Physics Colloquium - Current

Speaker: L.Y. Aron Yung, Space Telescope Science Institute

Abstract: The superb capabilities of the James Webb Space Telescope (JWST) have extended our view to the ultra-high-redshift universe (z > 12). Among numerous scientific discoveries enabled by JWST, some early deep extragalactic observations have unexpectedly revealed an abundance of massive galaxies, presenting significant challenges to conventional galaxy formation models. To address this cosmic puzzle, we utilize some well-established galaxy formation models in conjunction with state-of-the-art cosmological simulations to seek understanding of physical mechanisms that enabled extremely rapid star formation activities in the early universe.  We investigated and quantified the impact of various sources of uncertainty, including a potentially evolving mass-to-light ratio driven by changes in the IMF, underestimated field-to-field variance, and significant uncertainties in photometric redshifts, among others. Our study also examines the number density of halo populations during this epoch, alongside the gas cooling rates and star formation efficiencies of galaxies. We find that the stellar feedback models currently implemented in our framework—and widely adopted in many hydrodynamic simulations—eject gas at rates comparable to halo mass accretion rates. This process deprives galaxies of the necessary fuel for star formation. I will present new simulated results for various alternative star formation and stellar feedback models, discussing the essential conditions required to reproduce the observed ultra-high-redshift galaxy populations.

Speaker: Bikram Bhatia, University of Louisville

Abstract:  Our energy infrastructure predominantly relies on fluidic systems for energy generation, conversion, and storage, which are often complex, bulky, and primarily driven by fossil fuels. In this talk, I will highlight our work reinventing heat engines, heat pumps, and heat exchangers by substituting fluids with solids. This approach can significantly improve performance and open up new application possibilities.

The first part of the talk highlights novel radiative heat engines that combine the simplicity, compactness, reliability, and high power density of solid-state devices with the superior efficiency of conventional heat engines. We demonstrate thermodynamic cycles in pyroelectric materials and show how active thermal emission control, achieved through optical filters and reflectors, enhances their performance. Using an experimentally validated model that integrates thermodynamics and heat transfer, we simulate scaled-up performance and highlight the potential of radiative thermal switching in advancing solid-state heat engine technology.

The second part of the talk will center on heat pumps based on barocaloric cooling – the caloric response of solid materials subjected to applied hydrostatic pressure. These heat pumps are highly efficient, environmentally friendly, reliable, and present a promising alternative to existing vapor compression refrigeration systems. We specifically investigate the mechano-thermal response of low-cost polymers that combine a large barocaloric response with high compressibility. Additionally, as a step towards translating this promising material response into practical devices, we also quantify their performance at the system level.

Reimagining our energy systems using solid media could enhance energy efficiency, enable high-temperature and zero-gravity operations, and improve reliability. These innovative thermal systems offer compelling solutions for waste heat harvesting, air conditioning and refrigeration, solar energy conversion, and energy storage – paving the way for a sustainable energy future.

Speaker: Stephen Kane, University of California, Riverside

Abstract: Underpinning planetary science is a deep history of observation and, more recently, robotic exploration within the Solar System, from which models of planetary processes have been constructed. Concurrently, thousands of planets have been discovered outside our Solar System that exhibit enormous diversity, and their large numbers provide a statistical opportunity to place our Solar System within the broader context of planetary structure, atmospheres, architectures, formation, and evolution. Indeed, the field of exoplanetary science has rapidly forging onward toward a goal of atmospheric characterization, inferring surface conditions and interiors, and assessing the potential for habitability. However, the interpretation of exoplanet data requires the development and validation of exoplanet models that depend on in situ data that, in the foreseeable future, are only obtainable from our Solar System. Thus, planetary and exoplanetary science greatly benefit from a symbiotic relationship with a two way flow of information. In this talk I will briefly describe the critical lessons and outstanding questions from planetary science, the study of which are essential for addressing fundamental aspects for a variety of exoplanetary topics. I will particularly focus on the gaps in our knowledge regarding Venus within the context of modeling planetary habitability. I will outline exoplanet target selection for testing the conditions of runaway greenhouse, presenting examples of potential Venus analogs, and describe the benefits that will arise from coming NASA and ESA Venus missions. Finally, I will discuss the timeline of planetary science and exoplanet missions, and the potential for collaborations between these two communities.

Speaker:  Douglas Tucker, Fermi National Laboratory

Speaker:John Wu from Johns Hopkins University / Space Telescope Science Institute (STScI)

Abstract: Machine learning (ML) techniques and artificial intelligence (AI) are revolutionizing our ability to study galaxy evolution and large-scale structure. Convolutional neural networks (CNNs) can now reliably predict galaxies' physical properties, including cold gas content and metallicity, directly from three-color optical images. These models can even reconstruct entire optical spectra from imaging alone. Highly optimized CNNs can also robustly identify nearby dwarf galaxies from wide-area surveys, significantly expanding the sample of known satellite systems at low redshifts. Meanwhile, graph neural networks (GNNs) can learn how large-scale environment impacts the galaxy-halo connection from cosmological simulations. These applications demonstrate how ML models with strong inductive biases can enable new scientific insights in galaxy evolution and cosmology. With upcoming wide-area surveys from the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope, advanced ML and interpretable AI methods will play an increasingly vital role in extracting physical understanding from vast cosmic datasets.