Facility for Rare Isotope Beams

A James Madison College event at FRIB.

TBD

Learning is one way how biological systems change. Evolution can also be thought of as learning but on longer time scales. This presentation will describe emerging evidence showing that biological systems organize them according to hyperbolic surfaces and that these surfaces expand according to similar principles in both learning and evolution. Across different scales of biological organization, biological networks often exhibit hierarchical tree-like organization. For networks with such structure, hyperbolic geometry provides a natural metric because of its exponentially expanding resolution. I will describe how the use of hyperbolic geometry can be helpful for visualizing and analyzing information acquisition and learning process from across biology, from viruses, to plants and animals, including the brain. We find that local noise causes data to exhibit Euclidean geometry on small scales, but that at broader scales hyperbolic geometry becomes visible and pronounced. The hyperbolic maps are typically larger for datasets of more diverse and differentiated cells, e.g. with a range of ages. We find that adding a constraint on large distances according to hyperbolic geometry improves the performance of t-SNE algorithm to a large degree causing it to outperform other leading methods, such as UMAP and standard t-SNE. For neural responses, I will describe data showing that neural responses in the hippocampus have a low-dimensional hyperbolic geometry and that their hyperbolic size is optimized for the number of available neurons. It was also possible to analyze how neural representations change with experience. In particular, neural representations continued to be described by a low-dimensional hyperbolic geometry but the radius increased logarithmically with time. This time dependence matches the maximal rate of information acquisition by a maximum entropy discrete Poisson process, further implying that neural representations continue to perform optimally as they change with experience. Tatyana Sharpee received her PhD in condensed matter physics from Michigan State University studying under the supervision of Mark Dykman. After her PhD, she started to work in computational neuroscience at UCSF where she developed statistical methods for analyzing neural responses to natural stimuli, which exhibit strong correlations and non-Gaussian effects. These methods made it possible to reveal new adaptation processes in the brain by comparing neural responses to white noise and natural stimuli. Her independent research program has started at the Salk Institute for Biological Studies where she is currently a Professor in the Computational Neurobiology and Integrative Biology Laboratories. Dr. Sharpee is a fellow of the American Physical Society.

What do atomic nuclei look like? There are several known images of nuclei: liquid droplets, independent nucleons "moving" in a mean potential, and clusters of nucleons floating in the nucleus. One of the methods to capture the different images of nuclei is the knockout reaction. In this reaction, the components of the nucleus are knocked out by incident particles at intermediate energies. If the rest of the nucleus is not disturbed except for the knocked-out particle, it can be determined to what extent the knocked-out particle was present in the nucleus and how it moved. By knocking out nucleons or clusters, the image of the nucleus captured by the independent particle or cluster image emerges. In this talk I will give an overview of our recent activities on nucleon and cluster knockout reactions from stable and unstable nuclei. From a theoretical point of view, the focus will be on the development of a new reaction model, CDCCIA, for the knockout of fragile particles.

TBD

Gravitational waves are tiny ripples in the fabric of spacetime that travel to us from some of the most extreme events in our Universe, distant mergers of black holes and neutron stars. Observations of these events chart the history of stars through the collapsed remnants that are left behind at the end of their lives. Interpreting the patterns of their waves tells us about how these compact remnants orbit and spin, and can tell us how matter behaves at densities beyond that of an atomic nucleus. Mergers involving neutron stars are engines of transient astronomy, launching gamma-ray bursts and spreading newly created heavy elements into the universe. In this talk, I will tell some of the story of this new field of gravitational wave astronomy and show how our first detections are laying the groundwork for future observatories that can see across our entire Universe. Jocelyn Read is a Professor of Physics at California State University Fullerton in the Nicholas and Lee Begovich Center for Gravitational Wave Physics and Astronomy, and currently a Visiting Fellow at the Perimeter Institute. Her research connects the nuclear astrophysics of neutron stars with gravitational wave observations. She earned her Ph.D. in 2008 from the University of Wisconsin Milwaukee, where she developed a widely used model for dense matter inside neutron stars and produced first estimates of how gravitational waves from neutron star mergers would inform these properties. Her work has included proposed mechanisms for precursor flares in gamma-ray bursts, new methods for gravitational-wave cosmology, uncertainty quantification for neutron-star merger source modeling, and measurements of dense matter properties with LIGO and Virgo gravitational-wave observations. She is actively contributing to the development of the next-generation gravitational-wave observatory Cosmic Explorer. Read co-chaired the LIGO/Virgo Binary Neutron Star Sources Working Group from 2014 to 2016 and was part of the team awarded the 2016 Special Breakthrough Prize in Fundamental Physics for the discovery of gravitational waves. She co-led the Extreme Matter team of the LIGO-Virgo-Kagra Collaboration from 2016 to 2022, through the first discovery and analysis of gravitational waves from a neutron-star merger. She has held visiting positions at the California Institute of Technology and the Carnegie Observatories in Pasadena. Read chairs the Advisory Board for the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) and served on the Scientific Advisory Committee for the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav). She was elected a Fellow of the American Physical Society (APS) in 2019.

Nuclear reactions among charged particles in stars take place at energies generally well below the Coulomb barrier, so the Coulomb barrier penetration factor exponentially suppresses the cross sections down to values as small as few nanobarns or picobarns. Therefore, approaching astrophysical energies opens new challenges and calls for new approaches. I will introduce the mission of nuclear astrophysics and discuss how experiments are usually conducted. Then, I will focus on the use of indirect methods as complementary approaches to direct measurements, discussing in detail the asymptotic normalization coefficient (ANC) and the Trojan Horse Method (THM). These methods are used to deduce the astrophysical factors of reactions with photons and charged particles in the exit channel, respectively, with no need of extrapolation. I will present recent results of the application of the two methods as examples. First, I will discuss the 6Li(3He,d)7Be measurement used to deduced the ANC’s of the 3He+4He->7Be and p+6Li->7Be channels and the corresponding radiative capture astrophysical factors. Then, I will illustrate the THM measurement of the 27Al(p,a)24Mg astrophysical factor through the 2H(27Al,a24Mg)n reaction. The reaction rate of the 27Al(p,g)28Si reaction was also deduced thanks to the determination of the proton partial widths. Both the ANC and the THM applications made it possible to assess the occurrence or exclude the presence of resonances that could be responsible significant changes of the reaction rates at temperatures of astrophysical interest.

The Nuclear Science Summer School (NS3) is a summer school that introduces undergraduate student participants to the fields of nuclear science and nuclear astrophysics. NS3 is hosted by FRIB on the campus of Michigan State University (MSU). The school will offer lectures and activities covering selected nuclear science and astrophysics topics.

PAN introduces participants to the fundamentals of the extremely small domain of atomic nuclei and its connection to the extremely large domain of astrophysics and cosmology.

The PAN @ Michigan State Experience

• Learn about research in one of the top rare-isotope laboratories in the world.
• Get introduced to the fascinating fields of astrophysics, precision measurement, and nuclear science.
• Perform your own nuclear physics experiments.
• Meet researchers who are exploring a wide array of questions.
• Discover the surprising array of career opportunities in science.
• Experience the atmosphere of college life.
• Participants in the 2024 program get free room and board on campus (if required).