Yongheng Xu

Postdoctoral Fellow

Short bio

I am a particle physicist with a broad interest in new physics at the intersection of theory and experiment, focusing on the phenomenology of beyond-standard-model physics in detectors. I was trained as a theorist during my master's studies beginning from 2016, and defended my thesis on the phenomenology of future electron colliders in 2019. I then transitioned to experimental work, joining the T2K neutrino experiment as a PhD student at Lancaster University in the UK, supervised by Dr. Laura Kormos. I defended my PhD thesis on statistical inference and data analysis of the T2K experiment in May 2023. I then joined UCLA as a postdoctoral researcher, working with Prof. Alvine Kamaha on the data analysis of the LUX-ZEPLIN experiment and the setup of a local lab. Currently, I am a postdoctoral research fellow in the theoretical physics section, working on my DSTrain project, developing AI-based simulation tools for particle detectors.

Research interests and hobbies

Besides my DSTrain project, I am also a member of the GAMBIT community, working on the global analysis of neutrino experiments. I also plan to continue my participation in the data analysis and simulation work of the LUX-ZEPLIN experiment. When I don��t do particle physics, I spend my time travelling , cycling or doing nothing at all.

DSTrain project

Beyond my work on the DSTrain project, I am an active member of the GAMBIT collaboration, where I contribute to the global statistical analysis of neutrino experiments, integrating experimental results with theoretical models to explore new physics scenarios. My involvement includes refining global fits and developing efficient computational techniques to assess constraints on neutrino oscillation parameters.

Additionally, I plan to continue my participation in the data analysis and simulation efforts of the LUX-ZEPLIN (LZ) dark matter direct detection experiment, which utilizes a dual phase liquid xenon TPC. My work in LZ focuses on improving the sensitivity to new physics in low-energy electron recoil signals, which are crucial for dark matter searches and rare event detection. This involves optimizing signal extraction techniques, refining background modeling, and developing robust statistical inference frameworks. By leveraging my expertise in both phenomenology and experimental techniques, I aim to bridge the gap between theoretical predictions and observational data in dark matter direct detection experiments.