Research Projects
Focus Area 1: Solar Astrophysics
Project #1: Probing Energy Release in Solar Flares Using Radio and EUV Observations
Primary Mentor: Prof. Bin Chen
Co-Mentor: Yuqian Wei
Type of Project: Data Analysis
Project Description: Solar flares are the most powerful explosions in the solar system, releasing a large amount of energy. The released magnetic energy is capable of heating plasma to multi-million degrees and accelerating 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 reducing and analyzing radio data obtained by NJIT’s Expanded Owens Valley Solar Array (EOVSA) and EUV data from NASA’s Solar Dynamics Observatory, aiming to better understand the flare energy release processes.
Expected Outcomes: The students will learn basic methods for performing radio spectral imaging based on Fourier synthesis imaging 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 solar images made at multiple wavelengths and interpret the observed features in the physical context of solar flare energy release.
Project #2: Study of Small Scale Energy Releases in and around Sunspots
Primary Mentor: Prof. Haimin Wang
Co-Mentor: Dr. Nengyi Huang
Type of Project: Data Analysis
Project Description: The NASA Interface Region Imaging Spectrograph (IRIS) mission has been providing unprecedented high-spatial, temporal and spectral resolution observations of the Sun's chromosphere and transition region (TR) and revealed new small-scale energy release phenomena, such as TR penumbral bright dots, hot explosions in and around active regions as well as jets and brightening above sunspot light bridges. The 1.6 m BBSO/GST has collected a sufficient amount of data in joint BBSO/IRIS/Hinode coordinated observing campaigns since 2013.
Expected Outcomes: Our science goals and objectives are to make thorough analyses of small-scale energy release phenomena occurring above and around sunspots by taking full advantage of the existing IRIS-BBSO coordinated high-resolution multi-wavelength observations and to 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 basic methods to analyze GST and IRIS data. Students are expected to develop a good understanding of small-scale energy releases in and around sunspots.
Project #3: 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.
Project #4: 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 are deemed to 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 and flare energy release from the SDO data.
Project #5: Magnetohydrodynamic Simulation of Coronal Magnetic Field Evolution and Eruption
Primary Mentor: Prof. Satoshi Inoue
Co-Mentor: Nian Liu
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 #6: Tracing Energetic Electrons in the Solar Corona
Primary Mentor: Dr. Sijie Yu
Co-Mentor: Meiqi Wang
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).
Project #7: Photospheric Dynamics and Coronal Heating
Primary Mentor: Research Prof. Vasyl Yurchyshyn
Co-Mentor: Dr. Xu Yang
Type of Project: Data Analysis
Project Description: The question of coronal heating remains one of the most important and unsolved problems in solar physics. Earlier studies computed the rate at which closed simulated field lines open up, and estimated the energy flux released in “reconnection events” using artificially generated small-scale magnetograms. Now that we are capable of producing MCAO wide-field corrected, high time cadence NIRIS magnetic field data, we may perform a similar study but with a substantial difference: the Monte Carlo simulated “magnetograms” will be replaced with MCAO/NIRIS data. In this study the student will prepare a data cube. Special emphasis will be made on the stability of the data and removal of noise. After that the student will perform potential field extrapolations and detect the rate of changes in the field connectivity.
Expected Outcomes: The students will learn the basics of data processing and coronal field extrapolations. The data processing will be done either using IDL and/or Python programming language. Also, the student will learn how to visualize magnetic field lines as well as other data. Moreover they will be introduced to solar observations and learn how solar data are acquired with the 1.6 meter Goode Solar Telescope.
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 #9: Equatorial Wind Observations Using Fabry-Perot Doppler Image Data
Primary Mentor: Prof. Andrew Gerrard
Co-Mentor: Dr. Sovit Khadka
Type of Project: Data Analysis
Project Description: Equatorial winds from Second-generation, Optimized, Fabry-Perot Doppler Imager (SOFDI) data will be investigated, as compared to current models. SOFDI is the world’s only triple etalon Fabry-Perot interferometer and is currently located and operating in Huancayo, Peru. Located under the Earth’s magnetic equator, SOFDI observes thermosphere winds 24-hours a day. Currently, an extensive data set exists that requires additional analysis.
Expected Outcomes: Students will utilize the reduced winds to investigate the pre-reversal enhancement uplift as compared to equatorial ExB drifts, as well as compare to current state-of-the-art model output of said winds.
Project #10: Hurricane and Polar Vortex Variability Observations Using EarthShine Data
Primary Mentor: Prof. Andrew Gerrard
Co-Mentor: TBA
Type of Project: Data Analysis
Project Description: EarthShine data will be analyzed in regards to hurricane detection and polar vortex variability. The EarthShine instrument, located at the Big Bear Solar Observatory, measures Earth’s albedo from Earthshine scattered from the moon. Such measurements have been shown to make global and synoptic observations of cloud cover. While previous studies have focused on global and climatological Earthshine observations [e.g., Goode et al., 2021], this project will focus on synoptic observations during hurricane season and during winter. The later study will focus on the variability of the polar vortex, in its disruption of the polar night jet and cloud cover.
Expected Outcomes: TBA
Project #11: Observations of Geomagnetic Environments
Primary Mentor: Prof. Hyomin Kim
Co-Mentors: Dr. Sungjun Noh, Youra Shin
Type of Project: 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. The primary goal of this focus area is to learn about 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 #12: An Analysis of the Geolocation Algorithms Used by Ionospheric Radars
Primary Mentor: Prof. Gareth Perry
Co-Mentor: Dr. Lindsay Goodwin
Type of Project: Modeling and Data Analysis
Project Description: High Frequency (HF; 3-30 MHz) radars are a valuable tool for monitoring global plasma flows in the ionosphere, the Earth’s upper-atmosphere filled with weakly-ionized plasma. These radars, which include the SuperDARN, rely on a number of assumptions about the ionosphere’s radio wave propagation conditions to geolocate the origin of radar echoes. Some assumptions may be incorrect or inappropriate under certain conditions and should be reevaluated. This project will involve the analysis of SuperDARN radar data, combined with modeling the trajectories of HF radar transmissions through the ionosphere under a variety of geophysical conditions, aiming to assess the validity of assumptions used by SuperDARN’s geolocation algorithms.
Expected Outcomes: The students will learn about radar 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.
Project #13: Whistler Waves in the Solar Wind
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.
Focus Area 3: Data Science in Space Weather
Project #14: Predicting Solar Eruptions and Tracking Magnetic Features through Machine Learning
Primary Mentors: Prof. Jason Wang and Prof. Vincent Oria
Co-Mentor: Haodi Jiang
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.