REU - Research Projects | Institute for Space Weather Sciences

Focus Area 1: Solar Astrophysics

Project #1: Statistical Study of Solar Prominences

Primary Mentor: Research Prof. Vasyl Yurchyshyn
Co-Mentor: Dr. Xu Yang
Type of Project: 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 and coronal cavities 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 (coronal mass ejections, CMEs) 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. 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 #2: Tracing Energetic Electrons in the Solar Corona

Primary Mentor: Dr. Sijie Yu
Co-Mentor: Dr. Surajit Mondal
Type of Project: Data Analysis
Project Description: Energetic electrons can be produced in the Sun's million-degree outer atmosphere-the solar corona in a variety of solar activities, such as jets, flares, and coronal mass ejections. Solar energetic electrons are of particular interest as they carry crucial information for understanding a ubiquitous phenomenon in the heliospheric and many astrophysical plasma environments—particle acceleration. The project involves reducing and analyzing radio data obtained by the Very Large Array (VLA) and multiple wavelengths data from a suite of NASA spacecraft to investigate energetic electron events in the solar corona.
Expected Outcomes: The students are expected to learn radio interferometry techniques and the physics of radio wave emission and electron acceleration in the solar corona. Students will receive training on analyzing and interpreting multi-wavelength observation data using a series of software tools (mostly in Python).
Preferred Skills: Some experience in Python programming is preferred.

Project #3: “Jets” on the Sun: joint radio and extreme ultraviolet observations

Primary Mentors: Prof. Bin Chen, Prof. Pankaj Kumar (NASA Goddard Space Flight Center)
Co-Mentor: Meiqi Wang
Type of Project: Data Analysis
Project Description: Solar “jets” refer to highly collimated plasma outflows in the solar atmosphere. Understanding their origin helps in forecasting space weather and its impacts. Solar jets are thought to be powered by a process known as “magnetic reconnection,” during which magnetic field lines suddenly reconfigure and release a large amount of energy. The released magnetic energy is capable of heating plasma to multi-million degrees and accelerating charged particles to very high speeds; the former produces intense extreme ultraviolet (EUV) emission at the flaring site, and the latter generates radio emission that can sometimes outshine the entire Sun. The project will involve first identifying solar jet events with joint radio and EUV observations made by NJIT’s Expanded Owens Valley Solar Array (EOVSA) and NASA’s Solar Dynamics Observatory (SDO), respectively, as well as reducing and analyzing the multi-wavelength data. This is a collaborative project with researchers at NASA’s Goddard Space Flight Center (GSFC). The project involves regular virtual meetings with NASA/GSFC researchers and may include a short trip to GSFC in Greenbelt, MD.
Expected Outcomes: The student will learn basic methods for reducing and analyzing radio spectral imaging data based on Fourier synthesis techniques. They will also receive training on retrieving, visualizing, and processing high-resolution EUV images using Python-based software tools. Students will apply basic physics knowledge to understand and interpret these solar images made at multiple wavelengths.
Preferred Skills: Some experience in Python programming is preferred.

Project #4: 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 #5: Operating the Expanded Owens Valley Solar Array

Primary Mentor: Prof. Dale Gary
Co-Mentors: Owen Giersch and Brian O'Donnell
Type of Project: Instrument Operations and Calibration
Project Description: The EOVSA features the ability to conduct operations remotely, including arranging the daily observing schedule, performing required calibrations, and recording and pipeline-processing the data. This task is normally done by graduate students, who spend one week on duty in rotation. This project involves first training the undergraduate student on the basics of operation of the instrument and familiarity with the data, and then gives the student hands-on access and responsibility (under supervision) for operating the instrument and performing the calibrations.
Expected Outcomes: This is an opportunity for the student to go beyond simply working with canned data, and exposes them to the nuts and bolts of planning observations, acquiring, and calibrating the data. The student will learn not only the daily calibration methods, which are largely automated, but will also investigate the longer-term “engineering” calibrations needed to keep a major facility operating smoothly. Ultimately, the student will have the satisfaction of analyzing data they have personally acquired, and use it together with spacecraft data to explore the state of the solar atmosphere during their observing week.
Preferred Skills: Experience in the Python programming language preferre

Project #6: Trigger Mechanisms for Solar Flares

Primary Mentor: Dr. Jeongwoo Lee
Co-Mentor: Qin Li
Type of Project: Data Analysis
Project Description: How solar flares are triggered is one of the central issues in astrophysics and space weather. For solar flares, magnetic flux emergence and cancellation, shearing motion, and sunspot rotation observed in the photosphere play an important role in the energy buildup and flare trigger. We will address this issue using the Sun's coronal and magnetic field data obtained with the NASA’s Solar Dynamics Observatory (SDO) mission, especially extreme ultraviolet images from the Atmospheric Imaging Assembly (AIA) and magnetic field data from the Helioseismic and Magnetic Imager (HMI).
Expected Outcomes: We will go over the SDO data of a specific set of solar eruption events to measure the EUV emissions from flare ribbons and associated magnetic fields in the flaring active regions. From the measured parameters we will find clues to yet-unknown trigger mechanisms for solar major flares, which will help us understand their physical nature, origin and driving mechanisms as well as the role they may play in heating and mass flows in the solar atmosphere. The students will learn how to read and calculate magnetic quantities associated with solar flare energy release and how basic knowledge about MHD instabilities in plasma physics works are used to interpret solar phenomena.

Project #7: Magnetohydrodynamic Simulation of Coronal Magnetic Field Evolution and Eruption

Primary Mentor: Prof. Satoshi Inoue
Co-Mentor: Nian Liu
Type of Project: Simulation
Project Description: Solar flares and eruptive phenomena observed in the Sun are the largest explosions in our solar system. It is widely believed that these phenomena are carried primarily by the coronal magnetic fields. Although many of these phenomena have been observed with the latest ground-based telescopes and solar satellites, three-dimensional (3D) structure and dynamics of the magnetic fields have not been fully understood. In this project, the student will conduct a magnetohydrodynamic (MHD) simulation combined with the latest observed magnetic field taken from Solar Dynamics Observatory or Big Bear Observatory, and reveal the 3D dynamics of the coronal magnetic fields in solar flares and eruptions.
Expected Outcomes: The students will learn simulation research, how to make a program, how to run the simulation program, and how to visualize 3D physical values. At the same time, they will learn parallelized computing techniques by using a new NJIT cluster computer, SOLALAB. By analyzing the 3D simulation data, they will explore the questions for evolution and eruption of the coronal magnetic fields.

Project #8: 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.

Focus Area 2: Terrestrial Physics

Project #10: 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 #11: Searching for signatures of ionospheric variability with a high frequency radio link between Colorado and New Jersey

Primary Mentor: Prof. Gareth Perry
Co-Mentor: TBD
Type of Project: Data Analysis and Modeling
Project Description: Variability in the Earth’s upper-atmosphere and ionosphere due to space weather interactions can manifest as Doppler frequency variations in long-distance high frequency (HF; 3 - 30 MHz) radio links. A HF receiver located on campus at NJIT has been recording transmissions from the WWV AM station located in Fort Collins, Colorado, transmitting at 10 MHz, for nearly two years. The goal of this project is to - for the first time - analyze this data set for Doppler variations of WWV’s signatures and explore their cause.
Expected Outcomes: The students will learn about radio and remote techniques, the structure, composition, and dynamics of the terrestrial ionosphere, and the physics of electromagnetic wave propagation in a weakly-ionized plasma (i.e., 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 #12: 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 #13: Whistler Waves in the Solar Win

Primary Mentor: Dr. Ilya Kuzichev
Co-Mentor: TBA
Type of Project: Data Analysis and Modeling
Project Description: Whistler waves are one of the most important wave modes in space plasmas, in the solar wind particularly, due to their role in electron acceleration and scattering. Spacecraft missions, such as WIND, ARTEMIS, and Parker Solar Probe have generated and continue gathering a lot of data including particle and fields measurements in different regions of the solar wind. The primary goal of this research project is to use this data to improve our understanding of whistler wave properties and generation mechanisms in the solar wind.
Expected Outcomes: Students are expected to combine modeling and data analysis in their research. From the modeling perspective, they will learn some basic theoretical aspects of plasma physics, such as dispersion equations; they will learn how to work with dispersion equation numerical solvers. From the data analysis perspective, the students will learn how to process magnetic and electric field data measured aboard different satellites, they will get familiar with such techniques as spectral analysis and minimum variance analysis. Students will compare linear theory predictions based on measured velocity distribution functions and the actual wave observations.

Project #14: Analysis of Atmospheric Hydroxyl Emissions from the HODI Fabry-Perot System

Primary Mentor: Matthew Cooper
Co-Mentor: Dr. John Meriwether
Type of Project: Data Analysis
Project Description: Understanding of the Earth’s upper atmosphere has been a goal of science for many decades, and has given rise to an entire field of research named aeronomy. This understanding is intrinsically tied to technological advancements. The Hot Oxygen Doppler Imager (HODI) is at the forefront of said technological advancements, incorporating a high-sensitivity charge-coupled device (CCD) camera, apochromatic lens assembly, large aperture (15 cm) Fabry-Perot etalon, multiple narrow-band filters, and a smart-motor driven twin-mirror SkyScanner. Combining this with natural emission lines from particles at different atmospheric levels allows for ground–based deduction of the wind speed and temperatures at a variety of altitudes. This project will focus on the hydroxyl emission which presents in HODI’s 732 nanometer filter band. This emission is created by hydroxyl molecules in the Earth’s mesosphere. Analysis of this emission will allow the recovery of the wind speed and temperature at an altitude well out of reach of aircraft and most spacecraft measurements.
Expected Outcomes: Participants in this project will learn the fundamentals of Fabry-Perot interferometry in the field of aeronomy. They will follow the emission path from the source region in the atmosphere through the various components of the instrument mentioned above, and analyze the resultant Airy patterns to recover the bulk speed and temperature of the hydroxyl molecules. The data analysis will be done mostly in Python, with the potential to include IDL wrappers at the participant’s interest/skill level.
Preferred Skill: Programming and basic understanding of function fitting

Project #15: Analysis of Environmental Dependence of the HODI Instrument Calibrations

Primary Mentor: Matthew Cooper
Co-Mentor: Dr. John Meriwether
Type of Project: Hardware Fabrication, Software Integration, and Experimental Testing
Project Description: The Hot Oxygen Doppler Imager (HODI) is a state-of-the-art optical Fabry-Perot imager currently deployed at the Kjell Henrikson Observatory in Svalbard to observe during the polar night season. The purpose of HODI is the accurate measurement of both winds and temperatures at high altitudes. As with any experimental apparatus, there is some variance in the measurements due to the instrumentation. With HODI, the measurements can be varied by something as simple as an ambient temperature fluctuation or hand touching the housing of one of the optical components. This creates temperature fluctuations in the optics which expand on the scale of nanometers. In order to account for these fluctuations, a helium neon laser with a known emission spectrum is shot through the optical system periodically to calibrate. The goal of this project is to create a sensor unit which includes temperature, humidity, and pressure sensors which can be placed at various locations within the observatory to track how these environmental variables change over time. These results will then be compared with changes in the laser calibration profiles.
Expected Outcomes: The student will learn the fundamentals of Fabry-Perot systems and their implementation in aeronomy. They will learn about firmware writing and output reading for Arduino microcontrollers, loading operating systems onto Raspberry Pi single-board computer systems, software testing, and sensor calibration.
Preferred Skill: Knowledge/experience with electrical components

Focus Area 3: Data Science in Space Weather

Project #16: Predicting Solar Eruptions and Tracking Magnetic Features through Machine Learning

Primary Mentors: Prof. Jason Wang, Prof. Vincent Oria, Prof. Yao Ma
Co-Mentor: Genwei Zhang, Chunhui Xu.
Type of Project: Data Analysis, Software Development
Project Description: Flares and coronal mass ejections (CMEs) are major sources of solar eruptions. They can cause severe inﬂuences on the near-Earth space environment, resulting in potentially life-threatening consequences. This project will focus on early detection and forecasting of solar eruptions using machine learning. A human-in-the-loop paradigm, commonly used in big data analytics, will be employed to build and refine machine learning models. In addition, new deep learning tools will be developed, also based on the human-in-the-loop paradigm, for tracking magnetic features and tracing fibrils as well as loops. It is expected that these new tools will be faster, and produce better quality results than existing methods.
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.