#  Loeb Lectures in Physics: Phiala Shanahan (MIT), Nov 18-21, 2024 

 





October 22, 2024

 

 

     ![Prof. Phiala Shanahan](/sites/g/files/omnuum6476/files/styles/hwp_16_9__480x270/public/2025-02/shanahan.jpeg?itok=FbAyTlcK) 

 



 

## PHIALA SHANAHAN

Class of 1957 Career Development Associate Professor of Physics, MIT

*all lectures will be held in Jefferson Lab 250*   
*and streamed live through zoom (please see the link below)*

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**November 18 @4:30PM**

### Colloquium: "The building blocks of the Universe: proton and nuclear structure from the Standard Model"

Our understanding of the structure of matter, encapsulated in the Standard Model of particle physics, is that protons, neutrons, and nuclei emerge dynamically from the interactions of underlying quark and gluon degrees of freedom. In this colloquium, I will describe how first-principles theory calculations have given us new insights into this structure, including predictions of the contributions of gluons to the pressure and shear distributions in the proton, which have recently been constrained for the first time experimentally. I will also discuss studies of light nuclei which provide insights relevant to long-baseline neutrino experiments seeking to constrain the neutrino masses and mixing parameters, searches for evidence of the Majorana nature of neutrinos through neutrinoless double beta decay, and dark matter direct detection experiments. Finally, I will explain how provably-exact machine learning algorithms are providing new possibilities in this field.

**November 19 @4:30PM**

### Lecture I: "Aspects of the partonic structure of the proton from (lattice) QCD"

In the second lecture in this series I will discuss in further detail several new insights about the proton’s three-dimensional quark and gluon structure that have emerged over the last few years through a synergy of first-principles theory calculations and experimental constraints, and describe the interplay of these.

**November 21 @4:30PM**

### Lecture II: "Machine learning for sampling high-dimensional probability distributions in lattice field theory"

In the final lecture I will describe the evolution of a new generation of physics-informed machine learning algorithms designed to sample the extremely high-dimensional probability distributions that appear in the context of lattice field theory, and give context for these new developments.

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**Phiala Shanaha**n grew up in Adelaide, Australia, and obtained her PhD from the University of Adelaide in 2015. Before joining the MIT physics faculty in 2018, Prof. Shanahan was a Postdoctoral Associate at MIT from 2015-2017, and held a joint position as Assistant Professor at the College of William & Mary and Senior Staff Scientist at the Thomas Jefferson National Accelerator Facility from 2017-2018. She has been recognised by a number of awards including the Maria Goeppert Mayer Award from the American Physical Society, and the Bragg and Ruby Payne-Scott Medals from the Australian Institute of Physics.

*The lectures are sponsored by the Morris Loeb Lectureship Fund.*