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TRUNG HA
Astronomy PhD. Candidate, University of Massachusetts Amherst

ABOUT ME

I am a fifth-year astronomy PhD candidate at UMass Amherst working with Dr. Yuan Li. I recently transferred from University of North Texas, where I was working on a PhD. in Physics. My doctoral thesis focuses on computer simulations of the feeding and feedback cycle from supermassive black holes at the center of massive elliptical galaxies. I was also a Pre-Doctoral Fellow at the Flatiron Institute Center for Computational Astrophysics during Fall 2023, developing machine learning techniques to automatically segment current sheet structures in magnetized plasma turbulence simulations. I have since continued working on this project as a guest researcher. Further details about my past and current research can be found in the Research section.

Photo of Trung Ha

I grew up in Ho Chi Minh city, Vietnam, before moving to the U.S. as a cultural exchange student in 2014. After high school, I obtained an A.S. from Central Arizona College in 2017. I then graduated with a B.S. in Physics from University of Rochester in 2020, and an M.S. in Physics from University of North Texas in 2022.

Outside of research, I enjoy traveling, building computers, and solving Rubik's cubes. I also do landscape photography, and on the rare occasions, astrophotography. Consequently, images appear as backgrounds on this website were taken by me.

CURRICULUM VITAE
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RESEARCH

Meso-scale Simulations of Multiphase Accretion Flows onto Supermassive Black Holes (current)

(Preliminary result) Number density projection plot of our meso-scale simulation. The larger figure shows a region of 1.6 kpc x 0.9 kpc, which contains an inner and outer disks, rotating in opposite directions. The zoomed in figure shows a region of 10 pc x 10 pc, which shows the accretion flow onto the horizon-scale.

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)

My research projects in reverse chronological order.
Projects indicated by "(current)" are those I am actively working on.
Click on the images for more details.


Segmentation of Current Sheets in Magnetized Plasma Turbulence with Computer Vision (current)

Top: volume rendering of the projection of the current density onto the B field, j_parallel. Middle: xy-slice of j_parallel at z = 90 c_omp. Streamlines of the in-plane magnetic field b_perp are in the foreground. Bottom: SOM segmentation, color-coded by the cluster identified via aweSOM.

Magnetohydrodynamical (MHD) turbulence is a ubiquitous phenomenon in the universe. The intermittency in plasma turbulence 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, I developed a machine learning algorithm, aweSOM, to automatically segment and track current sheets in 3D plasma simulations. We find that in many test cases, the algorithm can effectively segment the current sheets from simulations.
We recently submitted the science paper for peer-review: arXiv:2410.01878 . We also submitted the software to the Journal of Open Source Software (JOSS).
Advisors: Joonas Nättilä, Jordy Davelaar, and Lorenzo Sironi (CCA / Columbia University)

Measuring Turbulence in the Interstellar Medium with Young Stars (current)

Figure 1 of our recent paper (arXiv:2205.00012), showing four of the nearest star-forming region to the Solar system: Orion, Ophiuchus, Perseus, and Taurus. In the foreground are young stars, in the background is Hα intensity (top) and Hα line-of-sight velocity (bottom).

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)

Weak Emission-Line Quasars (WLQs) in the Context of C Ⅳ Emission-line Properties


Preliminary result from our recently submitted paper. We found a strong correlation between the C Ⅳ rest-frame equivalent widths and the C Ⅳ || Distance parameter, which holds for <em>all</em> quasars in our sample.

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)

Similarity Mapping of the Milky Way Using Neural Style Transfer

Background: Cartesian projection of the Milky Way Hα map along the Galactic equator, with the Galactic center at the middle of the frame. Foreground: each white rectangle is a region identified by the VGG-19 algorithm as 'similar' to the Orion region. We observe that all highlighted frames are regions with high star formation rates, indicating that the neural network picked up this information from the flux and velocity input by color channels.

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)

Generating Initial Data for Binary Neutron Stars Merger Simulations


Isobaric contours plot of a binary neutron stars merger. The more compact star is 1.7 solar mass, while the puffier star is 1.14 solar mass. Each contour traces a curve of equal gas density, and brighter color indicates higher density.

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)

CONTACT

WHERE I WORK

I am currently working remotely, but I will soon be located at:
LGRT-B
710 North Pleasant St.
Amherst, MA 01003
Email:
tvha(at)umass.edu