Magnetohydrodynamical (MHD) turbulence is a ubiquitous phenomenon in the universe. The intermittency of MHD turbulence at the at the dissipation scale causes the formation of current sheets -- two-dimensional sheet-like structures of intense current flows. The magnetic field across these sheets usually experiences a reversal in polarity. Through tearing-mode instability, current sheets can tear, allowing magnetic reconnection, which is important in heating the plasma as well as accelerating particles. As a pre-doctoral fellow at the Center for Computational Astrophysics (CCA), I am working with my mentors to develop a machine learning algorithm to automatically segment and track current sheets in 3D plasma simulations. We call our algorithm aweSOM. We find that in many test cases, the algorithm can effectively segment the current sheets from simulations, and we are actively working on refining the technique for public release.
Advisors: Joonas Nättilä, Jordy Davelaar, and Lorenzo Sironi (CCA / Columbia University)

Current state-of-the-art numerical simulations of supermassive black holes at the center of galaxies are largely successful at recreating the accretion flows observed via telescopes, especially on the galaxy-scale (~0.1 to hundreds of kpc) and the event-horizon-scale (~10^-2 pc). However, our current theoretical understanding of the accretion flow between the Bondi radius and the outer edge of the accretion disk (the meso-scale) lags behind observations. My Ph.D. thesis aims to provide theoretical explanations for the observed meso-scale accretion flows. To this end, I am designing galaxy-scale MHD simulations of the accretion flow and relativistic jet feedback motivated by observations of the elliptical galaxy M87 using the adaptive mesh-refinement code Athena++. Then, through a recently developed nested zoom-in technique, I plan to construct meso-scale simulations of the same system to provide boundary and initial conditions at the horizon-scale for future GRMHD simulations of the accretion disk.
Advisor: Yuan Li (UNT)

Stars are born out of the turbulent molecular gas of the interstellar medium. Once formed, they decouple from the surrounding gas, but still retain their pre-natal kinematics. Using Gaia astrometric measurements in conjunction with APOGEE-2, we calculate the velocity structure function of stars in four nearby star-forming regions and compare them to the structure functions found in Hα and CO gas.
In our 2022 paper, we found that the structure functions of the four star-forming regions in the Solar neighborhood shows a universal scaling of turbulence when traced by young stars. We also found evidence of local supernova energy injection in Orion and Ophiuchus, which is supported by observational studies.
This work is a follow-up to our 2021 paper, which investigates young stars in the Orion Complex specifically.
Read our press release here. Watch our interview for the American Astronomical Society's Journal Author Series here.
Advisor: Yuan Li (UNT)

WLQs are a peculiar subset of luminous active galactic nuclei whose emission spectra show extremely weak emission line profiles of Lyα+N Ⅴ λ1240 and/or C Ⅳ λ1549. Much effort has been invested to explain the weakness in emission profiles of these quasars over the last two decades. My current project proposes a new way of looking at WLQs not as a disjoint subset of quasars but rather as quasars that lie preferentially towards the extreme end of the C Ⅳ || Distance parameter space. This work has been accepted for publication in ApJ and is currently in the final proof stage.
Advisor: Ohad Shemmer (UNT)

As a graduate student advisor, I helped M.S. students in the A.I. summer program at UNT apply the pre-trained VGG-19 deep neural network architecture to the Milky Way map from the Wisconsin H-Alpha Mapper survey. Using the Orion Molecular Cloud Complex as a reference image, neural style transfer was able to identify various star-forming regions in the Milky Way. This result shows neural style transfer's promise as a quick targets identification pipeline for future sky surveys.
Read our project summary poster here.
Advisors: Mark Albert and Yuan Li (UNT)

I worked as an undergraduate research student at the Rochester Institute of Technology's Center for Computational Relativity and Gravitation from 2018 to 2020. There, I modified and documented the LORENE initial data code to generate physically motivated binary neutron stars pre-merger. Furthermore, I performed several GRMHD simulations of binary neutron stars merger using the Einstein Toolkit, and reported our findings at the 2019 Midwest Relativity Meeting in Grand Rapids, Michigan.
Read our conference presentation here.
Watch one of my simulations here.
Advisors: Joshua Faber (RIT) and Eric Blackman (UofR)