Professor Puckett's Research Homepage

Prof. Andrew Puckett, Department of Physics, University of Connecticut

Precision studies of proton and neutron structure via medium-energy electron scattering

Electron scattering has been one of the most important tools for precisely probing the femtoscopic structure of strongly interacting matter ever since Hofstadter’s pioneering measurements of electron-proton scattering and electron-nucleus scattering at Stanford in the 1950s revealed the non-point-like nature of the proton and provided a first direct measurement of the proton’s size, leading to the Nobel Prize in Physics in 1961. The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) in Newport News, Virginia, is the world’s leading facility for the precision three-dimensional imaging of the nucleon’s quark-gluon structure in both coordinate and momentum space. CEBAF uses superconducting radio-frequency acceleration technology to deliver electron beams of unparalleled quality in terms of energy, intensity, duty-cycle, and polarization. Experimentalists use these high-quality electron beams together with state-of-the-art target and detector technologies and high-performance data acquisition and computing capabilities to map the internal structure of strongly interacting matter with unprecedented precision and kinematic reach. In this talk, I will give a brief overview of the physics of electron scattering and its utility as a precision probe of nuclear structure, followed by a detailed overview of UConn’s role and Ph.D. research opportunities at JLab with the Puckett group.

Madisyn Brooks, UConn

Title and abstract TBA

Adam Riess- Bloomberg Distinguished Professor and 2011 co-winner of the Nobel Prize in Physics, Johns Hopkins University

In 1929 Edwin Hubble discovered that our Universe is expanding. Eighty years later, the Space Telescope that bears his name is being used to study an even more surprising phenomenon: that the expansion is speeding up. The origin of this effect is not known, but is broadly attributed to a type of “dark energy” first posited to exist by Albert Einstein and now dominating the mass-energy budget of the Universe. Professor Riess will describe how his team discovered the acceleration of the Universe and why understanding the nature of dark energy presents one of the greatest remaining challenges in astrophysics and cosmology. He will also discuss recent evidence that the Universe continues to defy our best efforts to predict its behavior.

Adam Riess is a Bloomberg Distinguished Professor, the Thomas J. Barber Professor in Space Studies at the Krieger School of Arts and Sciences, a distinguished astronomer at the Space Telescope Science Institute and a member of the National Academy of Sciences.

He received his bachelor’s degree in physics from the Massachusetts Institute of Technology in 1992 and his PhD from Harvard University in 1996. His research involves measurements of the cosmological framework with supernovae (exploding stars) and Cepheids (pulsating stars). Currently, he leads the SHOES Team in efforts to improve the measurement of the Hubble Constant and the Higher-z Team to find and measure the most distant type Ia supernovae known to probe the origin of cosmic acceleration.

In 2011, he was named a co-winner of the Nobel Prize in Physics and was awarded the Albert Einstein Medal for his leadership in the High-z Supernova Search Team’s discovery that the expansion rate of the universe is accelerating, a phenomenon widely attributed to a mysterious, unexplained “dark energy” filling the universe. The discovery was named by Science magazine in 1998 as “the Breakthrough Discovery of the Year.”

His accomplishments have been recognized with a number of other awards, including a MacArthur Fellowship in 2008, the Gruber Foundation Cosmology Prize in 2007 (shared), and the Shaw Prize in Astronomy in 2006.

Reception at 3:00pm in the Gant Science Light Court

Prof. Lina Necib, Department of Physics, MIT

Mapping out the Dark Matter in the Milky Way

In this talk, I will explore the interfacing of simulations, observations, and machine learning techniques to construct a detailed map of Dark Matter in the Milky Way, focusing on the Galactic Center/Halo and dwarf galaxies. For the Galactic Halo, I will present a recent work that reveals a decline in the stellar circular velocity, inducing tensions with established estimates of the Milky Way’s mass and Dark Matter content. I will discuss how the underestimated systematic errors in such a common methodology necessitates a revised approach that combines theory, observations, and machine learning. In dwarf galaxies, I will present a novel Graph Neural Network methodology that facilitates the accurate extraction of Dark Matter density profiles, validated against realistic simulations. I will conclude with a discussion on the future trajectory of astroparticle physics, emphasizing the need for the integration of astrophysical probes with experimental Dark Matter research, potentially leading to a better understanding of the nature of Dark Matter.