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Seminars/Colloquia, Fall 2022

Unless noted otherwise, Tuesday Colloquia are at 4:00 PM, refreshments will be served 15 minutes before each colloquium.
Scheduling for additional seminars will vary.

Date/Place	Speaker	Title (and link to abstract)
Thurs, Aug 18 Lewis 101	Hartmut Grote Gravity Exploration Institute Cardiff University	Quantum-Enhanced Interferometry for Dark Matter and Quantum Gravity Searches
Tue, Aug 23 Lewis 101	Department Faculty Department of Physics and Astronomy University of Mississippi	Ice Cream Social
Tue, Aug 30 Lewis 101	Joe Rivest, Madusanka Abeykoon, Devesh Bhattarai Department of Physics and Astronomy University of Mississippi	Student Research Presentations
Tue, Sep 6 Lewis 101	Sina Rostami, Santosh Bhandari, Quinn Campagna Department of Physics and Astronomy University of Mississippi	Student Research Presentations
Tue, Sep 13 Lewis 101	Cecille Labuda Department of Physics and Astronomy University of Mississippi	Spatial Variation of the Ultrasonic Properties of Brain
Tue, Sep 20 Lewis 101	Jeffrey Kleykamp and Luiz Prais Department of Physics and Astronomy University of Mississippi	Search for Non-Standard Interactions with Neutrino Oscillations at the NOvA Experiment
Tue, Sep 27 Lewis 101	Sadia Kaliil Senior Data Scientist Caterpillar Inc.	From Physicist to a Data Scientist: It's Never Too Late!
Tue, Oct 4 Lewis 101	Michael Schatz School of Physics and Center for Nonlinear Science Georgia Institute of Technology	Forecasting Turbulence
Tue, Oct 11 Lewis 101	Alumni panel Department of Physics and Astronomy graduates	Alumni panel
Tue, Oct 18 Lewis 101	Jan Strube Physical and Computational Sciences Directorate Pacific Northwest National Laboratory	Machine Learning in the Physical Sciences
Tue, Oct 25 Lewis 101	Scott Hertel Department of Physics University of Massachusetts Amherst	Recent Progress Towards the Detection of Dark Matter
Tue, Nov 1 Lewis 101	John Beggs Department of Physics Indiana University Bloomington	The Cortex and the Critical Point
Tue, Nov 8 Lewis 101	Joon Sue Lee Department of Physics and Astronomy University of Tennesse Knoxville	Superconductor-Semiconductor Hybrid Systems for Quantum Devices
Tue, Nov 15 Lewis 101	John Wise Center for Relativistic Astrophysics, School of Physics Georgia Institute of Technology	The First Stars, Black Holes, and Galaxies in the Universe
Tue, Nov 22 Lewis 101	Thanksgiving Break
Tue, Nov 29 Lewis 101	Fernanda Psihas Neutrino Division Fermi National Accelerator Laboratory	Neutrino Physics with Deep Learning: Applications, Successes, and Lessons
Tue, Dec 6 Lewis 101	Final Exam Week
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Abstracts of Talks

Hartmut Grote Gravity Exploration Institute Cardiff University Quantum-Enhanced Interferometry for Dark Matter and Quantum Gravity Searches Laser interferometry has revolutionized astronomy by introducing a new sense in the observation of the universe. We can now hear the ripples of space-time: gravitational waves. Moving beyond this 'application' of laser interferometry, in this talk I will give an overview of how ultra-precise laser interferometers can also be used to try to shed light on other mysteries of the universe. Namely the search for dark matter and the question of whether space-time is quantized at the smallest level.

Ice Cream Social Join us for an opportunity to meet the faculty and students of the Department of Physics and Astronomy, hear about ongoing research, social and outreach activities, and enjoy some ice cream!

Student Research Presentations Graduate students in the Department of Physics and Astronomy will present brief reports on their ongoing research.

Cecille Labuda Department of Physics and Astronomy University of Mississippi Spatial Variation of the Ultrasonic Properties of Brain Brain is inhomogeneous due to its composition of different tissue types (gray and white matter), anatomical structures (e.g. thalamus and cerebellum), and cavities in the brain (ventricles). These inhomogeneities lead to spatial variations in the ultrasonic properties of the organ. However, reporting on the spatial variation of the ultrasonic properties is limited in the literature. The spatial variation of the speed of sound, frequency slope of attenuation, attenuation and backscatter in brain tissue are presented here as two-dimensional maps. Tissue specimens were 1-cm thick slices of fixed sheep brain prepared from the coronal, sagittal and transverse anatomic planes. Ultrasonic measurements were performed using broadband transducers with center frequencies of 3.5, 5.0, 7.5, and 10 MHz. The spatial variation of these properties are clearly visualized and structures visible in the maps are consistent with the known morphologic features of the brain. White and gray matter appeared to be distinguishable in the images. The average values of the ultrasonic properties are consistent with published values.

Jeffrey Kleykamp and Luiz Prais Department of Physics and Astronomy University of Mississippi Search for Non-Standard Interactions with Neutrino Oscillations at the NOvA Experiment The phenomenon of neutrino oscillations provided the first evidence for the so-called Physics Beyond the Standard Model, and opened a window for several and interesting new investigations in the field of neutrino physics. Among the possibilities, Non-standard interactions (NSI) are an extension of the neutrino matter effect leading to a rich phenomenology, and are expected to modify the propagation of neutrinos through matter. The current open questions in the neutrino oscillation model rely heavily on how neutrinos interact with matter, to an extent that NSI could induce possible effects. The NOvA Experiment presents its first preliminary search for flavor-changing NSI in neutrino oscillations in the 810 km baseline as neutrinos cross the Earth's crust between the Near and Far Detectors.

Sadia Khalil Senior Data Scientist Caterpiller, Inc From Physicist to a Data Scientist: It's Never Too Late! I am a senior data scientist at Caterpillar Inc, and I like to share my story of career transformation in the industry after more than a decade of research in the LHC experiments at the CERN. I like to tell you why a data scientist career is a highly desired profession for people with a STEM background, especially in Physics. I like to give some tips on how to build a professional network and a well-composed resume, coding techniques, soft skills, and a strong knowledge of the fundamentals of statistics.

Michael Schatz School of Physics and Center for Nonlinear Science Georgia Institute of Technology Forecasting Turbulence Fluid turbulence is one of the greatest unsolved problems of classical physics (and the subject of a million dollar mathematical (Millenium) challenge). Centuries of research--including Leonardo da Vinci's observations of "la turbolenza" and the best efforts of numerous scientists (Heisenberg, Kelvin, Rayleigh, Sommerfeld, ...)--have failed to yield a tractable predictive theory. However, recent theoretical and computational advances have successfully linked recurring transient patterns (coherent structures) within turbulence to unstable solutions of the equations governing fluid flow (the Navier-Stokes equations). The solutions describing coherent structures provide a geometrical structure that guides the evolution of turbulence. We describe laboratory experiments where the geometry of key coherent structures is identified, thereby provided building blocks for describing the behavior of weakly turbulent flows.

Alumni panel Department of Physics and Astronomy graduates Alumni panel Graduates of the University of Mississippi Department of Physics and Astronomy will discuss their career path following graduation, discuss some of the benefits that their degree brought them, and answer questions posed by students in attendance.

Jan Strube Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Machine Learning in the Physical Sciences Machine learning and artificial intelligence (AI) algorithms have become a part of everyday life, from recommendation systems to autonomous driving. In their basic form, machine learning algorithms have been used in high energy physics for well over two decades. Interpretability and uncertainty quantification are essential characteristics of scientific algorithms, unlike many use cases in the industry. The seminar will give a brief introduction to machine learning, deep learning, and artificial intelligence, review the state of the art of machine learning in physics, with a focus on high energy physics, and point out areas of opportunities for further development.

Scott Hertel Department of Physics University of Massachusetts Amherst Recent Progress Towards the Detection of Dark Matter As you read this, you are immersed in a bath of particles beyond the Standard Model, so-called `dark matter' particles which make their presence felt (so far) only through gravitational effects at astrophysical scales. Discovering the properties of these particles (their mass, interactions with other particles, etc.) is one of the great challenges of 21st century physics. I will describe three complementary efforts which look for dark matter particles scattering off atoms in a laboratory setting: LZ, HeRALD, and SPICE. Each effort uses novel technologies to progress towards ever greater sensitivity to new physics and potentially unraveling this great mystery.

John Beggs Department of Physics Indiana University Bloomington The Cortex and the Critical Point Condensed matter physics provides a framework for understanding experiments on ensembles of neurons. Within this framework, cascades of activity among cortical neurons follow the same equations that govern avalanches in granular materials, complete with power laws, an exponent relation and a universal scaling function. These "neuronal avalanches" also show that the cerebral cortex operates near a critical point where many of its information processing functions are optimized, analogous to peaks in susceptibility and correlation length seen at a continuous phase transition. I will review progress in this field over the past 20 years and point to the new frontiers it has opened in human health and computing. You can find Dr. Begg's new book The Cortex and the Critical Point at MIT press and on Amazon

Joon Sue Lee Department of Physics and Astronomy University of Tennessee Knoxville Superconductor-Semiconductor Hybrid Systems for Quantum Devices In superconductor-semiconductor hybrid systems, interfaces and junctions with minimal disorder are crucial for realizing quantum phenomena associated with induced superconductivity. Advances in developing transparent interfaces by molecular beam epitaxy and clean junctions by in-situ shadowing have resulted in enhanced features of superconducting proximity effect. These schemes of in-situ deposition and shadowing of superconductors can be applied to quantum devices based on 1D nanowires, selectively grown in-plane 1D wires, and 2D electron gases. In this talk, materials and devices prepared by in-situ deposition and shadowing will be demonstrated, and transport studies revealing hard superconducting gap, two-electron charging effect, and zero-bias conductance peaks will be discussed.

John Wise Center for Relativistic Astrophysics, School of Physics Georgia Institute of Technology The First Stars, Black Holes, and Galaxies in the Universe Cosmic structure forms hierarchically through smooth accretion and dark matter halo mergers. As a consequence, all galaxies are the product of the dozens of mergers over billions of years. However, one can ask, ``What were the first stars and galaxies in the universe?'' I will review the current state-of-the-art simulations of early galaxy formation, starting with the formation of the first stars, which are initially devoid of metals and are suggested to have a characteristic mass of tens of solar masses. I will present results from a suite of cosmological radiation hydrodynamics simulations that focus on the transition from the first stars to the first galaxies. Each simulation captures the radiative and chemical feedback from 10,000 first stars, leading to the formation of a 107 solar mass galaxy only 500 million years after the Big Bang, that can now be tested against the latest observations from JWST. Last I will highlight how some of the earliest massive black holes form during these early epochs that could be the seeds of supermassive black holes that exist at the centers of all massive galaxies today.

Fernanda Psihas Neutrino Division Fermi National Accelerator Laboratory Neutrino Physics with Deep Learning: Applications, Successes, and Lessons Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. Dr. Psihas will showcase applications to detector data analysis developed in the past few years and present the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields.