SCATTERING THEORY AND APPLICATIONS

A graduate level series of lectures on scattering theory for hadron spectroscopy

Class overview

The physics behind any dynamical mechanism that causes an unstable structure, such as resonance, is revealed by the singularities of reaction amplitudes when viewed as an analytical function of kinematic variables. Therefore, understanding the fundamental reaction mechanisms is essential for grasping the inherent, non-perturbative dynamics in any area of physics. For example, in nuclear and particle physics, which involve strongly interacting matter made of quarks and gluons, also known as hadrons, many exotic resonance candidates have been reported. However, their true identities will remain uncertain until the quantum amplitudes that describe the underlying scattering processes are identified. 

Why should you enroll in this course?

This course is intended for students in their second year or beyond of their PhD or MSc studies who are interested in the phenomenology of scattering processes. It is appropriate for those pursuing either experimental or theoretical approaches. We will examine topics from atomic, molecular, nuclear, and particle physics, emphasizing connecting observations (data) to fundamental physics principles and dynamics (theory).  

How does the course material differ from traditional QFT and subatomic physics courses?

We aim to provide a perspective that complements what is typically taught in courses on QFT and subatomic physics. We will emphasize the physical aspects of scattering, both in experiments and theory, to make it clearly relevant and useful for your future research projects.

What is the course format?

This is an 8-week, 2-credit course offered during the second half of the Spring 2026 semester. Classes will meet on Tuesdays and Thursdays from 1:45 pm to 3:35 pm from March 10 to May 7. Lectures will be given in person at Indiana Bloomington, in SW214, and streamed online on zoom. Some lectures will feature guest speakers discussing topics such as AI/ML applications, advanced statistical analysis, and key aspects of complex calculus and experimental data analysis. In addition to lectures, a list of reading materials will be provided, and students will have the opportunity to work on individual or group projects aligned with their interests under the instructor's supervision. Grading will be based on participation and delivering a 25-minute seminar on a project, as well as on reading material and research related to the course content.

How can I get more info?

Write to aszczepa@iu.edu