humanitarian practice, remotely sensed data can effectively geolocate and establish chronologies for mass graves, forced displacement, destruction of cultural heritage sites, and other violations. But as a highly technical field of science that relies on ever-changing technologies, remote sensing and geospatial analysis are not readily accessible for human rights and humanitarian practitioners. The community of practice grew out of innovative work by practitioners at NGOs and specialized inter-governmental …

Analysis Of Titan's Fluvial Features Using Numerical Modeling, Jeshurun Horton

Mechanical Engineering Undergraduate Honors Theses

River channels have been observed near the Huygens probe landing site on the surface of Titan, along with evidence of rounded water ice boulders transported through fluid flow. Evidence near the landing site suggests active flow of liquid methane, which has motivated the study of the effects of sediment load and channel sizes on Titan’s fluvial features. A numerical model is used to determine the viscosity, flow velocity, and critical boulder transport diameter based on channel size, slope, and a range of sediment concentrations. This model achieves two ends: first, observed boulder diameters are used to determine the ideal channel …

Space Science And Social Media: Automating Science Communication On Twitter, Maia Williams

Honors Projects

This project analyzes how social media is used to engage general audiences in astronomy and space science, as well as ways to improve engagement through automation. Tweets from five space science organizations were sampled. The engagement rate for each tweet was calculated from the number of interactions it received. Accounts that tweet more per day had more followers, and accounts with more followers received more interactions. This project also investigated how to build a Twitter bot to automate science communication. Using NASA Application Programming Interfaces, a Twitter bot was written in Python to tweet images taken by the NASA Mars …

Modeling The Spatiotemporal Dynamics Of Active Regions On The Sun Using Deep Neural Networks, Godwill Amankwa

Open Access Theses & Dissertations

Solar active regions are areas on the Sun's surface that have especially strong magnetic fields. Several phenomena that can have significant negative effects on technology and subsequently on human life, such as solar flares and coronal mass ejections (CMEs), are often associated with active regions.Since the physical phenomena underlying the evolution of active regions are still poorly understood, the accurate prediction of solar flares and coronal mass ejections remains an open problem.

Extracting insights from the available datasets of solar activity that can lead to a better understanding of solar active regions has been an important research goal at the …

Standard Non-Uniform Noise Dataset, Andres Imperial, John M. Edwards

Browse all Datasets

Fixed Pattern Noise Non-Uniformity Correction through K-Means Clustering

Fixed pattern noise removal from imagery by software correction is a practical approach compared to a physical hardware correction because it allows for correction post-capture of the imagery. Fixed pattern noise presents a unique challenge for de-noising techniques as the noise does not present itself where large number statistics are effective. Traditional noise removal techniques such as blurring or despeckling produce poor correction results because of a lack of noise identification. Other correction methods developed for fixed pattern noise can often present another problem of misidentification of noise. This problem can result …

Image Analysis Of Charged Bimodal Colloidal Systems In Microgravity., Adam J. Cecil

Electronic Theses and Dissertations

Colloids are suspensions of two or more phases and have been topics of research for advanced, tunable materials for decades. Stabilization of colloids is typically attributed to thermodynamic mechanisms; however, recent studies have identified transport or entropic mechanisms that can potentially stabilize a thermodynamically unstable colloidal system. In this study, suspensions of silsesquioxane microparticles and zirconia nanoparticles were dispersed in a nitric acid solution and allowed to aggregate for 8-12 days in microgravity aboard the International Space Station. The suspensions were subsequently imaged periodically at 2.5x magnification. Due to the inadequacy of existing image analysis programs, the python package “Colloidspy” …

Snore: An Intuitive Algorithm For Accurately Simulating N-Body Orbits, Connor L. Nance

Honors College Theses

We present SnOrE (Simple n-body Orbital Engine), a Python package which aims to simulate n-body orbital systems while simultaneously overcoming early educational barriers of computational astrodynamics for undergraduate physics students. SnOrE exploits rudimentary syntax and commonly-understood Python libraries to accurately simulate orbits of systems, given initial position and momentum conditions of each body in the system. As the n-body problem is as of yet unsolvable theoretically for n ≥ 3, having a numerical perspective on complicated orbits is of great importance to potentially understanding the processes of star and planet formation. Especially significant examples of this research …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …

Machine Learning For Scientific Data Mining And Solar Eruption Prediction, Hao Liu

Dissertations

This dissertation explores new machine learning techniques and adapts them to mine scientific data, specifically data from solar physics and space weather studies. The dissertation tackles three important problems in heliophysics: solar flare prediction, coronal mass ejection (CME) prediction and Stokes inversion.

First, the dissertation presents a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a certain class of solar flare within the next 24 hours. The essence of this approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. The …

Earth-Like Planet In A Binary Star System, Melissa Kamrowski

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper was written as a final lab report for a computer modeling class. It uses dynamical simulations to investigate the behavior of a planet with the mass and velocity magnitude of the Earth placed in a low mass binary star system, which was loosely based on the Sirius binary. The simulations are completed using gravitational force properties along with the Verlet algorithm, then the results were observed through a modeling program, in which each set was set as an animation to determine behavior. Results came from 40 strategic starting points, which were chosen to cover a large spread of …

Computational Astronomy: Classification Of Celestial Spectra Using Machine Learning Techniques, Gayatri Milind Hungund

Master's Projects

Lightyears beyond the Planet Earth there exist plenty of unknown and unexplored stars and Galaxies that need to be studied in order to support the Big Bang Theory and also make important astronomical discoveries in quest of knowing the unknown. Sophisticated devices and high-power computational resources are now deployed to make a positive effort towards data gathering and analysis. These devices produce massive amount of data from the astronomical surveys and the data is usually in terabytes or petabytes. It is exhaustive to process this data and determine the findings in short period of time. Many details can be missed …

Creating A Sample Of Off-Color Galaxies Using Big Data Tools, Christopher Becker

Honors Program Theses

This thesis begins an investigation into the presence of off-colored galaxies in the Sloan Digital Sky Survey. Through establishing the emergence and history of Astroinformatics, the thesis introduces the concepts surrounding both off-color galaxies and the Big Data tools helpful in analyzing the data to find them. A discussion of initial implementation methods and revised implementation due to difficulties with previous plans follows. Results are presented, with well in excess of 500,000 candidates for off-color galaxies present in the sample. Conclusions are then drawn regarding such a large sample and the implications this may have on the conventional understanding of …

Searching For Needles In The Cosmic Haystack, Thomas Ryan Devine

Graduate Theses, Dissertations, and Problem Reports (ETD)

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best …

Extreme Ultraviolet Quasar Colours From Galex Observations Of The Sdss Dr14q Catalogue, Daniel E. Vanden Berk, Sarah C. Wesolowski, Mary J. Yeckley, Joseph M. Marcinik, Jean M. Quashnock, Lawrence M. Machia, Jian Wu

Computer Science Faculty Publications

The rest-frame far to extreme ultraviolet (UV) colour–redshift relationship has been constructed from data on over 480,000 quasars carefully cross-matched between SDSS Data Release 14 and the final GALEX photometric catalogue. UV matching and detection probabilities are given for all the quasars, including dependencies on separation, optical brightness, and redshift. Detection limits are also provided for all objects. The UV colour distributions are skewed redward at virtually all redshifts, especially when detection limits are accounted for. The median GALEX far-UV minus near-UV (FUV − NUV) colour–redshift relation is reliably determined up to z ≈ 2.8, corresponding to rest-frame wavelengths as …

Searches For Fast Radio Bursts Using Machine Learning, Devansh Agarwal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fast Radio bursts (FRBs) are enigmatic astrophysical events with millisecond durations and flux densities in the range 0.1-100 Jy, with the prototype source discovered by Lorimer et al. (2007). Like pulsars, FRBs show the characteristic inverse square sweep in observing frequency due to propagation through an ionized medium. This effect is quantified by the dispersion measure (DM). Unlike pulsars, FRBs have anomalously high DMs, which are consistent with an extragalactic origin. Over 100 FRBs have been published at the time of writing, and 13 have been conclusively identified with host galaxies with spectroscopically determined redshifts in the range 0.003 ≤ …

Event Reconstruction In The Advanced Particle-Astrophysics Telescope, Emily Ramey

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Advanced Particle-Astrophysics Telescope (APT) is a concept for a gamma-ray space telescope operating in the keV to MeV energy range. Due to the nature of the telescope and the physics of detection, reconstructing initial photon trajectories can be very computationally complex. This is a barrier to the real-time detection of astrophysical transient phenomena such as Gamma Ray Bursts (GRBs), and a faster reconstruction algorithm is needed in order to effectively study them. In this project, we develop such an algorithm based on Boggs & Jean (2000) and discuss the effects of certain algorithmic parameters on computational performance. For testing, …

Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier, Michael J. Brice, Răzvan Andonie

All Faculty Scholarship for the College of the Sciences

The classification of stellar spectra is a fundamental task in stellar astrophysics. Stellar spectra from the Sloan Digital Sky Survey are applied to standard classification methods, k-nearest neighbors and random forest, to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into a complete Morgan Keenan classification (spectral and luminosity) using a single classifier. The motion of stars (radial velocity) causes machine-learning complications through the feature matrix when classifying stellar spectra. Due to the nature …

Quantum Algorithms With Applications To Simulating Physical Systems, Anirban Ch Narayan Chowdhury

Physics & Astronomy ETDs

The simulation of quantum physical systems is expected to be an important application for quantum computers. The work presented in this dissertation aims to improve the resource requirements of quantum computers for solving simulation problems, by providing both novel quantum algorithms and improved implementations of existing ones. I present three main results that cover diverse aspects of simulation including equilibrium physics, the preparation of useful quantum states, and simulations based on classical stochastic processes. The results rely on established quantum algorithms and other recent techniques which I review. My first original contribution is a new quantum algorithm to sample from …

Arecibo Message, Joshua P. Tan

Open Educational Resources

This two week assignment asks students to interpret and analyze the 1974 Arecibo Message sent by Drake and Sagan. Week 1 introduces the concepts behind the construction of the message and engages with a critical analysis of the architecture and the contents of the message. Week 2 asks students to develop software in a Jupyter Notebook (available for free from the Anaconda Python Distribution) to interpret messages that were similar to those produced by Drake and Sagan.

Real-Time Rfi Mitigation In Radio Astronomy, Emily Ramey, Nick Joslyn, Richard Prestage, Michael Lam, Luke Hawkins, Tim Blattner, Mark Whitehead

Senior Honors Papers / Undergraduate Theses

As the use of wireless technology has increased around the world, Radio Frequency Interference (RFI) has become more and more of a problem for radio astronomers. Preventative measures exist to limit the presence of RFI, and programs exist to remove it from saved data, but the use of algorithms to detect and remove RFI as an observation is occurring is much less common. Such a method would be incredibly useful for observations in which the data must undergo several rounds of processing before being saved, as in pulsar timing studies. Strategies for real-time mitigation have been discussed and tested with …

Machine Learning Pipeline For Exoplanet Classification, George Clayton Sturrock, Brychan Manry, Sohail Rafiqi

SMU Data Science Review

Planet identification has typically been a tasked performed exclusively by teams of astronomers and astrophysicists using methods and tools accessible only to those with years of academic education and training. NASA’s Exoplanet Exploration program has introduced modern satellites capable of capturing a vast array of data regarding celestial objects of interest to assist with researching these objects. The availability of satellite data has opened up the task of planet identification to individuals capable of writing and interpreting machine learning models. In this study, several classification models and datasets are utilized to assign a probability of an observation being an exoplanet. …