Two Graduate Students Awarded Prestigious Department of Energy Fellowships

Thomas Blommel was awarded the Computational Science Graduate Fellowship (CSGF). This highly competitive fellowship is awarded to undergraduate seniors or first-year graduate students in science or engineering who have a strong academic background and interest in computational research. The CSGF provides up to four years of support via a tuition waiver and stipend, and recipients spend 12 weeks at a national lab to further their research.

Thomas is a second-year graduate student working with Professor Emanuel Gull on simulations of condensed matter systems. Condensed matter systems such as solids are challenging to simulate because they contain billions of atoms. Most physics principles and calculations were developed to understand a single atom or a small group of atoms, and it is impossible to perform a calculation that considers all the atoms individually. Additionally, in solids, the ordered pattern of atoms and the interactions between them can allow new and unexpected properties to emerge for the solid as a whole - properties that the individual atoms do not display. To deal with this, researchers use quantum Monte Carlo methods, which are a family of computational tools that allow for collective calculations on large numbers of atoms. These methods can be applied with high accuracy to diverse types of condensed matter systems. 

For his CSGF project, Thomas is interested in applying quantum Monte Carlo methods to strongly correlated systems. Strongly correlated systems are materials in which new material properties arise from strong interactions between the electrons. A familiar example is in magnets, where the electrons line up in a specific pattern to produce the magnetic effect. Another type of strongly correlated system is a superconductor, where the electrons pair up, allowing current to flow in the material without resistance. Through his research, Thomas hopes to understand more about the physics behind superconductivity, allowing researchers to discover new materials that display this property. In the future, Thomas plans to spend his national lab time studying non-equilibrium systems, or how these systems evolve over time after a perturbation.

Fifth-year graduate student Melissa Hutcheson was awarded an Office of Science Graduate Student Research Fellowship to work at the Fermi National Accelerator Laboratory (Fermilab). This prestigious award allows graduate students in science or engineering to conduct dissertation research at a national laboratory for three to 12 months. 

Melissa works in Professor Myron Campbell’s research group and on the K-0 at Tokai (KOTO) experiment in Tokai, Japan. KOTO studies interactions within the Standard Model, our current description of fundamental particles and forces. Specifically, KOTO aims to measure the decay of a kaon into a pion and two neutrinos. Kaons and pions are particles that are each made of two quarks; quarks and neutrinos are both fundamental particles in the Standard Model. The particular decay studied by KOTO is extremely rare - one in 30 billion kaons undergoes this decay - but by studying it researchers hope to probe the Standard Model. Results could place new constraints on the types of interactions allowed between particles or lead to the discovery of violation of fundamental symmetries of the Standard Model. At the moment, the Standard Model predictions for the kaon decay studied at KOTO have a higher accuracy than the experimental results, so a major goal is to find techniques that improve the experimental accuracy. 

One way to do this is to remove the “background,” or unwanted data collected from processes other than the kaon decay being studied. The origins of the background must be understood so that this unwanted data can be removed, isolating the data from the kaon decay for further analysis. Especially for such a rare process, it is essential to understand and correctly remove the background. Melissa’s project uses techniques from machine learning to understand the origin of background data to improve how it is removed during analysis. This will allow for an increase in accuracy of the experimental results from KOTO, bringing the team closer to finding out whether there might be new physics beyond the Standard Model. At Fermilab, Melissa worked with Dr. Nhan Tran, who performs machine learning analyses for experiments at the Large Hadron Collider.