Su, Yuanyuan

Clusters of galaxies are the most massive virialized structures in the Universe. They are the end result of the hierarchical structure formation and contain most of the bulk of all dark matter, hot baryons, and metallicity. The space between cluster member galaxies is filled with hot and diffuse gas, emitting strongly in X-ray through bremsstrahlung. I am leading a research group at the University of Kentucky in the pursuit of a deeper understanding of the evolution of galactic systems through the X-ray observations of galaxy clusters and early-type galaxies, complemented by observations in other wavelengths and numerical simulations.

Suzaku and Chandra observations of cluster outskirts

The outskirts of galaxy clusters are the boundary between the free-falling substructures and the virialized ICM. The study of cluster outskirts provides unique distinguishing power on models of the hierarchical assembly of dark matter halos. I have been awarded Chandra and Suzaku observations of MKW4 and Abell 586 from the cluster center to the virial radius. My graduate student Arnab Sarkar is leading the data reduction.

An machine learning approach of the microscopic physics of the intracluster medium

Driven by the deep gravitational potentials of galaxy clusters, the ICM is hot, diffuse, and fully ionized. Therefore, it can be treated as a plasma with particle-magnetic field interactions. The effective transport coefficients, particularly the viscosity, of the ICM can affect dynamical processes over an enormous range: from the feedback of active galactic nuclei to shock acceleration in merging clusters, impacting the distribution of the active energy in the Universe. We employ a convolutional neural net- work to build a connection between the X-ray images and the properties of the magnetic fields which are inaccessible to traditional approaches.

Personnel:

Yuanyuan Su (Department of Physics and Astronomy)

Nathan Jacobs (Department of Computer Science)

Graduate students:

Gongbo Liang (Department of Computer Science)

Yu Zhang (Department of Computer Science)

Arnab Sarkar (Physics and Astronomy)

Sheng Chieh Lin (Grad, Physics and Astronomy) 05/18/2020

Valeria Olivares (Postdoc, Physics and Astronomy) 11/16/2020

Ryan Martinez-Eskenasy, on LCC, Added 10/30/2021

Computational methods:

Data analysis and machine learning. These methods are available at UK.

Software:

Heasoft, ciao, soxs, pyXSIM, keras, tensorflow, astropy

UK and non-UK collaborators:

The scientific projects conducted in my group involve Arnab Sarkar (student), Nathan Jacobs (faculty) , Gongbo Liang (student), Yu Zhang (student), and myself (faculty)

Grants:

Publications:

Patents: