Maxim Mai
personal homepage
Without experimentalists, theorists tend to drift, without theorists experimentalists tend to falter.
The overarching focus of my research lies in properties of strongly interacting matter. Specifically, I am interested in properties of excited states of matter, short-lived excitations of hadrons such as protons, pions etc.. My main research goal lies in building a "tridge" (three-sided bridge) between (1) theoretical methods based on symmetries of the strong interaction interaction, (2) phenomenology and application to experimental observations and (3) results of numerical ab-initio calculations of Lattice QCD.
If you want to know more see the sources below. They are ordered with respect to the desired level of details:
One of the currently best ways to access QCD at low energies while taking into account its non-perturbative nature is the lattice gauge theory. In the nutshell it is based on the discretization of the 4D-spacetime by an Euclidean 4D discrete lattice in a finite volume. Quarks are allowed to live only on the intersections of the lattice sites while gluons are associated with the links between the intersections. Through a series of well-defined steps (see fig. to the right for an excerpt) physical information can be accessed in a numerical calculation performed in supercomputing facilities. For an introductory lecture on the techniques as well as historical details see, e.g., [LectureNotes].
I am interested in relating the results of lattice QCD to the phenomenological ones based on experimental observations. A central object of study hereby is the so-called quantization condition which relates real energy eigenvalues of a multi-particle system in a finite volume with complex-valued scattering amplitudes. Based on continuum Quantum Field Theory principles we have derived few years ago the Finite-Volume-Unitarity (FVU) approach which extends to up to three interacting particles. See figure to the left and [Review].
Data driven approaches run a danger of over-fitting or under-fitting the provided data. Another issue is the treatment of outliers and model misspecifications or other biases. Such problems are well known beyond physics and plenty of modern statistical or machine learning tools exists to mitigate them. One example of Neural Network based approach is shown in the right figure from a recent talk: "Denoising and sharpening the hadron spectrum through machine learning" at the recent dedicated workshop[link] in Thessaloniki.