REU - Research Projects

Focus Area 1: Solar Astrophysics

Project #1: Feature Identification of Solar Prominences

• Primary Mentor: Dr. Vasyl Yurchyshyn
• Co-Mentor: Dr. Xu Yang
• Type of Project: Image processing and Data Analysis
• Project Description: We propose to use an automatic routine to detect solar prominences seen off the limb in BBSO H­alpha and SDO/AIA images. Solar prominences are protrusions above the solar limb best visible in hydrogen alpha line (Ha, 6563A) and ultraviolet images at 304A. The importance of prominences lies in the fact that due to loss of stability they erupt, carrying a significant amount of plasma and magnetic field out into interplanetary space. These eruptions impact the heliosphere in general and the Earth's magnetosphere in particular. Over the past years the solar community has acquired large amounts of data on solar prominences. The current NASA SDO mission generates large quantities of excellent multi-temperature prominence data. We propose to perform a statistical study of solar prominences and a comparative study of the prominence statistics detected during the past and the current solar cycles. In this study the student will apply the prominence identification software to the full disk H-alpha data stored in BBSO archives as well as to full disk AIA 304A images.  The software will need to be modified in order to be applicable to newer data sets. After that the student will perform statistical analysis of the generated data in order to reveal temporal and spatial distributions of the prominence parameters over solar cycles.
• Expected Outcomes: The students will learn the basics of data processing and feature identification. The data processing will be done either using IDL and/or Python programming language. Also, the student will learn the basics of statistical analysis and data visualization. Moreover they will be introduced to solar observations and learn how solar data are acquired with the 1.6 meter Goode Solar Telescope and the full disk H-alpha telescope.

Project #1A: Exploring pre-erupting configuration of magnetic fields in solar active regions

• Primary Mentor: Dr. Vasyl Yurchyshyn
• Co-Mentor: Dr. Xu Yang
• Type of Project: Image processing and Data Analysis
• Project Description: Coronal Mass Ejections (CMEs) have long been identified as a prime cause of large, non-recurrent geomagnetic storms. They often have a magnetic flux rope (MFR) embedded in them, meaning that they harbor a large-scale, coherent magnetic structure with a large amount of twist. One of the goals of solar physics research is to comprehend the evolution of an AR toward an eruption. From a practical standpoint, it is desirable to know the location and timing of the next eruption. We propose a novel approach to solving this long standing problem. The main objective is to detect and parameterize a MFR in extrapolated coronal fields prior to a flare or an eruption. Coronal field extrapolations will be performed using Fleishman et al. (2017) tool that exploits the optimization and weight function methods. The numerical realization of this approach is part of the GX Simulator package, which is freely available from the SolarSoft IDL library. The boundary conditions for the extrapolations will be vector magnetograms provided by the Helioseismic and Magnetic Imager on board Solar Dynamics Observatory. Using extrapolated data cubes we will fine tune our methodology for detecting signatures of strong current systems in the AR corona. We will use existing Q factor package and develop new tools and approaches to quantifying evolution of magnetic configurations and development of MFR that lead to eruptions.
• Expected Outcomes: The students will learn the basics of data processing and feature identification. The data processing will be done either using IDL and/or Python programming language. Also, the student will learn the basics of statistical analysis and data visualization. Moreover they will be introduced to solar observations and learn how solar data are acquired with the 1.6 meter Goode Solar Telescope and the full disk H-alpha telescope.

Project #2: Investigation of Mini-filament Eruptions and Their Relationship with Small Scale Magnetic Flux Ropes in Solar Wind

• Primary Mentor: Prof. Haimin Wang
• Co-Mentor: Dr. Nengyi Huang
• Type of Project: Data Analysis
• Project Description: In recent years,  small-scale MFRs  (SMFRs) in both solar surface and solar wind are receiving significant attention. Initial evidence shows that they are numerous and ubiquitous. Studies of SMFRs are advanced significantly with high resolution observations from the 1.6m Goode Solar Telescope (GST) of Big Bear Solar Observatory (BBSO) on the solar surface, and  solar wind observations from Parker Solar Probe (PSP). Students will carry out comprehensive case and statistical studies of SMFRs in solar surface and solar wind.
• ​Expected Outcomes:
  Students will investigate the kinematic, thermal and magnetic  properties of them,  as well as possible photospheric magnetic field evolution associated with eruption of mini-filaments. In addition, they will anticipate to find possible connection between solar mini-filament eruptions  and detected SMFRs in solar wind.
• Preferred Skill: Took courses in college physics, and some programming skill

Project #3: Instrument Operation and Data Calibration at the Big Bear Solar Observatory

• Primary Mentor: Prof. Wenda Cao
• Co-Mentor: Dr. Nicolas Gorceix and Dr. Xu Yang
• Type of Project: Instrument Operations and Calibration
• Project Description: Big Bear Solar Observatory (BBSO) now operates one of the largest aperture ground-based solar telescopes − the 1.6-meter Goode Solar Telescope (GST) located in Big Bear Lake, California. The GST, equipped with high-order adaptive optics, is the highest-resolution operating solar telescope built in the U.S. in a generation. Currently, the GST has six operational facility-class instruments to generate several TB of data daily in support of scientific research and NASA space missions. This project involves training the undergraduate students, in the telescope dome, on the operation of GST instruments, and offers an access to instrument calibration and data processing.
• ​Expected Outcomes: The project provides undergraduate students with opportunities in scientific research and instrument development at the leading edge, in an environment designed to stimulate their scientific research and interest in instrumentation, to acquaint them with scientific methodology, to cultivate their creativity, and to train them to be the next generation of solar physicists and instrument engineers.

Project #4: Oscillations and Magnetic Activity of Solar-type Stars

• Primary Mentor: Prof. Alexander Kosovichev
• Co-Mentor: Dr. Krishnendu Mandal
• Type of Project: Data analysis
• Project Description: The magnetic activity of the Sun and solar-type stars plays a crucial role in the space weather conditions on planets of the solar and stellar systems. The source of the magnetic activity is dynamo, driven by turbulent convection and rotation in the solar and stellar interiors. The information about physical processes inside the Sun and stars can be obtained by analyzing solar and stellar pulsations observed by NASA’s space missions, Solar Dynamics Observatory (SDO), Kepler, and Transiting Exoplanet Survey Satellite (TESS). This project aims to investigate stellar pulsations and variability associated with magnetic activity (such as starspots and flares) for a sample of solar-type stars by using available software tools developed for analyzing Kepler and TESS data, and to compare the results with observations of our Sun.
• Expected outcomes:  The students will learn characteristics of high-precision solar and stellar observations from space, fundamentals of signal processing and data analysis, principles of helio- and asteroseismology, and basic physical properties of solar and stellar activity.  The results of this project will shed light on the properties of stellar oscillations and activity, as well as on the evolutionary history of the Sun.
• Preferred Skills: Some experience in Python programming is preferred.

Project #5: Temporal Variability in Solar Acoustic Wave Properties Across Solar Cycles

• Primary mentor: Dr. Krishnendu Mandal
• Co-Mentor: Prof. Alexander Kosovichev
• Type of Project: Data analysis and modeling
• Project Description: Solar acoustic waves represent our primary method for imaging the solar interior. Originating just below the solar surface, these waves traverse through the inner layers. As the speed of sound increases from the surface towards the core, waves refract back when reaching the center, continuously resurfacing throughout their journey. This propagation and resurfacing carry invaluable information about the solar interior. Any alteration in the solar interior properties reflects in these waves, making it pivotal to comprehend interior dynamics. The solar magnetic field's changes across its 11-year cycle significantly impact wave propagation. Parameters like frequencies, amplitudes, and line-width undergo fluctuations during this cycle. With over two decades of Helioseismic observation data, we now possess an opportunity to re-examine how these parameters evolve throughout the solar cycle. Within this project, students will endeavor to address these inquiries: Do these phenomena display an 11-year periodicity, and which parameters demonstrate greater sensitivity to variations in the magnetic field?
• Expected Outcomes: The program will equip students with the skills to access and analyze solar observational data from sources like NASA’s Solar Dynamic Observatory (SDO), Solar and Heliospheric Observatory (SOHO), and the ground-based observatory NSO/GONG. Additionally, students will delve into understanding stellar oscillation modeling. Exploring a star's properties, such as mass and radius, will unravel how these attributes determine oscillation frequencies, akin to the functioning of a musical instrument. Furthermore, students will master the utilization of Python packages like Scipy and Numpy. This project offers students the opportunity for an in-depth exploration of data analysis, stellar oscillation modeling, and aims to spark their passion for research in this field. The skills acquired will not only benefit aspiring researchers but also prove valuable across diverse disciplines.

Project #6: Tracing Flare Energy Release Through Atmospheric and Helioseismic Waves

• Primary Mentor: Dr. John Stefan
• Co-Mentor: Prof. Alexander Kosovichev
• Type of Project: Data analysis
• Project Description: Solar flares occur when the Sun’s magnetic field spontaneously reorganizes and releases energy. This process sometimes, but not always, excites various types of waves, including helioseismic waves (those that penetrate and travel throughout the entire Sun) and atmospheric waves such as the fast-traveling EUV (Extreme Ultraviolet) and Moreton waves. Though there is a tendency for more intense flares to be more wave-active, this alone doesn’t explain why only some flares excite these waves. In this project, the student will analyze observations of the solar surface and atmosphere during flares as well as descriptors of flare energy release, such as soft and hard X-ray measurements, to gain a better understanding of the background conditions and flare characteristics that ultimately lead to impulsive wave generation.
• Expected Outcomes: The student will learn how to access and analyze solar observations, including those from NASA’s Solar Dynamics Observatory and NOAA GOES satellites, as well as the physical processes that lead to these observations. This includes an understanding of Doppler and ultraviolet imaging, X-ray emission, and dynamics of the solar atmosphere. The student will also learn useful data processing techniques such as reprojection and various image normalization procedures, in addition to the utilization of the popular Astropy and Scipy Python packages that are used across a broad range of disciplines.

Project #7: Magnetohydrodynamic simulation of solar eruption: Numerical experiment for Predicting Magnetic Field Eruption

• Primary mentor: Dr. Nian Liu
• Co-mentor: Prof. Satoshi Inoue
• Type of Project: Simulation 
• Project description: Solar eruptions are a source of solar storms that produce large electromagnetic disturbances in geomagnetic space. Therefore, predicting the solar eruption is very important to achieve more precious space weather forecasts. In this project, we offer the simulation study of solar eruptions using realistic magnetic fields that are extrapolated from the observed photospheric magnetic field.  First,  focus on the large solar flares and the final goal is to figure out when, where, and how the magnetic flux rope, which is the core of an erupting magnetic field, is formed and erupt. 
• Expected outcome: Students will learn the programming skill, and procedure of the simulation study including post-process of the simulation and visualization. Also, they will learn parallel computation and how to handle the parallelized data. Throughout this project, students are able to develop physical insights needed to understand the phenomena occurring in the simulation.  
• Prefered skill: Experience in programming, and knowledge of  electromagnetics and fluid dynamics are desirable. 

Project #13: Analysis of Coronal Mass Ejections Using OVRO-LWA Radio Imaging

• Primary Mentor: Dr. Peijin Zhang
• Co-mentor: Prof. Bin Chen
• Type of Project: Data Analysis and Radio Astronomy
• Project Description: Coronal Mass Ejections (CMEs) play a critical role in influencing space weather, and their study is vital for predicting and mitigating their impact on Earth's technological systems. By studying the radio emissions of CMEs, this project aims to contribute valuable insights into its evolution in the middle corona (1.5-3 R_sun). This research project aims to conduct a detailed study of CME through the unique lens of radio astronomy, utilizing the cutting-edge capabilities of the Owens Valley Radio Observatory-Long Wavelength Array (OVRO-LWA). OVRO-LWA offers unparalleled continuous low-frequency radio imaging of the Sun in decameter wavelength (20-80MHz), making it an exceptional tool for investigating the complexities of CMEs in the middle corona (1.5-3 R_sun). During the project, the students will have access to an extensive archive of data from the solar high year (2024), including real-time recordings. The primary focus will be on using the radio emissions of CMEs to infer the magnetic field structure and plasma parameters of the CME, utilizing the spectroscopy imaging data by OVRO-LWA. The OVRO-LWA solar dataset provides an unprecedented opportunity to delve into the CME evolution in the middle corona. The student will learn how to process the OVRO-LWA solar data. With the help of the mentor, the student will perform the data reduction for a CME, and analyze the radio emission features.
• Expected Outcomes: Students engaged in this project will gain hands-on experience in data analysis, specifically in the context of radio astronomy and solar physics. They will develop a deeper understanding of plasma physics and the dynamics of solar events. The project will also enhance their skills in programming and data processing, particularly in the realm of radio astronomy data. Event analysis summarized and prepared for scientific publication.
• Skill Requirements: Knowledge in introductory mechanics, thermodynamics, and electromagnetism. Experience of Python programming is a plus but not a requirement. Basic knowledge of solar physics is advantageous. Experience in radio data processing is helpful but not mandatory.

Focus Area 2: Terrestrial Physics

Project #8: Observations of Magnetosphere-Ionosphere Coupling Using Magnetometer Network

• Primary Mentor: Prof. Hyomin Kim
• Co-Mentors: Youra Shin
• Type of Project: Instrumentation and Data Analysis
• Project Description: Observations of geomagnetic environments is a critical part of geospace research as the Earth’s magnetic fields are highly susceptible to the solar activity mainly via the solar wind. One of the most important and widely-used instruments is "magnetometer" which measures the magnetic field intensity and direction. The primary goal of this project is to learn about the instrument and analysis techniques for magnetic field data from spacecraft and ground-based magnetometers to study important geomagnetic activities such as geomagnetic storms, substorms, waves and how they are related with solar wind parameters.  
• ​Expected Outcomes: Students are expected to learn how solar wind, magnetosphere, and ionosphere are coupled in the context of geomagnetic fields and current systems primarily using magnetometer data and relevant analysis techniques (e.g., spectral analysis). They are also expected to understand how magnetometer data are acquired and processed for scientific use.

Project #9: An incoherent scatter radar investigation of polar-cap F-region plasma structuring and dynamics

• Primary Mentor: Prof. Gareth Perry
• Co-Mentor: None.
• Type of Project: Data Analysis and Modeling
• Project Description: Both the northern and southern polar-cap regions of the terrestrial ionosphere are home to highly structured and variable plasma dynamics that is not well understood. One of the most powerful techniques used or studying this region is the incoherent scatter radar technique, which provides estimates of the ionospheric plasma's state parameters (density, line-of-sight plasma temperature, and line-of-sight plasma velocity). Two Advanced Modular Incoherent Scatter Radars (AMISRs) located at Resolute Bay use electronic beam steering techniques to generate volumetric estimates of the aforementioned parameters, which have allowed for unprecedented observations of the complex plasma processes occurring in the polar-cap region. This project will employ both data analysis and instrument modeling of the AMISRs at Resolute Bay to gain new insight into the nature of the complex plasma structuring and dynamics present in the northern polar-cap.
• Expected Outcomes: The students will learn about radio and remote sensing techniques, and learn about the structure, composition, and dynamics of the terrestrial ionosphere. The students are expected to develop strong data analysis and software development skills in both MATLAB and Python.
• Preferred Skill: Software programing experience in MATLAB and/or Python

Project #10: Ion-Neutral Heating Observed with Fabry-Perot Interferometers and SuperDARN

• Primary Mentor: Prof. Lindsay Goodwin
• Co-Mentor(s): Matthew Cooper
• Type of Project: Data Analysis
• Project Description: As ions and neutral particles collide in the upper atmosphere, they generate heating which cascades throughout the atmosphere. These collisions can have damaging consequences for spacecraft, as well as long distance radio communication. However, the drivers of this heating, as well as the scales-sizes they cascade to, are not always known. In this work, observations of the ionosphere (charged particles) and thermosphere (neutral particles) over New Jersey are compared against each other, as well as geomagnetic activity parameters, to gain a better understanding of what drives high-altitude heating at midlatitudes. The ionosphere is measured using observations from the Super Dual Auroral Radar Network (SuperDARN), and the thermosphere is measured using the Jenny Jump Fabry-Perot Interferometer.
• Expected Outcomes: By the end of this project, students will understand remote sensing techniques in relation to interferometry and radars. By comparing these two datasets, students will also learn more about ionospheric-thermospheric dynamics (plasma-neutral dynamics) and be able to develop computational skills to manipulate these datasets to output information about the occurrence of ion-neutral frictional heating.

Project #11: Analysis of Atmospheric Hydroxyl Emissions from the HODI Fabry-Perot System

• Primary Mentor: Dr. Matthew Cooper
• Co-Mentor: Dr. John Meriwether
• Type of Project: Data Analysis
• Project Description:  The Hot Oxygen Doppler Imager (HODI) is an optical instrument currently deployed at the Kjell Henriksen Observatory (KHO) 15 km outside Longyearbyen on Svalbard.  HODI enables the measurement of neutral oxygen winds and ionized oxygen drifts in the thermosphere and ionosphere.  In January 2024, these measurements will be made alongside dedicated experiments run on the European Incoherent Scatter Scientific Association Incoherent Scatter Radar (EISCAT-ISR).  The participant will help to investigate this sensor fusion experiment and use known physical links between observed values to compare the two datasets.  This project will involve data manipulation in a programming environment common to the space sciences community (Python, Matlab, and/or IDL).  
• Outcomes:  Participant will learn about physical processes from several scientific sub-disciplines: light emitted from different atoms/molecules (quantum mechanics), Fabry-Perot optical resonators (optics), behavior of winds and ion drifts in the thermosphere and ionosphere (aeronomy), and how physical interactions affect the brightness of the particles’ emitted light (physical chemistry).  The participant will also learn about different remote sensing techniques such as Incoherent Scatter Radar (ISR) and Fabry-Perot Interferometers (FPIs).  The participant will explore a novel dataset for the 10-week period and make conclusions on the physical processes thought to govern these observations.  They will gain data communication and visualization skills.

Focus Area 3: Data Science in Space Weather

Project #12: Predicting Solar Eruptions and Tracking Magnetic Features through Machine Learning

• Primary Mentors: Prof. Bo Shen and Prof. Jason Wang
  Co-Mentor: Dr. Qin Li, Chenyang Li, Marco Marena, Chunhui Xu.
• Type of Project: Data Analysis, Physics-informed Machine Learning, Software Development
• Project Description: Flares and coronal mass ejections (CMEs) represent significant sources of solar eruptions, capable of exerting profound impacts on the space environment near Earth, potentially leading to life-threatening consequences. This initiative aims to address these challenges through two primary objectives: (1) Implementing early detection and prediction of solar eruptions using machine learning techniques. (2) Generating machine learning-synthesized vector magnetograms for CME-producing active regions to extract 3D magnetic field information within the solar corona.
  The initial phase of the project will utilize conventional machine learning methods to address various data science challenges. Subsequently, advanced deep learning models, including physics-informed neural networks and neural operators, will be developed. These state-of-the-art models are anticipated to exhibit enhanced speed and produce higher-quality results compared to existing methods, benefiting from the incorporation of physics principles during model construction.
• Expected Outcomes: The students will learn basic machine learning models including random forests, support vector machines, and neural networks. Python-based libraries will be introduced. Students will receive training on writing machine learning programs or modify existing programs available from GitHub. More importantly, they will learn how to use these machine-learning tools to analyze solar data for predicting solar eruptions and tracking patterns in the data.