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
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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!