Wine and Cheese Spring 2025: Difference between revisions
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'''Decoding the Complexity of Galaxy Formation with Physics-Informed, AI-Accelerated Dynamical Models'''<br> | '''Decoding the Complexity of Galaxy Formation with Physics-Informed, AI-Accelerated Dynamical Models'''<br> | ||
Galaxies are complex dynamical systems evolving against the backdrop of cosmology. One of the grand challenges of modern astrophysics is to build a fully predictive theory for galaxy formation so that we can reliably use them to understand the fundamental physics of dark matter, dark energy and inflation. However, the relevant physical processes are poorly understood and non-linearly coupled over an enormous range of spatiotemporal scales. This, combined with our inability to observe the time evolution of individual systems, necessitates phenomenological approaches. I will describe my interdisciplinary efforts to use population studies to decode the ordered complexity of galaxies. First, I will show that my JWST discovery of a significant excess of linear, elongated galaxies at high-redshift has deep implications for the origin of the Hubble Sequence. Galaxies may not start out as axisymmetric disks as commonly assumed, but rather as prolate (cigar-shaped) or triaxial (surfboard-shaped) ellipsoids. This preferential elongation is naturally expected from the tidal field of the filamentary cosmic web, and it motivates an exciting search for intrinsic alignments among this dominant population of "early blue ellipticals" with JWST, Roman and Euclid. Although this puzzle may seem niche, it bridges together many different subfields (including astrostatistics, stellar dynamics, Galactic archaeology and dark matter phenomenology) and unlocks fresh science cases for HWO and GMT. Second, I will introduce a novel automatically differentiable, GPU-capable framework to help definitively answer ~50-year-old questions about how galaxies self-regulate and why they follow remarkably tight scaling relations. In my new model, supernovae and black holes over-pressurize galactic atmospheres, which naturally explains why galaxies are so inefficient at forming stars. But robustly testing this paradigm requires physics-informed, data-driven, AI-accelerated techniques for parameter inference and causal discovery. I will highlight some promising early applications of gradient descent optimization, Hamiltonian Monte Carlo and population-level implicit likelihood inference. These reveal that observations of the circumgalactic medium may hold the key to breaking numerous modeling degeneracies. I will conclude by outlining how my future research will enable transformative cosmology-style precision astrophysics for galaxy formation, provide a much-needed interpretable emulator for expensive hydrodynamical simulations, and rigorously connect observations with theory to maximize the impact of multiple JHU-led missions (e.g., SDSS-V, Rubin, PFS and NASA's next Great Observatories). | Galaxies are complex dynamical systems evolving against the backdrop of cosmology. One of the grand challenges of modern astrophysics is to build a fully predictive theory for galaxy formation so that we can reliably use them to understand the fundamental physics of dark matter, dark energy and inflation. However, the relevant physical processes are poorly understood and non-linearly coupled over an enormous range of spatiotemporal scales. This, combined with our inability to observe the time evolution of individual systems, necessitates phenomenological approaches. I will describe my interdisciplinary efforts to use population studies to decode the ordered complexity of galaxies. First, I will show that my JWST discovery of a significant excess of linear, elongated galaxies at high-redshift has deep implications for the origin of the Hubble Sequence. Galaxies may not start out as axisymmetric disks as commonly assumed, but rather as prolate (cigar-shaped) or triaxial (surfboard-shaped) ellipsoids. This preferential elongation is naturally expected from the tidal field of the filamentary cosmic web, and it motivates an exciting search for intrinsic alignments among this dominant population of "early blue ellipticals" with JWST, Roman and Euclid. Although this puzzle may seem niche, it bridges together many different subfields (including astrostatistics, stellar dynamics, Galactic archaeology and dark matter phenomenology) and unlocks fresh science cases for HWO and GMT. Second, I will introduce a novel automatically differentiable, GPU-capable framework to help definitively answer ~50-year-old questions about how galaxies self-regulate and why they follow remarkably tight scaling relations. In my new model, supernovae and black holes over-pressurize galactic atmospheres, which naturally explains why galaxies are so inefficient at forming stars. But robustly testing this paradigm requires physics-informed, data-driven, AI-accelerated techniques for parameter inference and causal discovery. I will highlight some promising early applications of gradient descent optimization, Hamiltonian Monte Carlo and population-level implicit likelihood inference. These reveal that observations of the circumgalactic medium may hold the key to breaking numerous modeling degeneracies. I will conclude by outlining how my future research will enable transformative cosmology-style precision astrophysics for galaxy formation, provide a much-needed interpretable emulator for expensive hydrodynamical simulations, and rigorously connect observations with theory to maximize the impact of multiple JHU-led missions (e.g., SDSS-V, Rubin, PFS and NASA's next Great Observatories). | ||
=24 February= | |||
== Ting Li (Toronto) == | |||
'''From Stellar Streams to Near Field Cosmology: Insights from Large-Scale Spectroscopic Surveys''' | |||
Stellar streams serve as exceptional tracers in near-field cosmology, providing critical insights into galaxy formation and evolution, as well as the fundamental nature of dark matter. My talk will feature two major ongoing spectroscopic programs targeting the Milky Way’s streams. The Southern Stellar Stream Spectroscopic Survey (S5) is the first systematic effort to map known streams in the Southern Hemisphere, utilizing the AAOmega spectrograph on the Anglo-Australian Telescope. Complementing this, the Milky Way Survey of the Dark Energy Spectroscopic Instrument (DESI) is a recently launched 5-year initiative targeting the Northern Hemisphere. Together, these surveys are delivering unprecedented 6D kinematic and chemical data on dozens of streams, transforming our understanding of the Milky Way’s chemo-dynamical evolution, including the tidal disruption of satellite galaxies and globular clusters. I will also discuss the broader implications of these findings for dark matter and near-field cosmology, concluding with a perspective on next-generation spectroscopic surveys. |
Revision as of 16:08, 21 February 2025
This page records the schedule, titles and abstracts of the JHU/STScI CAS Astrophysics Wine & Cheese Series in Spring 2025.
Wine and Cheese sessions with one speaker will have a 50 minute talk with 10 minutes for questions. Sessions with two speakers will have two 25 minute talks, each with 5 minutes for questions.
Back to W&C Schedule
03 February
Cameron Trapp (JHU)
Torques and Radial Flows of Gas in Simulated Milky-Way Mass Galaxies
Observations indicate that a continuous gas supply is needed to maintain observed star formation rates in large, disky galaxies. To fuel star formation, gas must reach the inner regions of such galaxies. Despite its crucial importance for galaxy evolution, how and where gas joins galaxies is poorly constrained observationally and rarely explored in fully cosmological simulations. I will discuss the results of our initial study investigating gas accretion and transport in the FIRE-2 cosmological zoom-in simulations for 4 Milky Way mass galaxies. We generally found that gas joins just interior to the disk edge before radially transporting through the disk at average speeds of 1-5 km/s. This corresponds to radial mass fluxes of a few solar masses per year, comparable to the galaxies’ star formation rates. I will also discuss more recent work focused on understanding the angular momentum transfer required for these gas flows, including torques arising from various gravitational and magnetohydrodynamical forces. Finally, I will give a quick introduction to the work I will be doing as a new postdoc here at JHU, working with the FOGGIE simulations.
10 February
George Wong (IAS)
Precision Black Hole Astrophysics in the Era of Event-Horizon-Scale Observation
Black holes are ubiquitous and essential to our understanding of the universe, shaping galaxy evolution, driving magnetized outflows, and providing a natural laboratory for theories of gravity, high-energy plasma physics, and relativistic accretion. Their extreme environments also offer insights into broader astrophysical processes, from planetary accretion disks to pulsar magnetospheres and beyond. Over the past decade, cutting-edge interferometric experiments like the Event Horizon Telescope (EHT) have produced exquisite, transformative horizon-scale observations. These data provide an unprecedented opportunity to probe relativistic plasma physics, test general relativity in the strong-field regime, and constrain the mechanisms of accretion and jet formation. I will discuss the state-of-the-art in supermassive black hole accretion modeling and highlight how these methods have been applied to EHT data, producing quantitative constraints on near-horizon physics. I will then describe recent advances in identifying robust observational signatures of black hole spin and spacetime geometry from semi-analytic arguments. The next generation of black hole experiments promises to revolutionize our understanding of accretion and jet physics through a combination of space-based interferometry, expanded VLBI arrays, and high-energy multi-wavelength observatories. I will conclude with a discussion of how we will bridge the gap between modeling and observations, paving the way for precision black hole astrophysics in the coming decade.
17 February
Viraj Pandya (Columbia)
Decoding the Complexity of Galaxy Formation with Physics-Informed, AI-Accelerated Dynamical Models
Galaxies are complex dynamical systems evolving against the backdrop of cosmology. One of the grand challenges of modern astrophysics is to build a fully predictive theory for galaxy formation so that we can reliably use them to understand the fundamental physics of dark matter, dark energy and inflation. However, the relevant physical processes are poorly understood and non-linearly coupled over an enormous range of spatiotemporal scales. This, combined with our inability to observe the time evolution of individual systems, necessitates phenomenological approaches. I will describe my interdisciplinary efforts to use population studies to decode the ordered complexity of galaxies. First, I will show that my JWST discovery of a significant excess of linear, elongated galaxies at high-redshift has deep implications for the origin of the Hubble Sequence. Galaxies may not start out as axisymmetric disks as commonly assumed, but rather as prolate (cigar-shaped) or triaxial (surfboard-shaped) ellipsoids. This preferential elongation is naturally expected from the tidal field of the filamentary cosmic web, and it motivates an exciting search for intrinsic alignments among this dominant population of "early blue ellipticals" with JWST, Roman and Euclid. Although this puzzle may seem niche, it bridges together many different subfields (including astrostatistics, stellar dynamics, Galactic archaeology and dark matter phenomenology) and unlocks fresh science cases for HWO and GMT. Second, I will introduce a novel automatically differentiable, GPU-capable framework to help definitively answer ~50-year-old questions about how galaxies self-regulate and why they follow remarkably tight scaling relations. In my new model, supernovae and black holes over-pressurize galactic atmospheres, which naturally explains why galaxies are so inefficient at forming stars. But robustly testing this paradigm requires physics-informed, data-driven, AI-accelerated techniques for parameter inference and causal discovery. I will highlight some promising early applications of gradient descent optimization, Hamiltonian Monte Carlo and population-level implicit likelihood inference. These reveal that observations of the circumgalactic medium may hold the key to breaking numerous modeling degeneracies. I will conclude by outlining how my future research will enable transformative cosmology-style precision astrophysics for galaxy formation, provide a much-needed interpretable emulator for expensive hydrodynamical simulations, and rigorously connect observations with theory to maximize the impact of multiple JHU-led missions (e.g., SDSS-V, Rubin, PFS and NASA's next Great Observatories).
24 February
Ting Li (Toronto)
From Stellar Streams to Near Field Cosmology: Insights from Large-Scale Spectroscopic Surveys Stellar streams serve as exceptional tracers in near-field cosmology, providing critical insights into galaxy formation and evolution, as well as the fundamental nature of dark matter. My talk will feature two major ongoing spectroscopic programs targeting the Milky Way’s streams. The Southern Stellar Stream Spectroscopic Survey (S5) is the first systematic effort to map known streams in the Southern Hemisphere, utilizing the AAOmega spectrograph on the Anglo-Australian Telescope. Complementing this, the Milky Way Survey of the Dark Energy Spectroscopic Instrument (DESI) is a recently launched 5-year initiative targeting the Northern Hemisphere. Together, these surveys are delivering unprecedented 6D kinematic and chemical data on dozens of streams, transforming our understanding of the Milky Way’s chemo-dynamical evolution, including the tidal disruption of satellite galaxies and globular clusters. I will also discuss the broader implications of these findings for dark matter and near-field cosmology, concluding with a perspective on next-generation spectroscopic surveys.