Yuqian Gui

Yuqian Gui

Yuqian Gui’s Website

Hi there! I am Yuqian Gui (桂雨谦), currently a 3rd-year PhD student in the Department of Astronomy at Tsinghua University. I am currently visiting Prof. Stijn Wuyts at the University of Bath. I received my B.S. in Physics from Zhili College, Tsinghua University, in June 2024.

My current research focuses on how the evolution of galaxies relates to their merger histories and large-scale environments.

My recent work focuses on the kinematics of the circumgalactic medium (CGM) around M31, under the supervision of Prof. Zhijie Qu. I use ultraviolet absorption spectra of background quasars observed with the Cosmic Origins Spectrograph (COS) aboard the Hubble Space Telescope (HST) to probe the motions of gas across M31’s halo. Through this work, I aim to understand how large-scale gas kinematics connect to the host galaxy’s dynamics and evolution.

During my time at the University of Bath, I will investigate the origin of the largest disk galaxies in the Euclid survey, trying to understand physical mechanisms responsible for the formation by linking their observed abundance and properties to theoretical models.

In a recently published paper, I studied episodic star formation in star-forming galaxies using the IllustrisTNG simulations, under the supervision of Prof. Dandan Xu. We explored how galaxies move through different stages of their star formation cycles and showed that temporal fluctuations within individual galaxies, together with differences between their historical mean star formation rates, can account for the scatter in the star formation main sequence.

Before turning to galaxy evolution, I worked on statistical analyses of planets detected through gravitational microlensing using KMTNet data, under the supervision of Prof. Shude Mao. As part of the “Mass-Production for KMTNet AnomalyFinder Planets” project, I analyzed half of the 2017 events in the AnomalyFinder project independently and published a paper; see my Publications page for details.

I am also interested in machine learning and AI for science. I believe these tools can help us better understand the universe and explore new physics. I am still learning about them and look forward to developing my knowledge and skills.

Hobbies and Personal Life

  • I enjoy photography and music, especially singing.
  • Running and swimming are two of my favorite ways to stay active and healthy.
  • I always enjoy meeting new people and chatting with those around me.

For more info

For more information about my background and experience, please see my CV.