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TAP Colloquia on YouTube

TAP - University of Arizona (TAP at UA)

TAP - University of Arizona (TAP at UA)
YouTube Video VVVnalEtOEJOZVZrWklHUDlnMndTQkRRLlhVOEJVYWhUUlM4 University of Arizona, Theoretical Astrophysics Program (TAP) Colloquia Series TITLE: Naturally Reproducing JWST Observations with New Numerically-Calibrated Models of Star Formation ABSTRACT: JWST observations have sparked a lively debate following the detection of surprisingly bright sources in the early universe. Reproducing these observations requires theoretical models to predict star formation rates that are unexpectedly efficient. This can be achieved through various competing modifications, such as invoking alternative dark matter scenarios, implementing different feedback processes, or adopting a top-heavy initial mass function, to name a few. In this talk, I will demonstrate that new numerically-calibrated models of star formation, such as the Pressure-Regulated Feedback-Modulated (PRFM) model, naturally reproduce the abundance of galaxies detected by JWST at high redshifts. The PRFM model predicts shorter gas depletion times at high densities, which inherently leads to the high-efficiency star formation indicated by JWST observations. This underscores the importance of implementing more accurate 3 of 5 star formation models in cosmological galaxy formation simulations, which have traditionally relied on models tuned to local observations or constant-efficiency star formation scenarios. BIO: Dr. Sultan Hassan develops and tests theoretical models and statistical techniques to study the formation and evolution of the first population of galaxies and black holes during the universe's first billion years. His current interests include resolving the tension between JWST observations and theoretical predictions at high redshift by developing the next generation of cosmological simulations, informed by insights from small-scale Interstellar Medium (ISM) simulations. Additionally, he is actively working on addressing the out-of-distribution challenge in extracting the maximum amount of information from future large-scale surveys, such as those conducted by the Roman Space Telescope, using machine learning techniques. As a part of the KaggleX fellowship program, he is currently developing a chatbot using state-of-the-art large language models (LLMs) to enhance research productivity in cosmology—stay tuned!
University of Arizona, Theoretical Astrophysics Program (TAP) Colloquia Series TITLE: Naturally Reproducing JWST Observations with New Numerically-Calibrated Models of Star Formation ABSTRACT: JWST observations have sparked a lively debate following the detection of surprisingly bright sources in the early universe. Reproducing these observations requires theoretical models to predict star formation rates that are unexpectedly efficient. This can be achieved through various competing modifications, such as invoking alternative dark matter scenarios, implementing different feedback processes, or adopting a top-heavy initial mass function, to name a few. In this talk, I will demonstrate that new numerically-calibrated models of star formation, such as the Pressure-Regulated Feedback-Modulated (PRFM) model, naturally reproduce the abundance of galaxies detected by JWST at high redshifts. The PRFM model predicts shorter gas depletion times at high densities, which inherently leads to the high-efficiency star formation indicated by JWST observations. This underscores the importance of implementing more accurate 3 of 5 star formation models in cosmological galaxy formation simulations, which have traditionally relied on models tuned to local observations or constant-efficiency star formation scenarios. BIO: Dr. Sultan Hassan develops and tests theoretical models and statistical techniques to study the formation and evolution of the first population of galaxies and black holes during the universe's first billion years. His current interests include resolving the tension between JWST observations and theoretical predictions at high redshift by developing the next generation of cosmological simulations, informed by insights from small-scale Interstellar Medium (ISM) simulations. Additionally, he is actively working on addressing the out-of-distribution challenge in extracting the maximum amount of information from future large-scale surveys, such as those conducted by the Roman Space Telescope, using machine learning techniques. As a part of the KaggleX fellowship program, he is currently developing a chatbot using state-of-the-art large language models (LLMs) to enhance research productivity in cosmology—stay tuned!
University of Arizona, Theoretical Astrophysics Program (TAP) Gravity Initiative Lectureship Series TITLE: Inquiring Through Black Holes ABSTRACT: Black holes have become unique tools in the understanding of a plethora of phenomena in vastly different fronts. From understanding spectacularly energetic events in our universe, gaining insights on potential new particles to unraveling seemingly unrelated phenomena. As we move through the 2nd century of General Relativity, the combination of observations together with a deeper understanding of the theory will catalyze new breakthroughs touching, arguably, all areas of physics. In this talk, we will connect with several of these fronts, and discuss opportunities from-and-to black holes.
University of Arizona, Theoretical Astrophysics Program (TAP) Planet Formation Initiative Lectureship PhotoChem Workshop 2 TITLE: An Open-Source Chemical and Climate Model of Planetary Atmospheres LEARNING OBJECTIVES: A brief introduction to photochemistry and climate modeling Installing Photochem First photochemistry simulation: a model of Modern Earth 3 of 4 Modifying chemical reaction rates or other internal model data (e.g., photolysis or aerosol cross sections) Modifying the Photochem source code & contributing to the model First climate simulations: reproducing the Habitable Zone If we have time - a photochemistry and climate simulation of TRAPPIST-1e assuming it is an inhabited world. BIO: Nick Wogan is a 2nd year postdoctoral fellow at NASA Ames Research Center, where he uses atmospheric models to interpret observations of exoplanets with the James Webb Space Telescope. Nick received his PhD at the University of Washington (UW), where he used the same atmospheric models to study the early Earth atmospheres to aid understanding of the origin of life.
University of Arizona, Theoretical Astrophysics Program (TAP) Planet Formation Initiative Lectureship Series Workshop for Photochem: An Open-Source Chemical and Climate Model of Planetary Atmospheres LEARNING OBJECTIVES: A brief introduction to photochemistry and climate modeling Installing Photochem First photochemistry simulation: a model of Modern Earth Modifying chemical reaction rates or other internal model data (e.g., photolysis or aerosol cross sections) Modifying the Photochem source code & contributing to the model First climate simulations: reproducing the Habitable Zone If we have time - a photochemistry and climate simulation of TRAPPIST-1e assuming it is an inhabited world. BIO: Nick Wogan is a 2nd year postdoctoral fellow at NASA Ames Research Center, where he uses atmospheric models to interpret observations of exoplanets with the James Webb Space Telescope. Nick received his PhD at the University of Washington (UW), where he used the same atmospheric models to study the early Earth atmospheres to aid understanding of the origin of life.
University of Arizona, Theoretical Astrophysics Program (TAP) Planet Formation Initiative Lectureship TITLE: The Photochemistry and Climate of Foreign Atmospheres: Implications for the Origin of Life on Early Earth and Understanding Exoplanets Observed by the James Webb Space Telescope ABSTRACT: The nature of the earliest Earth atmosphere determines the environmental conditions under which life began. However, our understanding of the early Earth is shrouded in deep time; few clues to its atmospheric composition and climate have been preserved in the geologic record. In the same vein, our knowledge of distant exoplanet atmospheres depends on limited and noisy telescopic observations that are often challenging to decipher. In this presentation, we supplement these sparse data with atmospheric photochemical and climate models to constrain the atmospheres of early Earth and exoplanets and consequently improve our understanding of life’s beginnings, and to search for distant habitable worlds. First, we use models to estimate the atmospheric production of key prebiotic molecules, like HCN, in the wake of massive asteroid impacts on Earth ~4 billion years ago. We find that impacts larger than ~600 km in diameter, in an optimistic case, result in ample prebiotic molecule production while smaller impacts make negligible amounts of origin-of-life molecules. Second, we use the same photochemical and climate models to interpret recent James Webb Space Telescope (JWST) observations of K2-18b, a habitable-zone sub-Neptune exoplanet. These observations revealed CH4 and CO2 in a H2-rich atmosphere, which some researchers have interpreted as a sign that the planet has a habitable liquid water ocean. Overall, our simulations find that a gas-rich mini-Neptune with no habitable surface is a better explanation of the data, because photochemistry prohibits simultaneous CO2 and CH4 in a habitable and lifeless scenario. Finally, we showcase a climate modeling framework that enables the 3 of 4 search for an atmosphere on a rocky exoplanet orbiting an M star with near-term JWST thermal emission observations. BIO: Nick Wogan is a 2nd year postdoctoral fellow at NASA Ames Research Center, where he uses atmospheric models to interpret observations of exoplanets with the James Webb Space Telescope. Nick received his PhD at the University of Washington (UW), where he used the same atmospheric models to study the early Earth atmospheres to aid understanding of the origin of life.
University of Arizona, Theoretical Astrophysics Program (TAP) Colloquia Series TITLE: The Universe We Cannot See ABSTRACT: Galaxies exist because invisible dark matter outweighs normal matter by a factor of six in our universe; cosmological expansion accelerates today because dark energy dominates spacetime on cosmic scales. Neither of these phenomena is explained by known particles or forces – their existence points to tremendous gaps in our understanding of nature on the most fundamental level. Over the past two decades, measurements of the cosmic microwave background radiation and other high-precision observations have enabled allocation of our universe into dark matter, dark energy, radiation, and baryon components, giving rise to the backbone model of cosmology. This talk will discuss the quest to understand the microphysics of the dominant (but invisible) components of our universe. I will focus on the mass and interactions of dark matter and neutrino particles as notable examples of science targets for cosmological surveys, to illustrate how observational data that spans billions of years of cosmic history and decades in physical distance scales can pave a new path toward discoveries in fundamental physics. BIO: Professor Gluscevic (she/her) studies fundamental physics using cosmology. She got her undergraduate degree in astrophysics in Belgrade (Serbia) and PhD at Caltech (Pasadena, California). She was a postdoctoral scholar at the Institute for Advanced Study in Princeton, before joining University of Southern California (USC) Physics and Astronomy faculty as a Gabilan Assistant Professor in 2019. Professor Gluscevic has co-led dark matter science teams for the Simons Observatory and CMB-S4 collaborations, and has served on the executive committee for the NASA Physics of the Cosmos. Her work was recognized through the Cottrell Scholars Award, NSF CAREER award, and USC Raubenheimer Outstanding Junior Faculty Award. In addition to research, she works with the USC Center for Excellence in Teaching to create active and inclusive learning experiences in physics classrooms at USC and chairs the Climate committee at USC Physics and Astronomy.
University of Arizona, Theoretical Astrophysics Program (TAP) Computation & Data Lectureship Series TITLE: The Next Generation of Horizon-Scale Black Hole Accretion Science ABSTRACT: The last decade has seen a rapid increase in opportunities for event-horizon-scale black hole science, with interferometric observatories producing exquisite high resolution, high cadence images and astrometry. These new data provide an unparalleled opportunity to directly investigate jet-launching, trans-relativistic plasma, and the details of how fluids and light behave in the vicinity of a black hole. In this talk, I will describe the state-of-the-art in modeling supermassive black hole accretion systems and discuss how uncertainties in the thermodynamics and plasma kinetics can influence the results. I will then describe how analytic results can be combined with numerical simulations to identify novel observables that directly probe spacetime geometry in spite of the messy gastrophysics. I will conclude with my take on the future of the theory and observation of horizon-scale black hole science. BIO: George Wong is a theoretical and computational astrophysicist primarily studying supermassive black hole environments in the context of accretion, radiative transfer, photon rings, and jet launching. He develops general relativistic magnetohydrodynamic and polarized radiative transfer codes, which are used to simulate the magnetized accretion disk and jet and predict spectra and polarimetric images of those sources. He is a member of the Event Horizon Telescope collaboration, where he co-leads the Theory Working Group, and is active in the design of next generation ground-and space-based near-horizon radio interferometry experiments. George is the Frank and Peggy Taplin Member at the Institute for Advanced Study and holds a joint position as an Associate Research Scholar in the Gravity Initiative at Princeton University. He received his Ph.D. in 2021 from the University of Illinois at Urbana-Champaign.
University of Arizona, Theoretical Astrophysics Program (TAP) Gravity Initiative Lectureship TITLE: Gravitational Waves and Searching for the Unexpected ABSTRACT: The detection of gravitational waves has revealed an invisible side of the Universe. Observations of gravitational waves from black holes and neutron stars allow us to test our understanding of dynamical spacetime, study matter at extreme densities, and measure the expansion history of our Universe in new ways. Gravitational wave data also provides opportunities for unexpected discoveries.  In this talk I will give an overview of gravitational wave detections made by the LIGO, Virgo and KAGRA collaborations. I will then discuss two avenues for future discovery with gravitational waves: direct searches for exotic stars, and black hole spectroscopy using ringdown. BIO: Aaron Zimmerman is an Assistant Professor at the University of Texas at Austin. He is a member of the LIGO Scientific Collaboration, and his research focus is gravitational wave modeling and data analysis. He did his PhD at Caltech with Yanbei Chen, where he studied the ringdown of black holes and methods to visualize curved spacetimes. After graduating in 2013 he moved to the Canadian Institute for Theoretical Astrophysics (CITA) in Toronto as a CITA Fellow and then Senior Research Associate. He began at UT Austin in 2018.
University of Arizona, Theoretical Astrophysics Program (TAP) Colloquia Series TITLE: More Pixels, More Stars, More Physics: Probing Late-Stage Massive Star Evolution with Cutting-Edge Datasets ABSTRACT: As individual objects, evolved massive stars are laboratories in which to probe the unconstrained physics of stellar evolution. En masse, they have an outsized impact on their host galaxies through their high luminosities, strong winds, and in particular, their 2 of 3 terminal supernova explosions. Despite this importance, evolved massive stars -- in which our uncertainties in convection, winds, interactions with binary companions, and more are most amplified -- are also the poorest studied due to limitations in sample sizes. In this talk, I will present my recent work that utilizes machine learning and Gaia DR3 spectroscopy to compile a high-resolution portrait of the HR diagram of cool supergiants in the Large Magellanic Cloud. Drawing connections with my previous work on the variability of evolved massive stars, I will show that these data point the way to a more complete understanding of the physics of massive star evolution, and further bridge the gap between massive stars and the core collapse events they produce. BIO: Trevor is a Carnegie Fellow at the Carnegie Observatories where he leverages cutting-edge datasets to study the evolution of 3 of 3 massive stars from ZAMS to core collapse. His work touches on time domain methods, astrostatistics, population synthesis, spectroscopy from R=50-50000, machine learning, and more. He is a proud alumnus of Wesleyan University (2015) and the University of Washington (2021). Outside of work, he is a baker, a musician, a coffee snob, and the co-owner of the world's grumpiest dachshund.
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